diff --git a/.github/workflows/fetch_all_tutorials.yaml b/.github/workflows/fetch_all_tutorials.yaml new file mode 100644 index 00000000..b0765b32 --- /dev/null +++ b/.github/workflows/fetch_all_tutorials.yaml @@ -0,0 +1,44 @@ +name: Fetch all tools + +on: + workflow_dispatch: + schedule: + #Every Sunday at 8:00 am + - cron: "0 8 * * 0" + +# Allow only one concurrent deployment, skipping runs queued between the run in-progress and latest queued. +# However, do NOT cancel in-progress runs as we want to allow these production deployments to complete. +concurrency: + group: "tutorials" + cancel-in-progress: false + +permissions: + contents: write + +jobs: + fetch-all-tutorials: + runs-on: ubuntu-20.04 + environment: fetch-tutorials + name: Fetch all tutorials + steps: + - uses: actions/checkout@v4 + - uses: actions/setup-python@v5 + with: + python-version: ${{ matrix.python-version }} + - name: Install requirement + run: python -m pip install -r requirements.txt + - name: Run script #needs PAT to access other repos + run: | + bash ./bin/extract_all_tutorials.sh + env: + PLAUSIBLE_API_KEY: ${{ secrets.PLAUSIBLE_API_TOKEN }} + - name: Commit all tools + # add or commit any changes in results if there was a change, merge with main and push as bot + run: | + git config user.name github-actions + git config user.email github-actions@github.com + git pull --no-rebase -s recursive -X ours + git add results + git status + git diff --quiet && git diff --staged --quiet || (git commit -m "fetch all tutorials bot - step fetch") + git push diff --git a/.github/workflows/filter_communities.yaml b/.github/workflows/filter_communities.yaml index e7cc5000..b73d1976 100644 --- a/.github/workflows/filter_communities.yaml +++ b/.github/workflows/filter_communities.yaml @@ -25,6 +25,52 @@ permissions: contents: write jobs: + filter-all-tutorials: + runs-on: ubuntu-20.04 + steps: + - uses: actions/checkout@v3 + - uses: actions/setup-python@v5 + with: + python-version: '3.8' + - name: Install requirement + run: python -m pip install -r requirements.txt + - name: Run script + run: | + bash ./bin/get_community_tutorials.sh + - name: Commit results + # commit the new filtered data, only if stuff was changed + run: | + git config user.name github-actions + git config user.email github-actions@github.com + git pull --no-rebase -s recursive -X ours + git add results + git status + git diff --quiet && git diff --staged --quiet || (git commit -m "fetch all tutorials / tools bot - step tutorial filter") + git push + + update-tools-to-keep-exclude: + runs-on: ubuntu-20.04 + steps: + - uses: actions/checkout@v3 + - uses: actions/setup-python@v5 + with: + python-version: '3.8' + - name: Install requirement + run: python -m pip install -r requirements.txt + - name: Run script + run: | + bash ./bin/update_tools_to_keep_exclude.sh + - name: Commit results + # commit the new filtered data, only if stuff was changed + run: | + git config user.name github-actions + git config user.email github-actions@github.com + git pull --no-rebase -s recursive -X ours + git add results + git status + git diff --quiet && git diff --staged --quiet || (git commit -m "fetch all tutorials / tools bot - step exluded/kept tool list update") + git push + filter-all-tools: runs-on: ubuntu-20.04 steps: @@ -45,5 +91,8 @@ jobs: git pull --no-rebase -s recursive -X ours git add results git status - git diff --quiet && git diff --staged --quiet || (git commit -m "fetch all tools bot - step filter") + git diff --quiet && git diff --staged --quiet || (git commit -m "fetch all tools bot - step tool filter") git push + + + \ No newline at end of file diff --git a/.github/workflows/run_tests.yaml b/.github/workflows/run_tests.yaml index 802a3b58..87dd1c2e 100644 --- a/.github/workflows/run_tests.yaml +++ b/.github/workflows/run_tests.yaml @@ -29,11 +29,17 @@ jobs: python-version: ${{ matrix.python-version }} - name: Install requirement run: python -m pip install -r requirements.txt - - name: Run script + - name: Test tool extraction # run: bash bin/extract_all_tools.sh run: | - export GITHUB_API_KEY=${{ secrets.GH_API_TOKEN }} bash ./bin/extract_all_tools_test.sh "${{ matrix.subset }}" + env: + GITHUB_API_KEY: ${{ secrets.GH_API_TOKEN }} + - name: Test tutorial extraction and filtering + run: | + bash ./bin/extract_filter_tutorial_test.sh + env: + PLAUSIBLE_API_KEY: ${{ secrets.PLAUSIBLE_API_TOKEN }} - name: Commit all tools # add or commit any changes in results if there was a change, merge with main and push as bot run: | diff --git a/.gitignore b/.gitignore new file mode 100644 index 00000000..f749f52b --- /dev/null +++ b/.gitignore @@ -0,0 +1,2 @@ +.DS_Store +__pycache__ \ No newline at end of file diff --git a/README.md b/README.md index 61d17126..a84187cf 100644 --- a/README.md +++ b/README.md @@ -62,7 +62,9 @@ The tool performs the following steps: $ python3 -m pip install -r requirements.txt ``` -## Extract all tools +## Tools + +### Extract all tools 1. Get an API key ([personal token](https://docs.github.com/en/authentication/keeping-your-account-and-data-secure/managing-your-personal-access-tokens)) for GitHub 2. Export the GitHub API key as an environment variable: @@ -96,7 +98,7 @@ The script will generate a TSV file with each tool found in the list of GitHub r 15. Conda id 16. Conda version -## Filter tools based on their categories in the ToolShed +### Filter tools based on their categories in the ToolShed 1. Run the extraction as explained before 2. (Optional) Create a text file with ToolShed categories for which tools need to be extracted: 1 ToolShed category per row ([example for microbial data analysis](data/microgalaxy/categories)) @@ -118,6 +120,37 @@ The script will generate a TSV file with each tool found in the list of GitHub r [--status ] ``` +## Training + +### Extract tutorials from GTN + +1. Get an API key ([personal token](https://docs.github.com/en/authentication/keeping-your-account-and-data-secure/managing-your-personal-access-tokens)) for Plausible +2. Export the Plausible API key as an environment variable: + + ``` + $ export PLAUSIBLE_API_KEY= + ``` + +3. Run the script + + ``` + $ python bin/extract_all_tutorials.sh + ``` + +### Filter tutorials based on tags + +1. Run the extraction as explained before +2. Create a file named `tutorial_tags` in your community `data` folder with the list of tutorial tags to keep +3. Run the following command + + ``` + $ python bin/extract_gtn_tutorials.py \ + filtertutorials \ + --all_tutorials "results/all_tutorials.json" \ + --filtered_tutorials "results//tutorials.tsv" \ + --tags "data/communities//tutorial_tags" + ``` + ## Development To make a test run of the tool to check its functionalities follow [Usage](#Usage) to set-up the environnement and the API key, then run diff --git a/bin/compare_tools.py b/bin/compare_tools.py new file mode 100644 index 00000000..7ab5e809 --- /dev/null +++ b/bin/compare_tools.py @@ -0,0 +1,80 @@ +#!/usr/bin/env python + +import argparse +from pathlib import Path +from typing import Set + +import pandas as pd +import shared_functions + + +def get_tutorials_tool_suites(tuto_fp: str, tool_fp: str) -> Set: + """ + Get tool suite ids for all tools in tutorials + """ + tutorials = pd.read_csv(tuto_fp, sep="\t", keep_default_na=False).to_dict("records") + all_tools = shared_functions.read_suite_per_tool_id(tool_fp) + print(all_tools) + tuto_tool_suites = set() + for tuto in tutorials: + tools = tuto["Tools"].split(", ") + for t in tools: + if t in all_tools: + tuto_tool_suites.add(all_tools[t]["Galaxy wrapper id"]) + else: + print(f"{t} not found in all tools") + return tuto_tool_suites + + +def write_tool_list(tools: Set, fp: str) -> None: + """ + Write tool list with 1 element per row in a file + """ + tool_list = list(tools) + tool_list.sort() + with Path(fp).open("w") as f: + f.write("\n".join(tool_list)) + + +def update_excl_keep_tool_lists(tuto_tool_suites: Set, excl_tool_fp: str, keep_tool_fp: str) -> None: + """ + Update the lists in to keep and exclude with tool suites in tutorials + """ + # exclude from the list of tools to exclude the tools that are in tutorials + excl_tools = set(shared_functions.read_file(excl_tool_fp)) - tuto_tool_suites + write_tool_list(excl_tools, excl_tool_fp) + # add from the list of tools to keep the tools that are in tutorials + keep_tools = set(shared_functions.read_file(keep_tool_fp)) | tuto_tool_suites + write_tool_list(keep_tools, keep_tool_fp) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser( + description="Update community-curated list of tools to keep and exclude with tools in community-curated tutorials" + ) + parser.add_argument( + "--filtered_tutorials", + "-t", + required=True, + help="Filepath to TSV with filtered tutorials", + ) + parser.add_argument( + "--exclude", + "-e", + help="Path to a file with ids of tools to exclude (one per line)", + ) + parser.add_argument( + "--keep", + "-k", + help="Path to a file with ids of tools to keep (one per line)", + ) + parser.add_argument( + "--all_tools", + "-a", + required=True, + help="Filepath to TSV with all extracted tools, generated by extractools command", + ) + args = parser.parse_args() + + tuto_tools = get_tutorials_tool_suites(args.filtered_tutorials, args.all_tools) + update_excl_keep_tool_lists(tuto_tools, args.exclude, args.keep) diff --git a/bin/extract_all_tutorials.sh b/bin/extract_all_tutorials.sh new file mode 100644 index 00000000..74f6c272 --- /dev/null +++ b/bin/extract_all_tutorials.sh @@ -0,0 +1,7 @@ +#!/usr/bin/env bash + +python bin/extract_gtn_tutorials.py \ + extracttutorials \ + --all_tutorials "results/all_tutorials.json" \ + --tools "results/all_tools.json" \ + --api $PLAUSIBLE_API_KEY \ No newline at end of file diff --git a/bin/extract_filter_tutorials_test.sh b/bin/extract_filter_tutorials_test.sh new file mode 100644 index 00000000..b4d066c3 --- /dev/null +++ b/bin/extract_filter_tutorials_test.sh @@ -0,0 +1,32 @@ +#!/usr/bin/env bash + +mkdir -p 'results/' + +tsv_output="results/${1}_tools.tsv" +json_output="results/${1}_tools.json" + +python bin/extract_gtn_tutorials.py \ + extracttutorials \ + --all_tutorials "results/test_tutorials.json" \ + --tools "results/all_tools.json" \ + --api $PLAUSIBLE_API_KEY \ + --test + +if [[ ! -f "results/test_tutorials.json" ]] ; then + echo 'File "results/test_tutorials.json" is not there, aborting.' + exit +fi + +python bin/extract_gtn_tutorials.py \ + filtertutorials \ + --all_tutorials "results/test_tutorials.json" \ + --filtered_tutorials "results/microgalaxy/test_tutorials.tsv" \ + --tags "data/communities/microgalaxy/tutorial_tags" + +if [[ ! -f "results/microgalaxy/test_tutorials.tsv" ]] ; then + echo 'File "results/microgalaxy/test_tutorials.tsv" is not there, aborting.' + exit +fi + +rm "results/test_tutorials.json" +rm "results/microgalaxy/test_tutorials.tsv" diff --git a/bin/extract_galaxy_tools.py b/bin/extract_galaxy_tools.py index 4ca5a0d2..5c279fe7 100644 --- a/bin/extract_galaxy_tools.py +++ b/bin/extract_galaxy_tools.py @@ -17,6 +17,7 @@ import pandas as pd import requests +import shared_functions import yaml from github import Github from github.ContentFile import ContentFile @@ -91,22 +92,6 @@ def get_tool_stats_from_stats_file(tool_stats_df: pd.DataFrame, tool_ids: List[s return int(agg_count) -def read_file(filepath: Optional[str]) -> List[str]: - """ - Read an optional file with 1 element per line - - :param filepath: path to a file - """ - if filepath is None: - return [] - fp = Path(filepath) - if fp.is_file(): - with fp.open("r") as f: - return [x.rstrip() for x in f.readlines()] - else: - return [] - - def get_string_content(cf: ContentFile) -> str: """ Get string of the content from a ContentFile @@ -507,13 +492,6 @@ def check_tools_on_servers(tool_ids: List[str], galaxy_server_url: str) -> int: return counter -def format_list_column(col: pd.Series) -> pd.Series: - """ - Format a column that could be a list before exporting - """ - return col.apply(lambda x: ", ".join(str(i) for i in x)) - - def export_tools_to_json(tools: List[Dict], output_fp: str) -> None: """ Export tool metadata to TSV output file @@ -537,17 +515,19 @@ def export_tools_to_tsv( """ df = pd.DataFrame(tools).sort_values("Galaxy wrapper id") if format_list_col: - df["ToolShed categories"] = format_list_column(df["ToolShed categories"]) - df["EDAM operation"] = format_list_column(df["EDAM operation"]) - df["EDAM topic"] = format_list_column(df["EDAM topic"]) + df["ToolShed categories"] = shared_functions.format_list_column(df["ToolShed categories"]) + df["EDAM operation"] = shared_functions.format_list_column(df["EDAM operation"]) + df["EDAM topic"] = shared_functions.format_list_column(df["EDAM topic"]) - df["EDAM operation (no superclasses)"] = format_list_column(df["EDAM operation (no superclasses)"]) - df["EDAM topic (no superclasses)"] = format_list_column(df["EDAM topic (no superclasses)"]) + df["EDAM operation (no superclasses)"] = shared_functions.format_list_column( + df["EDAM operation (no superclasses)"] + ) + df["EDAM topic (no superclasses)"] = shared_functions.format_list_column(df["EDAM topic (no superclasses)"]) - df["bio.tool ids"] = format_list_column(df["bio.tool ids"]) + df["bio.tool ids"] = shared_functions.format_list_column(df["bio.tool ids"]) # the Galaxy tools need to be formatted for the add_instances_to_table to work - df["Galaxy tool ids"] = format_list_column(df["Galaxy tool ids"]) + df["Galaxy tool ids"] = shared_functions.format_list_column(df["Galaxy tool ids"]) # if add_usage_stats: # df = add_usage_stats_for_all_server(df) @@ -764,7 +744,7 @@ def reduce_ontology_terms(terms: List, ontology: Any) -> List: with Path(args.tools).open() as f: tools = json.load(f) # get categories and tools to exclude - categories = read_file(args.categories) + categories = shared_functions.read_file(args.categories) try: status = pd.read_csv(args.status, sep="\t", index_col=0, header=None).to_dict("index") except Exception as ex: diff --git a/bin/extract_gtn_tutorials.py b/bin/extract_gtn_tutorials.py new file mode 100644 index 00000000..39b02154 --- /dev/null +++ b/bin/extract_gtn_tutorials.py @@ -0,0 +1,344 @@ +#!/usr/bin/env python + +import argparse +from datetime import ( + date, + datetime, +) +from typing import ( + Dict, + List, +) + +import pandas as pd +import requests +import shared_functions +import yt_dlp +from owlready2 import get_ontology + + +def get_request_json(url: str, headers: dict) -> dict: + """ + Return JSON output using request + + :param url: galaxy tool id + """ + r = requests.get(url, auth=None, headers=headers) + r.raise_for_status() + return r.json() + + +def format_date(date: str) -> str: + return datetime.fromisoformat(date).strftime("%Y-%m-%d") + + +def add_supported_servers(tuto: dict) -> None: + """ + Split supported_servers into 2 lists there + """ + if "supported_servers" in tuto: + if "exact" in tuto["supported_servers"]: + tuto["exact_supported_servers"] = [server["name"] for server in tuto["supported_servers"]["exact"]] + if "inexact" in tuto["supported_servers"]: + tuto["inexact_supported_servers"] = [server["name"] for server in tuto["supported_servers"]["inexact"]] + + +def get_short_tool_ids(tuto: dict) -> None: + """ + Get tool ids without toolshed URL + """ + tuto["short_tools"] = set() + if "tools" in tuto: + for tool in tuto["tools"]: + if "toolshed" in tool: + tuto["short_tools"].add(tool.split("/")[-2]) + else: + tuto["short_tools"].add(tool) + tuto["short_tools"] = list(tuto["short_tools"]) + + +def get_edam_topics(tuto: dict, edam_ontology: dict) -> None: + """ + Get EDAM topics instead of EDAM ids + """ + tuto["edam_topic"] = [] + if "edam_ontology" in tuto: + for term in tuto["edam_ontology"]: + if "topic" in term and edam_ontology[term]: + tuto["edam_topic"] += edam_ontology[term].label + + +def get_edam_operations(tuto: dict, tools: dict) -> None: + """ + Get EDAM operations from the tools + """ + tuto["edam_operation"] = [] + if "short_tools" in tuto: + edam_operation = set() + for t in tuto["short_tools"]: + if t in tools: + edam_operation.update(set(tools[t]["EDAM operation"])) + else: + print(f"{t} not found in all tools") + tuto["edam_operation"] = list(edam_operation) + + +def get_feedback(tuto: dict, feedback: dict) -> None: + """ + Get feedback for tutorial + """ + tuto["feedback_number"] = 0 + tuto["feedback_mean_note"] = None + title = tuto["title"] + if title in feedback: + tuto["feedback_number"] = feedback[title]["number"] + tuto["feedback_mean_note"] = feedback[title]["mean note"] + + +def get_visit_results(url: str, tuto: dict, plausible_api: str) -> None: + """ + Extract visit results from Plausible URL + """ + headers = {"Authorization": f"Bearer {plausible_api}"} + results = get_request_json(url, headers) + if "results" in results: + for metric in ["visitors", "pageviews", "visit_duration"]: + tuto[metric] += results["results"][metric]["value"] + + +def get_visits(tuto: dict, plausible_api: str) -> None: + """ + Extract tutorial visitors and pageviews from Plausible + """ + tuto["visitors"] = 0 + tuto["pageviews"] = 0 + tuto["visit_duration"] = 0 + url_prefix = f"https://plausible.galaxyproject.eu/api/v1/stats/aggregate?site_id=training.galaxyproject.org&period=custom&date={tuto['pub_date']},{date.today()}&metrics=visitors,pageviews,visit_duration&filters=event:page%3D%3D%2Ftraining-material%2Ftopics%2F{tuto['topic_name']}%2Ftutorials%2F{tuto['tutorial_name']}" + if tuto["hands_on"]: + url = f"{url_prefix}%2Ftutorial.html" + get_visit_results(url, tuto, plausible_api) + if tuto["slides"]: + url = f"{url_prefix}%slides.html" + get_visit_results(url, tuto, plausible_api) + + +def get_youtube_stats(tuto: dict) -> None: + """ + Get YouTube stats + """ + tuto["video_versions"] = 0 + tuto["video_view"] = 0 + ydl_opts = {"ignoreerrors": True, "quiet": True} + if "video_library" in tuto and tuto["video_library"]["tutorial"]: + tuto["video_versions"] = len(tuto["video_library"]["tutorial"]["versions"]) + for v in tuto["video_library"]["tutorial"]["versions"]: + url = f"https://www.youtube.com/watch?v={v['link']}" + with yt_dlp.YoutubeDL(ydl_opts) as ydl: + info = ydl.extract_info(url, download=False) + info = ydl.sanitize_info(info) + if info: + tuto["video_view"] += info["view_count"] + + +def format_tutorial(tuto: dict, edam_ontology: dict, tools: dict, feedback: dict, plausible_api: str) -> Dict: + tuto["url"] = f'https://training.galaxyproject.org/{tuto["url"]}' + tuto["mod_date"] = format_date(tuto["mod_date"]) + tuto["pub_date"] = format_date(tuto["pub_date"]) + add_supported_servers(tuto) + get_short_tool_ids(tuto) + get_edam_topics(tuto, edam_ontology) + get_edam_operations(tuto, tools) + get_visits(tuto, plausible_api) + get_feedback(tuto, feedback) + get_youtube_stats(tuto) + return tuto + + +def get_feedback_per_tutorials() -> Dict: + """ + Get feedback from GTN API and group per tutorial + """ + feedback = get_request_json("https://training.galaxyproject.org/training-material/api/feedback2.json", {}) + feedback_per_tuto = {} # type: dict + for tutorials in feedback.values(): + for tuto, feedback in tutorials.items(): + for f in feedback: + feedback_per_tuto.setdefault(tuto, {"number": 0, "mean rating": 0}) + feedback_per_tuto[tuto]["number"] += 1 + feedback_per_tuto[tuto]["mean rating"] += int(f["rating"]) + for tuto in feedback_per_tuto: + feedback_per_tuto[tuto]["mean rating"] /= feedback_per_tuto[tuto]["number"] + return feedback_per_tuto + + +def get_tutorials( + tool_fp: str, + plausible_api: str, + run_test: bool, +) -> List[Dict]: + """ + Extract training material from the GTN API, format them, extract EDAM operations from tools, feedback stats, view stats, etc + """ + tools = shared_functions.read_suite_per_tool_id(tool_fp) + feedback = get_feedback_per_tutorials() + edam_ontology = get_ontology("https://edamontology.org/EDAM_unstable.owl").load() + topics = get_request_json("https://training.galaxyproject.org/training-material/api/topics.json", {}) + if run_test: + topics = {"microbiome": topics["microbiome"]} + tutos = [] + for topic in topics: + topic_information = get_request_json( + f"https://training.galaxyproject.org/training-material/api/topics/{topic}.json", {} + ) + for tuto in topic_information["materials"]: + if tuto is None: + continue + format_tutorial(tuto, edam_ontology, tools, feedback, plausible_api) + tutos.append(tuto) + return tutos + + +def filter_tutorials(tutorials: dict, tags: List) -> List: + """ + Filter training based on a list of tags + """ + filtered_tutorials = [] + for tuto in tutorials: + to_keep = False + if "tags" in tuto and tuto["tags"]: + for t in tuto["tags"]: + if t in tags: + to_keep = True + if to_keep: + filtered_tutorials.append(tuto) + return filtered_tutorials + + +def export_tutorials_to_tsv(tutorials: list, output_fp: str) -> None: + """ + Export tutorials to a TSV file + """ + df = pd.DataFrame(tutorials).assign( + Workflows=lambda df: df.workflows.notna(), + exact_supported_servers=lambda df: df.exact_supported_servers.fillna("").apply(list), + inexact_supported_servers=lambda df: df.inexact_supported_servers.fillna("").apply(list), + visit_duration=lambda df: df.visit_duration / 60, + ) + + for col in ["exact_supported_servers", "inexact_supported_servers", "short_tools", "edam_operation", "edam_topic"]: + df[col] = shared_functions.format_list_column(df[col]) + + df = ( + df.rename( + columns={ + "title": "Title", + "hands_on": "Tutorial", + "url": "Link", + "slides": "Slides", + "mod_date": "Last modification", + "pub_date": "Creation", + "version": "Version", + "short_tools": "Tools", + "exact_supported_servers": "Servers with precise tool versions", + "inexact_supported_servers": "Servers with tool but different versions", + "topic_name_human": "Topic", + "video": "Video", + "edam_topic": "EDAM topic", + "edam_operation": "EDAM operation", + "feedback_number": "Feedback number", + "feedback_mean_note": "Feedback mean note", + "visitors": "Visitors", + "pageviews": "Page views", + "visit_duration": "Visit duration", + "video_versions": "Video versions", + "video_view": "Video views", + } + ) + .fillna("") + .reindex( + columns=[ + "Topic", + "Title", + "Link", + "EDAM topic", + "EDAM operation", + "Creation", + "Last modification", + "Version", + "Tutorial", + "Slides", + "Video", + "Workflows", + "Tools", + "Servers with precise tool versions", + "Servers with tool but different versions", + "Feedback number", + "Feedback mean note", + "Visitors", + "Page views", + "Visit duration", + "Video views", + ] + ) + ) + df.to_csv(output_fp, sep="\t", index=False) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser( + description="Extract Galaxy Training Materials from GTN API together with statistics" + ) + subparser = parser.add_subparsers(dest="command") + # Extract tutorials + extracttutorials = subparser.add_parser("extracttutorials", help="Extract all training materials") + extracttutorials.add_argument( + "--all_tutorials", "-o", required=True, help="Filepath to JSON with all extracted training materials" + ) + extracttutorials.add_argument( + "--tools", + "-t", + required=True, + help="Filepath to JSON with all extracted tools, generated by extractools command", + ) + extracttutorials.add_argument("--api", "-a", required=True, help="Plausible access token") + extracttutorials.add_argument( + "--test", + action="store_true", + default=False, + required=False, + help="Run a small test case only on one topic", + ) + + # Filter tutorials + filtertutorials = subparser.add_parser("filtertutorials", help="Filter training materials based on their tags") + filtertutorials.add_argument( + "--all_tutorials", + "-t", + required=True, + help="Filepath to JSON with all extracted tutorials, generated by extracttutorials command", + ) + filtertutorials.add_argument( + "--filtered_tutorials", + "-f", + required=True, + help="Filepath to TSV with filtered tutorials", + ) + filtertutorials.add_argument( + "--tags", + "-c", + help="Path to a file with tags to keep in the extraction (one per line)", + ) + + args = parser.parse_args() + + if args.command == "extracttutorials": + tutorials = get_tutorials(args.tools, args.api, args.test) + shared_functions.export_to_json(tutorials, args.all_tutorials) + + elif args.command == "filtertutorials": + all_tutorials = shared_functions.load_json(args.all_tutorials) + # get categories and training to exclude + tags = shared_functions.read_file(args.tags) + # filter training lists + filtered_tutorials = filter_tutorials(all_tutorials, tags) + export_tutorials_to_tsv(filtered_tutorials, args.filtered_tutorials) diff --git a/bin/get_community_tutorials.sh b/bin/get_community_tutorials.sh new file mode 100644 index 00000000..c4f8d3e4 --- /dev/null +++ b/bin/get_community_tutorials.sh @@ -0,0 +1,18 @@ +#!/usr/bin/env bash + +for com_data_fp in data/communities/* ; do + if [[ -d "$com_data_fp" && ! -L "$com_data_fp" ]]; then + community=`basename "$com_data_fp"` + + echo "$community"; + + if [[ -f "data/communities/$community/tutorial_tags" && -f "results/$community/tutorials.tsv" ]]; then + + python bin/extract_gtn_tutorials.py \ + filtertutorials \ + --all_tutorials "results/all_tutorials.json" \ + --filtered_tutorials "results/$community/tutorials.tsv" \ + --tags "data/communities/$community/tutorial_tags" + fi; + fi; +done diff --git a/bin/shared_functions.py b/bin/shared_functions.py new file mode 100644 index 00000000..6cc3a4b0 --- /dev/null +++ b/bin/shared_functions.py @@ -0,0 +1,67 @@ +#!/usr/bin/env python + +import json +from pathlib import Path +from typing import ( + Dict, + List, + Optional, +) + +import pandas as pd + + +def format_list_column(col: pd.Series) -> pd.Series: + """ + Format a column that could be a list before exporting + """ + return col.apply(lambda x: ", ".join(str(i) for i in x)) + + +def read_file(filepath: Optional[str]) -> List[str]: + """ + Read an optional file with 1 element per line + + :param filepath: path to a file + """ + if filepath is None: + return [] + fp = Path(filepath) + if fp.is_file(): + with fp.open("r") as f: + return [x.rstrip() for x in f.readlines()] + else: + return [] + + +def export_to_json(data: List[Dict], output_fp: str) -> None: + """ + Export to a JSON file + """ + with Path(output_fp).open("w") as f: + json.dump(data, f, indent=4, sort_keys=True) + + +def load_json(input_df: str) -> Dict: + """ + Read a JSON file + """ + with Path(input_df).open("r") as t: + content = json.load(t) + return content + + +def read_suite_per_tool_id(tool_fp: str) -> Dict: + """ + Read the tool suite table and extract a dictionary per tool id + """ + tool_suites = load_json(tool_fp) + tools = {} + for suite in tool_suites: + for tool in suite["Galaxy tool ids"]: + tools[tool] = { + "Galaxy wrapper id": suite["Galaxy wrapper id"], + "Galaxy wrapper owner": suite["Galaxy wrapper id"], + "EDAM operation": suite["EDAM operation"], + } + return tools diff --git a/bin/update_tools_to_keep_exclude.sh b/bin/update_tools_to_keep_exclude.sh new file mode 100644 index 00000000..0b555533 --- /dev/null +++ b/bin/update_tools_to_keep_exclude.sh @@ -0,0 +1,18 @@ +#!/usr/bin/env bash + +for com_data_fp in data/communities/* ; do + if [[ -d "$com_data_fp" && ! -L "$com_data_fp" ]]; then + community=`basename "$com_data_fp"` + + echo "$community"; + + if [[ -f "data/communities/$community/tools_to_exclude" && -f "data/communities/$community/tools_to_keep" && -f "results/$community/tutorials.tsv" ]]; then + + python bin/compare_tools.py \ + --filtered_tutorials "results/$community/tutorials.tsv" \ + --exclude "data/communities/$community/tools_to_exclude" \ + --keep "data/communities/$community/tools_to_keep" \ + --all_tools "results/all_tools.tsv" + fi; + fi; +done \ No newline at end of file diff --git a/data/communities/microgalaxy/tutorial_tags b/data/communities/microgalaxy/tutorial_tags new file mode 100644 index 00000000..11876d93 --- /dev/null +++ b/data/communities/microgalaxy/tutorial_tags @@ -0,0 +1 @@ +microgalaxy \ No newline at end of file diff --git a/requirements.txt b/requirements.txt index 32129d5f..7ba08f83 100644 --- a/requirements.txt +++ b/requirements.txt @@ -6,4 +6,5 @@ Pillow matplotlib wordcloud owlready2 +yt-dlp pyopenssl>=24.0.0 diff --git a/results/all_tutorials.json b/results/all_tutorials.json new file mode 100644 index 00000000..08a0f348 --- /dev/null +++ b/results/all_tutorials.json @@ -0,0 +1,124594 @@ +[ + { + "admin_install": { + "install_repository_dependencies": true, + "install_resolver_dependencies": true, + "install_tool_dependencies": true, + "tools": [] + }, + "admin_install_yaml": "---\ninstall_tool_dependencies: true\ninstall_repository_dependencies: true\ninstall_resolver_dependencies: true\ntools: []\n", + "api": "https://training.galaxyproject.org/training-material/api/topics/admin/tutorials/uwsgi/tutorial.json", + "contributors": [ + { + "email": "nate@bx.psu.edu", + "id": "natefoo", + "joined": "2017-12", + "matrix": "natefoo:matrix.org", + "name": "Nate Coraor", + "orcid": "0000-0001-8083-2963", + "page": "https://training.galaxyproject.org/training-material/hall-of-fame/natefoo/", + "twitter": "natefoo", + "url": "https://training.galaxyproject.org/training-material/api/contributors/natefoo.json" + }, + { + "elixir_node": "au", + "email": "simon.gladman@unimelb.edu.au", + "id": "slugger70", + "in_memoriam": "Simon Gladman, system administrator of UseGalaxy.org.au, passed away on November 26, 2022\n\nHe was a fantastic teacher, tutorial author, system administrator, and warm and welcoming friend. He contributed heavily to the Galaxy Training Network and especially the Galaxy Administration community over the years, we will miss him dearly. The GTN and GAT would not be what they are today, without him.\n\n\u201cHave I told you about my watch?\u201d", + "joined": "2017-09", + "name": "Simon Gladman", + "orcid": "0000-0002-6100-4385", + "page": "https://training.galaxyproject.org/training-material/hall-of-fame/slugger70/", + "url": "https://training.galaxyproject.org/training-material/api/contributors/slugger70.json" + } + ], + "dir": "topics/admin/tutorials/uwsgi", + "edam_operation": [], + "edam_topic": [], + "feedback_mean_note": null, + "feedback_number": 0, + "hands_on": false, + "id": "admin/uwsgi", + "js_requirements": { + "mathjax": null, + "mermaid": false + }, + "layout": "tutorial_slides", + "license": "CC-BY-4.0", + "logo": "assets/images/gat.png", + "mod_date": "2022-03-01", + "pageviews": 5533184, + "pub_date": "2019-01-28", + "short_id": "S00028", + "short_tools": [], + "slides": true, + "slides_recordings": false, + "subtopic": "deprecated", + "supported_servers": [], + "symlink": null, + "title": "uWSGI", + "tools": [], + "topic_name": "admin", + "topic_name_human": "Galaxy Server administration", + "tours": false, + "translations": { + "slides": [], + "tutorial": [], + "video": false + }, + "tutorial_name": "uwsgi", + "type": "tutorial", + "url": "https://training.galaxyproject.org//topics/admin/tutorials/uwsgi/slides.html", + "urls": { + "hands_on": "https://training.galaxyproject.org/training-material/api/topics/admin/tutorials/uwsgi/slides.json", + "slides": "https://training.galaxyproject.org/training-material/api/topics/admin/tutorials/uwsgi/slides.json" + }, + "version": 17, + "video": false, + "video_versions": 0, + "video_view": 0, + "visit_duration": 152, + "visitors": 1984183 + }, + { + "abbreviations": { + "DVCS": "Distributed Version Control System" + }, + "admin_install": { + "install_repository_dependencies": true, + "install_resolver_dependencies": true, + "install_tool_dependencies": true, + "tools": [] + }, + "admin_install_yaml": "---\ninstall_tool_dependencies: true\ninstall_repository_dependencies: true\ninstall_resolver_dependencies: true\ntools: []\n", + "api": "https://training.galaxyproject.org/training-material/api/topics/admin/tutorials/ansible-galaxy/tutorial.json", + "contributions": { + "authorship": [ + "hexylena", + "natefoo", + "slugger70" + ], + "editing": [ + "shiltemann", + "nsoranzo", + "gmauro", + "mvdbeek", + "martenson", + "jmchilton", + "davebx", + "lecorguille", + "abretaud", + "lldelisle" + ], + "testing": [ + "mira-miracoli", + "edmontosaurus" + ] + }, + "contributors": [ + { + "affiliations": [ + "gallantries", + "by-covid", + "erasmusmc", + "elixir-europe", + "elixir-converge" + ], + "contact_for_training": false, + "elixir_node": "nl", + "former_affiliations": [ + "deNBI", + "avans-atgm", + "uni-freiburg" + ], + "id": "hexylena", + "joined": "2017-09", + "location": { + "country": "NL", + "lat": 51.91, + "lon": 4.46 + }, + "maintainer_contact": "gitter", + "matrix": "hexylena:matrix.org", + "name": "Helena Rasche", + "orcid": "0000-0001-9760-8992", + "page": "https://training.galaxyproject.org/training-material/hall-of-fame/hexylena/", + "url": "https://training.galaxyproject.org/training-material/api/contributors/hexylena.json" + }, + { + "email": "nate@bx.psu.edu", + "id": "natefoo", + "joined": "2017-12", + "matrix": "natefoo:matrix.org", + "name": "Nate Coraor", + "orcid": "0000-0001-8083-2963", + "page": "https://training.galaxyproject.org/training-material/hall-of-fame/natefoo/", + "twitter": "natefoo", + "url": "https://training.galaxyproject.org/training-material/api/contributors/natefoo.json" + }, + { + "elixir_node": "au", + "email": "simon.gladman@unimelb.edu.au", + "id": "slugger70", + "in_memoriam": "Simon Gladman, system administrator of UseGalaxy.org.au, passed away on November 26, 2022\n\nHe was a fantastic teacher, tutorial author, system administrator, and warm and welcoming friend. He contributed heavily to the Galaxy Training Network and especially the Galaxy Administration community over the years, we will miss him dearly. The GTN and GAT would not be what they are today, without him.\n\n\u201cHave I told you about my watch?\u201d", + "joined": "2017-09", + "name": "Simon Gladman", + "orcid": "0000-0002-6100-4385", + "page": "https://training.galaxyproject.org/training-material/hall-of-fame/slugger70/", + "url": "https://training.galaxyproject.org/training-material/api/contributors/slugger70.json" + }, + { + "affiliations": [ + "CINECA-Project", + "gallantries", + "erasmusmc", + "elixir-europe", + "uni-freiburg" + ], + "bio": "Researcher at Erasmus Medical Center", + "bluesky": "shiltemann.bsky.social", + "contact_for_training": true, + "elixir_node": "nl", + "email": "saskia.hiltemann@gmail.com", + "fediverse": "https://mstdn.science/@shiltemann", + "fediverse_flavor": "mastodon", + "id": "shiltemann", + "joined": "2017-09", + "linkedin": "shiltemann", + "location": { + "country": "NL", + "lat": 51.912, + "lon": 4.462 + }, + "maintainer_contact": "gitter", + "matrix": "shiltemann:matrix.org", + "name": "Saskia Hiltemann", + "orcid": "0000-0003-3803-468X", + "page": "https://training.galaxyproject.org/training-material/hall-of-fame/shiltemann/", + "url": "https://training.galaxyproject.org/training-material/api/contributors/shiltemann.json" + }, + { + "affiliations": [ + "earlham", + "elixir-europe" + ], + "elixir_node": "uk", + "fediverse": "https://mstdn.science/@nsoranzo", + "fediverse_flavor": "mastodon", + "id": "nsoranzo", + "joined": "2017-09", + "linkedin": "nicola-soranzo-7884125", + "matrix": "nsoranzo:matrix.org", + "name": "Nicola Soranzo", + "orcid": "0000-0003-3627-5340", + "page": "https://training.galaxyproject.org/training-material/hall-of-fame/nsoranzo/", + "twitter": "NicolaSoranzo", + "url": "https://training.galaxyproject.org/training-material/api/contributors/nsoranzo.json" + }, + { + "elixir_node": "de", + "former_affiliations": [ + "uni-freiburg", + "elixir-europe" + ], + "id": "gmauro", + "joined": "2019-03", + "name": "Gianmauro Cuccuru", + "orcid": "0000-0002-5335-545X", + "page": "https://training.galaxyproject.org/training-material/hall-of-fame/gmauro/", + "url": "https://training.galaxyproject.org/training-material/api/contributors/gmauro.json" + }, + { + "id": "mvdbeek", + "joined": "2018-06", + "name": "Marius van den Beek", + "page": "https://training.galaxyproject.org/training-material/hall-of-fame/mvdbeek/", + "url": "https://training.galaxyproject.org/training-material/api/contributors/mvdbeek.json" + }, + { + "affiliations": [ + "elixir-europe" + ], + "contact_for_training": true, + "elixir_node": "cz", + "email": "marten@bx.psu.edu", + "fediverse": "https://mastodon.world/@martenson", + "fediverse_flavor": "mastodon", + "id": "martenson", + "joined": "2017-09", + "location": { + "country": "USA", + "lat": 37.0, + "lon": -122.0 + }, + "matrix": "martenson:matrix.org", + "name": "Martin \u010cech", + "orcid": "0000-0002-9318-1781", + "page": "https://training.galaxyproject.org/training-material/hall-of-fame/martenson/", + "url": "https://training.galaxyproject.org/training-material/api/contributors/martenson.json" + }, + { + "email": "jmchilton@gmail.com", + "id": "jmchilton", + "joined": "2017-09", + "matrix": "jmchilton:matrix.org", + "name": "John Chilton", + "orcid": "0000-0002-6794-0756", + "page": "https://training.galaxyproject.org/training-material/hall-of-fame/jmchilton/", + "twitter": "jmchilton", + "url": "https://training.galaxyproject.org/training-material/api/contributors/jmchilton.json" + }, + { + "id": "davebx", + "joined": "2021-06", + "name": "Dave B.", + "page": "https://training.galaxyproject.org/training-material/hall-of-fame/davebx/", + "url": "https://training.galaxyproject.org/training-material/api/contributors/davebx.json" + }, + { + "affiliations": [ + "elixir-europe" + ], + "elixir_node": "fr", + "id": "lecorguille", + "joined": "2017-09", + "name": "Gildas Le Corguill\u00e9", + "page": "https://training.galaxyproject.org/training-material/hall-of-fame/lecorguille/", + "url": "https://training.galaxyproject.org/training-material/api/contributors/lecorguille.json" + }, + { + "affiliations": [ + "gallantries", + "eurosciencegateway", + "elixir-europe" + ], + "contact_for_training": true, + "elixir_node": "fr", + "email": "anthony.bretaudeau@inrae.fr", + "fediverse": "https://genomic.social/@abretaud", + "fediverse_flavor": "mastodon", + "id": "abretaud", + "joined": "2017-09", + "location": { + "country": "FR", + "lat": 48.11, + "lon": -1.64 + }, + "matrix": "abretaud:matrix.org", + "name": "Anthony Bretaudeau", + "orcid": "0000-0003-0914-2470", + "page": "https://training.galaxyproject.org/training-material/hall-of-fame/abretaud/", + "twitter": "abretau", + "url": "https://training.galaxyproject.org/training-material/api/contributors/abretaud.json" + }, + { + "affiliations": [ + "elixir-europe" + ], + "contact_for_training": true, + "elixir_node": "ch", + "email": "lucille.delisle@epfl.ch", + "id": "lldelisle", + "joined": "2019-08", + "location": { + "country": "CH", + "lat": 46.52, + "lon": 6.56 + }, + "name": "Lucille Delisle", + "orcid": "0000-0002-1964-4960", + "page": "https://training.galaxyproject.org/training-material/hall-of-fame/lldelisle/", + "url": "https://training.galaxyproject.org/training-material/api/contributors/lldelisle.json" + }, + { + "affiliations": [ + "uni-freiburg", + "elixir-europe", + "deNBI" + ], + "elixir_node": "de", + "email": "kuntzm@informatik.uni-freiburg.de", + "id": "mira-miracoli", + "joined": "2022-07", + "matrix": "mira-miracoli:matrix.org", + "name": "Mira Kuntz", + "orcid": "0000-0003-4302-5091", + "page": "https://training.galaxyproject.org/training-material/hall-of-fame/mira-miracoli/", + "url": "https://training.galaxyproject.org/training-material/api/contributors/mira-miracoli.json" + }, + { + "id": "edmontosaurus", + "joined": "2023-11", + "matrix": "edmontosaurus:matrix.org", + "name": "Edwin den Haas", + "page": "https://training.galaxyproject.org/training-material/hall-of-fame/edmontosaurus/", + "url": "https://training.galaxyproject.org/training-material/api/contributors/edmontosaurus.json" + } + ], + "dir": "topics/admin/tutorials/ansible-galaxy", + "edam_ontology": [ + "topic_3489", + "topic_0605", + "topic_3071" + ], + "edam_operation": [], + "edam_topic": [ + "Database management", + "Informatics", + "Data management" + ], + "feedback_mean_note": 4.117647058823529, + "feedback_number": 34, + "hands_on": true, + "id": "admin/ansible-galaxy", + "js_requirements": { + "mathjax": null, + "mermaid": false + }, + "key_points": [ + "Basic deployment with Ansible is surprisingly easy", + "Complexity can grow over time as your organisation does, no need to start with playbooks like UseGalaxy.org" + ], + "layout": "tutorial_hands_on", + "license": "CC-BY-4.0", + "mod_date": "2024-05-29", + "objectives": [ + "Have an understanding of how Galaxy's Ansible roles are structured and interact with one another", + "Be able to use an Ansible playbook to install different flavors of Galaxy for different purposes" + ], + "pageviews": 5576722, + "pub_date": "2019-01-28", + "questions": [ + "How does the Galaxy Ansible module work internally?", + "How can I install a Galaxy server with Ansible" + ], + "recordings": [ + { + "captioners": [ + "natefoo", + "hexylena" + ], + "date": "2022-03-01", + "galaxy_version": "21.05", + "length": "2H50M", + "speakers": [ + "natefoo", + "hexylena" + ], + "youtube_id": "LPK8rP_qUiA" + }, + { + "captioners": [ + "natefoo", + "hexylena" + ], + "date": "2021-06-28", + "galaxy_version": "21.05", + "length": "2H47M", + "speakers": [ + "natefoo" + ], + "youtube_id": "zT70luZqPOU" + }, + { + "captioners": [ + "shiltemann", + "hexylena" + ], + "date": "2021-02-15", + "galaxy_version": "21.01", + "length": "53M", + "speakers": [ + "hexylena" + ], + "youtube_id": "il83uApg7Hc" + } + ], + "requirements": [ + { + "topic_name": "admin", + "tutorials": [ + "ansible" + ], + "type": "internal" + }, + { + "title": "A VM with at least 2 vCPUs and 4 GB RAM, preferably running Ubuntu 20.04 - 22.04.", + "type": "none" + } + ], + "short_id": "T00001", + "short_tools": [], + "slides": true, + "slides_recordings": false, + "subtopic": "core", + "supported_servers": [], + "symlink": null, + "tags": [ + "ansible", + "deploying", + "git-gat" + ], + "time_estimation": "2h30m", + "title": "Galaxy Installation with Ansible", + "tools": [], + "topic_name": "admin", + "topic_name_human": "Galaxy Server administration", + "tours": false, + "translations": { + "slides": [], + "tutorial": [], + "video": true + }, + "tutorial_name": "ansible-galaxy", + "type": "tutorial", + "url": "https://training.galaxyproject.org//topics/admin/tutorials/ansible-galaxy/tutorial.html", + "urls": { + "hands_on": "https://training.galaxyproject.org/training-material/api/topics/admin/tutorials/ansible-galaxy/tutorial.json", + "slides": "https://training.galaxyproject.org/training-material/api/topics/admin/tutorials/ansible-galaxy/tutorial.json" + }, + "version": 377, + "video": true, + "video_versions": 0, + "video_view": 0, + "visit_duration": 254, + "visitors": 2006732 + }, + { + "admin_install": { + "install_repository_dependencies": true, + "install_resolver_dependencies": true, + "install_tool_dependencies": true, + "tools": [] + }, + "admin_install_yaml": "---\ninstall_tool_dependencies: true\ninstall_repository_dependencies: true\ninstall_resolver_dependencies: true\ntools: []\n", + "api": "https://training.galaxyproject.org/training-material/api/topics/admin/tutorials/introduction/tutorial.json", + "contributors": [ + { + "id": "valentinmarcon", + "joined": "2017-09", + "name": "Valentin Marcon", + "page": "https://training.galaxyproject.org/training-material/hall-of-fame/valentinmarcon/", + "url": "https://training.galaxyproject.org/training-material/api/contributors/valentinmarcon.json" + }, + { + "email": 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Fragment Assembly)\"];\n 1 --> 48b854e2-dd6e-4345-8d05-09abca6659da;\n style 48b854e2-dd6e-4345-8d05-09abca6659da stroke:#2c3143,stroke-width:4px;\n 8672c172-71a7-432c-9679-a8e37f36cf53[\"Output\\nFlye assembly on input dataset(s) (assembly_info)\"];\n 1 --> 8672c172-71a7-432c-9679-a8e37f36cf53;\n style 8672c172-71a7-432c-9679-a8e37f36cf53 stroke:#2c3143,stroke-width:4px;\n 2[\"Fasta statistics\"];\n 1 -->|consensus| 2;\n 3[\"Quast genome report\"];\n 1 -->|consensus| 3;\n 17cdf8e0-8ad4-4570-afae-1861934fc678[\"Output\\nQuast on input dataset(s): HTML report\"];\n 3 --> 17cdf8e0-8ad4-4570-afae-1861934fc678;\n style 17cdf8e0-8ad4-4570-afae-1861934fc678 stroke:#2c3143,stroke-width:4px;\n 4[\"Bandage image: Flye assembly\"];\n 1 -->|assembly_gfa| 4;\n e66bd129-146f-48dc-95b8-39a2a1ffb68d[\"Output\\nBandage Image on input dataset(s): Assembly Graph Image\"];\n 4 --> e66bd129-146f-48dc-95b8-39a2a1ffb68d;\n style e66bd129-146f-48dc-95b8-39a2a1ffb68d stroke:#2c3143,stroke-width:4px;\n 5[\"Bar chart: show contig sizes\"];\n 1 -->|assembly_info| 5;\n 6d0a4e23-d631-4e37-8930-3d22fc91369b[\"Output\\nBar chart showing contig sizes\"];\n 5 --> 6d0a4e23-d631-4e37-8930-3d22fc91369b;\n style 6d0a4e23-d631-4e37-8930-3d22fc91369b stroke:#2c3143,stroke-width:4px;", + "modified": "2024-06-24 00:06:29 +0000", + "name": "Assembly with Flye - upgraded", + "path": "topics/assembly/tutorials/largegenome/workflows/Galaxy-Workflow-Assembly_with_Flye.ga", + "test_results": null, + "tests": false, + "title": "Assembly with Flye - upgraded", + "trs_endpoint": "https://training.galaxyproject.org/training-material/api/ga4gh/trs/v2/tools/assembly-largegenome/versions/galaxy-workflow-assembly_with_flye", + "url": "https://training.galaxyproject.org/training-material/topics/assembly/tutorials/largegenome/workflows/Galaxy-Workflow-Assembly_with_Flye.ga", + "wfid": "assembly-largegenome", + "wfname": "galaxy-workflow-assembly_with_flye", + "workflow": "Galaxy-Workflow-Assembly_with_Flye.ga" + }, + { + "creators": [ + { + "class": "Person", + "identifier": "0000-0002-9906-0673", + "name": "Anna Syme" + } + ], + "license": "GPL-3.0-or-later", + "mermaid": "flowchart TD\n 0[\"\u2139\ufe0f Input Dataset\\nPolished assembly\"];\n style 0 stroke:#2c3143,stroke-width:4px;\n 1[\"\u2139\ufe0f Input Dataset\\nReference genome\"];\n style 1 stroke:#2c3143,stroke-width:4px;\n 2[\"Busco: assess assembly\"];\n 0 -->|output| 2;\n 219e4952-36e0-4b01-a407-e774b5b02dca[\"Output\\nBusco short summary\"];\n 2 --> 219e4952-36e0-4b01-a407-e774b5b02dca;\n style 219e4952-36e0-4b01-a407-e774b5b02dca stroke:#2c3143,stroke-width:4px;\n 3[\"Quast: assess assembly\"];\n 1 -->|output| 3;\n 0 -->|output| 3;\n 77ab0186-4cfd-460f-b71e-39a923414ef4[\"Output\\nQuast on input dataset(s): HTML report\"];\n 3 --> 77ab0186-4cfd-460f-b71e-39a923414ef4;\n style 77ab0186-4cfd-460f-b71e-39a923414ef4 stroke:#2c3143,stroke-width:4px;", + "modified": "2024-06-24 00:06:29 +0000", + "name": "Assess genome quality - upgraded", + "path": "topics/assembly/tutorials/largegenome/workflows/Galaxy-Workflow-Assess_genome_quality.ga", + "test_results": null, + "tests": false, + "title": "Assess genome quality - upgraded", + "trs_endpoint": "https://training.galaxyproject.org/training-material/api/ga4gh/trs/v2/tools/assembly-largegenome/versions/galaxy-workflow-assess_genome_quality", + "url": "https://training.galaxyproject.org/training-material/topics/assembly/tutorials/largegenome/workflows/Galaxy-Workflow-Assess_genome_quality.ga", + "wfid": "assembly-largegenome", + "wfname": "galaxy-workflow-assess_genome_quality", + "workflow": "Galaxy-Workflow-Assess_genome_quality.ga" + }, + { + "creators": [ + { + "class": "Person", + "identifier": "0000-0002-9906-0673", + "name": "Anna Syme" + } + ], + "license": "GPL-3.0-or-later", + "mermaid": "flowchart TD\n 0[\"\u2139\ufe0f Input Dataset\\nlong reads\"];\n style 0 stroke:#2c3143,stroke-width:4px;\n 1[\"\u2139\ufe0f Input Dataset\\nR1\"];\n style 1 stroke:#2c3143,stroke-width:4px;\n 2[\"\u2139\ufe0f Input Dataset\\nR2\"];\n style 2 stroke:#2c3143,stroke-width:4px;\n 3[\"\u2139\ufe0f Input Parameter\\nminimap settings for long reads\"];\n style 3 fill:#ded,stroke:#393,stroke-width:4px;\n 4[\"\u2139\ufe0f Input Dataset\\nReference genome for Quast\"];\n style 4 stroke:#2c3143,stroke-width:4px;\n 5[\"\ud83d\udee0\ufe0f Subworkflow\\nkmer counting - meryl - upgraded\"];\n style 5 fill:#edd,stroke:#900,stroke-width:4px;\n 1 -->|output| 5;\n 6[\"\ud83d\udee0\ufe0f Subworkflow\\nData QC - upgraded\"];\n style 6 fill:#edd,stroke:#900,stroke-width:4px;\n 1 -->|output| 6;\n 2 -->|output| 6;\n 0 -->|output| 6;\n 7[\"\ud83d\udee0\ufe0f Subworkflow\\nTrim and filter reads - fastp - upgraded \"];\n style 7 fill:#edd,stroke:#900,stroke-width:4px;\n 1 -->|output| 7;\n 2 -->|output| 7;\n 0 -->|output| 7;\n 8[\"\ud83d\udee0\ufe0f Subworkflow\\nAssembly with Flye - upgraded\"];\n style 8 fill:#edd,stroke:#900,stroke-width:4px;\n 7 -->|fastp filtered long reads| 8;\n 9[\"\ud83d\udee0\ufe0f Subworkflow\\nAssembly polishing - upgraded\"];\n style 9 fill:#edd,stroke:#900,stroke-width:4px;\n 8 -->|Flye assembly on input datasets consensus| 9;\n 7 -->|fastp filtered R1 reads| 9;\n 7 -->|fastp filtered long reads| 9;\n 3 -->|output| 9;\n 10[\"\ud83d\udee0\ufe0f Subworkflow\\nAssess genome quality - upgraded\"];\n style 10 fill:#edd,stroke:#900,stroke-width:4px;\n 9 -->|Assembly polished by long reads using Medaka| 10;\n 4 -->|output| 10;", + "modified": "2024-06-24 00:06:29 +0000", + "name": "Combined workflows for large genome assembly - upgraded ", + "path": "topics/assembly/tutorials/largegenome/workflows/Galaxy-Workflow-Combined_workflows_for_large_genome_assembly.ga", + "test_results": null, + "tests": false, + "title": "Combined workflows for large genome assembly - upgraded ", + "trs_endpoint": "https://training.galaxyproject.org/training-material/api/ga4gh/trs/v2/tools/assembly-largegenome/versions/galaxy-workflow-combined_workflows_for_large_genome_assembly", + "url": "https://training.galaxyproject.org/training-material/topics/assembly/tutorials/largegenome/workflows/Galaxy-Workflow-Combined_workflows_for_large_genome_assembly.ga", + "wfid": "assembly-largegenome", + "wfname": "galaxy-workflow-combined_workflows_for_large_genome_assembly", + "workflow": "Galaxy-Workflow-Combined_workflows_for_large_genome_assembly.ga" + }, + { + "creators": [ + { + "class": "Person", + "identifier": "0000-0002-9906-0673", + "name": "Anna Syme" + } + ], + "license": "GPL-3.0-or-later", + "mermaid": "flowchart TD\n 0[\"\u2139\ufe0f Input Dataset\\nInput file: long reads\"];\n style 0 stroke:#2c3143,stroke-width:4px;\n 1[\"\u2139\ufe0f Input Dataset\\nInput file: Illumina reads R1\"];\n style 1 stroke:#2c3143,stroke-width:4px;\n 2[\"\u2139\ufe0f Input Dataset\\nInput file: Illumina reads R2\"];\n style 2 stroke:#2c3143,stroke-width:4px;\n 3[\"Nanoplot: long reads\"];\n 0 -->|output| 3;\n 73d0e4cf-366e-41c1-810a-b269638826b3[\"Output\\nNanoPlot on input dataset(s): HTML report\"];\n 3 --> 73d0e4cf-366e-41c1-810a-b269638826b3;\n style 73d0e4cf-366e-41c1-810a-b269638826b3 stroke:#2c3143,stroke-width:4px;\n 4[\"FastQC on R1\"];\n 1 -->|output| 4;\n 5[\"FastQC on R2\"];\n 2 -->|output| 5;\n 6[\"MultiQC: combine fastQC reports\"];\n 4 -->|text_file| 6;\n 5 -->|text_file| 6;\n 8baf8700-876e-4a74-ad99-6e656f3ba618[\"Output\\nMultiQC on input dataset(s): Webpage\"];\n 6 --> 8baf8700-876e-4a74-ad99-6e656f3ba618;\n style 8baf8700-876e-4a74-ad99-6e656f3ba618 stroke:#2c3143,stroke-width:4px;", + "modified": "2024-06-24 00:06:29 +0000", + "name": "Data QC - upgraded", + "path": "topics/assembly/tutorials/largegenome/workflows/Galaxy-Workflow-Data_QC.ga", + "test_results": null, + "tests": false, + "title": "Data QC - upgraded", + "trs_endpoint": "https://training.galaxyproject.org/training-material/api/ga4gh/trs/v2/tools/assembly-largegenome/versions/galaxy-workflow-data_qc", + "url": "https://training.galaxyproject.org/training-material/topics/assembly/tutorials/largegenome/workflows/Galaxy-Workflow-Data_QC.ga", + "wfid": "assembly-largegenome", + "wfname": "galaxy-workflow-data_qc", + "workflow": "Galaxy-Workflow-Data_QC.ga" + }, + { + "creators": [ + { + "class": "Person", + "identifier": "0000-0002-9906-0673", + "name": "Anna Syme" + } + ], + "license": "GPL-3.0-or-later", + "mermaid": "flowchart TD\n 0[\"\u2139\ufe0f Input Dataset\\nAssembly to be polished\"];\n style 0 stroke:#2c3143,stroke-width:4px;\n 1[\"\u2139\ufe0f Input Dataset\\nIllumina reads, R1, in fastq.gz format\"];\n style 1 stroke:#2c3143,stroke-width:4px;\n 2[\"Minimap2 round 1: map reads to assembly\"];\n 1 -->|output| 2;\n 0 -->|output| 2;\n 3[\"Racon round 1: polish assembly\"];\n 0 -->|output| 3;\n 2 -->|alignment_output| 3;\n 1 -->|output| 3;\n 4[\"Minimap2 round 2: map reads to assembly\"];\n 1 -->|output| 4;\n 3 -->|consensus| 4;\n 5[\"Racon round 2: polish assembly\"];\n 3 -->|consensus| 5;\n 4 -->|alignment_output| 5;\n 1 -->|output| 5;\n 594819c3-668e-4575-b9a6-4459ffacf952[\"Output\\nAssembly polished by short reads using Racon\"];\n 5 --> 594819c3-668e-4575-b9a6-4459ffacf952;\n style 594819c3-668e-4575-b9a6-4459ffacf952 stroke:#2c3143,stroke-width:4px;", + "modified": "2024-06-24 00:06:29 +0000", + "name": "Racon polish with Illumina reads (R1 only), x2 - upgraded", + "path": "topics/assembly/tutorials/largegenome/workflows/Galaxy-Workflow-Racon_polish_with_Illumina_reads_x2.ga", + "test_results": null, + "tests": false, + "title": "Racon polish with Illumina reads (R1 only), x2 - upgraded", + "trs_endpoint": "https://training.galaxyproject.org/training-material/api/ga4gh/trs/v2/tools/assembly-largegenome/versions/galaxy-workflow-racon_polish_with_illumina_reads_x2", + "url": "https://training.galaxyproject.org/training-material/topics/assembly/tutorials/largegenome/workflows/Galaxy-Workflow-Racon_polish_with_Illumina_reads_x2.ga", + "wfid": "assembly-largegenome", + "wfname": "galaxy-workflow-racon_polish_with_illumina_reads_x2", + "workflow": "Galaxy-Workflow-Racon_polish_with_Illumina_reads_x2.ga" + }, + { + "creators": [ + { + "class": "Person", + "identifier": "0000-0002-9906-0673", + "name": "Anna Syme" + } + ], + "license": "GPL-3.0-or-later", + "mermaid": "flowchart TD\n 0[\"\u2139\ufe0f Input Dataset\\nAssembly to be polished\"];\n style 0 stroke:#2c3143,stroke-width:4px;\n 1[\"\u2139\ufe0f Input Dataset\\nlong reads\"];\n style 1 stroke:#2c3143,stroke-width:4px;\n 2[\"\u2139\ufe0f Input Parameter\\nminimap setting for long reads \"];\n style 2 fill:#ded,stroke:#393,stroke-width:4px;\n 3[\"Minimap2: map long reads to assembly\"];\n 2 -->|output| 3;\n 1 -->|output| 3;\n 0 -->|output| 3;\n 4[\"Racon: polish 1\"];\n 0 -->|output| 4;\n 3 -->|alignment_output| 4;\n 1 -->|output| 4;\n 5[\"Minimap2: map long reads to polished assembly 1\"];\n 2 -->|output| 5;\n 1 -->|output| 5;\n 4 -->|consensus| 5;\n 6[\"Racon: polish 2\"];\n 4 -->|consensus| 6;\n 5 -->|alignment_output| 6;\n 1 -->|output| 6;\n 7[\"Minimap2: map long reads to polished assembly 2\"];\n 2 -->|output| 7;\n 1 -->|output| 7;\n 6 -->|consensus| 7;\n 8[\"Racon: polish 3\"];\n 6 -->|consensus| 8;\n 7 -->|alignment_output| 8;\n 1 -->|output| 8;\n 9[\"Minimap2: map long reads to polished assembly 3\"];\n 2 -->|output| 9;\n 1 -->|output| 9;\n 8 -->|consensus| 9;\n 10[\"Racon: polish 4\"];\n 8 -->|consensus| 10;\n 9 -->|alignment_output| 10;\n 1 -->|output| 10;\n bcf0f03c-5951-46a7-aa38-545aed9bc183[\"Output\\nAssembly polished by long reads using Racon\"];\n 10 --> bcf0f03c-5951-46a7-aa38-545aed9bc183;\n style bcf0f03c-5951-46a7-aa38-545aed9bc183 stroke:#2c3143,stroke-width:4px;", + "modified": "2024-06-24 00:06:29 +0000", + "name": "Racon polish with long reads, x4 - upgraded", + "path": "topics/assembly/tutorials/largegenome/workflows/Galaxy-Workflow-Racon_polish_with_long_reads_x4.ga", + "test_results": null, + "tests": false, + "title": "Racon polish with long reads, x4 - upgraded", + "trs_endpoint": "https://training.galaxyproject.org/training-material/api/ga4gh/trs/v2/tools/assembly-largegenome/versions/galaxy-workflow-racon_polish_with_long_reads_x4", + "url": "https://training.galaxyproject.org/training-material/topics/assembly/tutorials/largegenome/workflows/Galaxy-Workflow-Racon_polish_with_long_reads_x4.ga", + "wfid": "assembly-largegenome", + "wfname": "galaxy-workflow-racon_polish_with_long_reads_x4", + "workflow": "Galaxy-Workflow-Racon_polish_with_long_reads_x4.ga" + }, + { + "creators": [ + { + "class": "Person", + "identifier": "0000-0002-9906-0673", + "name": "Anna Syme" + } + ], + "license": "GPL-3.0-or-later", + "mermaid": "flowchart TD\n 0[\"\u2139\ufe0f Input Dataset\\nIllumina reads R1\"];\n style 0 stroke:#2c3143,stroke-width:4px;\n 1[\"\u2139\ufe0f Input Dataset\\nIllumina reads R2\"];\n style 1 stroke:#2c3143,stroke-width:4px;\n 2[\"\u2139\ufe0f Input Dataset\\nlong reads\"];\n style 2 stroke:#2c3143,stroke-width:4px;\n 3[\"fastp on short reads\"];\n 0 -->|output| 3;\n 1 -->|output| 3;\n 656e4138-41ab-4561-8989-33de9ac9a2f3[\"Output\\nfastp report on short reads html\"];\n 3 --> 656e4138-41ab-4561-8989-33de9ac9a2f3;\n style 656e4138-41ab-4561-8989-33de9ac9a2f3 stroke:#2c3143,stroke-width:4px;\n 0d53f347-0368-47cb-953a-2e4dac57e013[\"Output\\nfastp filtered R1 reads\"];\n 3 --> 0d53f347-0368-47cb-953a-2e4dac57e013;\n style 0d53f347-0368-47cb-953a-2e4dac57e013 stroke:#2c3143,stroke-width:4px;\n 10fbe1e5-400c-4ffe-8794-9b776b0d7322[\"Output\\nfastp report on short reads json\"];\n 3 --> 10fbe1e5-400c-4ffe-8794-9b776b0d7322;\n style 10fbe1e5-400c-4ffe-8794-9b776b0d7322 stroke:#2c3143,stroke-width:4px;\n 639ed3f7-0e51-4e5d-b6f8-081378962109[\"Output\\nfastp filtered R2 reads\"];\n 3 --> 639ed3f7-0e51-4e5d-b6f8-081378962109;\n style 639ed3f7-0e51-4e5d-b6f8-081378962109 stroke:#2c3143,stroke-width:4px;\n 4[\"fastp on long reads\"];\n 2 -->|output| 4;\n 5e0d2c3d-41a4-4823-ae9c-b1e4d2826541[\"Output\\nfastp report on long reads html\"];\n 4 --> 5e0d2c3d-41a4-4823-ae9c-b1e4d2826541;\n style 5e0d2c3d-41a4-4823-ae9c-b1e4d2826541 stroke:#2c3143,stroke-width:4px;\n e6018ad6-86f4-4e78-8cf2-ccc8b97022fe[\"Output\\nfastp filtered long reads\"];\n 4 --> e6018ad6-86f4-4e78-8cf2-ccc8b97022fe;\n style e6018ad6-86f4-4e78-8cf2-ccc8b97022fe stroke:#2c3143,stroke-width:4px;\n 69f8383b-a1be-4a74-95f4-3dba35e01426[\"Output\\nfastp report on long reads json\"];\n 4 --> 69f8383b-a1be-4a74-95f4-3dba35e01426;\n style 69f8383b-a1be-4a74-95f4-3dba35e01426 stroke:#2c3143,stroke-width:4px;", + "modified": "2024-06-24 00:06:29 +0000", + "name": "Trim and filter reads - fastp - upgraded ", + "path": "topics/assembly/tutorials/largegenome/workflows/Galaxy-Workflow-Trim_and_filter_reads.ga", + "test_results": null, + "tests": false, + "title": "Trim and filter reads - fastp - upgraded ", + "trs_endpoint": "https://training.galaxyproject.org/training-material/api/ga4gh/trs/v2/tools/assembly-largegenome/versions/galaxy-workflow-trim_and_filter_reads", + "url": "https://training.galaxyproject.org/training-material/topics/assembly/tutorials/largegenome/workflows/Galaxy-Workflow-Trim_and_filter_reads.ga", + "wfid": "assembly-largegenome", + "wfname": "galaxy-workflow-trim_and_filter_reads", + "workflow": "Galaxy-Workflow-Trim_and_filter_reads.ga" + }, + { + "creators": [ + { + "class": "Person", + "identifier": "0000-0002-9906-0673", + "name": "Anna Syme" + } + ], + "license": "GPL-3.0-or-later", + "mermaid": "flowchart TD\n 0[\"\u2139\ufe0f Input Dataset\\nIllumina reads R1\"];\n style 0 stroke:#2c3143,stroke-width:4px;\n 1[\"Meryl - count kmers\"];\n 0 -->|output| 1;\n 899ddd93-4c0f-4f81-a973-8120494ed983[\"Output\\nMeryl on input dataset(s): read-db.meryldb\"];\n 1 --> 899ddd93-4c0f-4f81-a973-8120494ed983;\n style 899ddd93-4c0f-4f81-a973-8120494ed983 stroke:#2c3143,stroke-width:4px;\n 2[\"Meryl - generate histogram\"];\n 1 -->|read_db| 2;\n 3[\"Genomescope\"];\n 2 -->|read_db_hist| 3;\n efc727b6-1ef4-4c4c-8cce-35c7d3cc8aac[\"Output\\nGenomeScope on input dataset(s) Transformed log plot\"];\n 3 --> efc727b6-1ef4-4c4c-8cce-35c7d3cc8aac;\n style efc727b6-1ef4-4c4c-8cce-35c7d3cc8aac stroke:#2c3143,stroke-width:4px;\n 701df341-5767-44bc-ade2-6af498ab7467[\"Output\\nGenomeScope on input dataset(s) Transformed linear plot\"];\n 3 --> 701df341-5767-44bc-ade2-6af498ab7467;\n style 701df341-5767-44bc-ade2-6af498ab7467 stroke:#2c3143,stroke-width:4px;\n 85fa4004-b351-47b3-84aa-6788d338037a[\"Output\\nGenomeScope on input dataset(s) Log plot\"];\n 3 --> 85fa4004-b351-47b3-84aa-6788d338037a;\n style 85fa4004-b351-47b3-84aa-6788d338037a stroke:#2c3143,stroke-width:4px;\n c71ce055-98f0-4354-9397-2f8833b90cc4[\"Output\\nGenomeScope on input dataset(s) Linear plot\"];\n 3 --> c71ce055-98f0-4354-9397-2f8833b90cc4;\n style c71ce055-98f0-4354-9397-2f8833b90cc4 stroke:#2c3143,stroke-width:4px;", + "modified": "2024-06-24 00:06:29 +0000", + "name": "kmer counting - meryl - upgraded", + "path": "topics/assembly/tutorials/largegenome/workflows/Galaxy-Workflow-kmer_counting.ga", + "test_results": null, + "tests": false, + "title": "kmer counting - meryl - upgraded", + "trs_endpoint": "https://training.galaxyproject.org/training-material/api/ga4gh/trs/v2/tools/assembly-largegenome/versions/galaxy-workflow-kmer_counting", + "url": "https://training.galaxyproject.org/training-material/topics/assembly/tutorials/largegenome/workflows/Galaxy-Workflow-kmer_counting.ga", + "wfid": "assembly-largegenome", + "wfname": "galaxy-workflow-kmer_counting", + "workflow": "Galaxy-Workflow-kmer_counting.ga" + } + ], + "zenodo_link": "https://doi.org/10.5281/zenodo.7055935" + }, + { + "abbreviations": { + "SVs": "Structural Variations" + }, + "admin_install": { + 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+ "objectives": [ + "Load genome into Galaxy", + "Annotate genome with Maker", + "Evaluate annotation quality with BUSCO", + "View annotations in JBrowse" + ], + "pageviews": 21070, + "priority": 4, + "pub_date": "2018-09-24", + "questions": [ + "How to annotate an eukaryotic genome?", + "How to evaluate and visualize annotated genomic features?" + ], + "short_id": "T00166", + "short_tools": [ + "maker", + "busco", + "gffread", + "maker_map_ids", + "augustus_training", + "fasta-stats", + "snap_training", + "jcvi_gff_stats", + "jbrowse" + ], + "slides": false, + "slides_recordings": false, + "subtopic": "eukaryote", + "supported_servers": { + "exact": [ + { + "name": "UseGalaxy.eu", + "url": "https://usegalaxy.eu", + "usegalaxy": true + }, + { + "name": "UseGalaxy.no", + "url": "https://usegalaxy.no/", + "usegalaxy": false + }, + { + "name": "UseGalaxy.org (Main)", + "url": "https://usegalaxy.org", + "usegalaxy": true + } + ], + "inexact": [ + { + "name": "UseGalaxy.cz", + "url": 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+ "topic_name_human": "Genome Annotation", + "tours": false, + "translations": { + "slides": [], + "tutorial": [], + "video": false + }, + "tutorial_name": "annotation-with-maker", + "type": "tutorial", + "url": "https://training.galaxyproject.org//topics/genome-annotation/tutorials/annotation-with-maker/tutorial.html", + "urls": { + "hands_on": "https://training.galaxyproject.org/training-material/api/topics/genome-annotation/tutorials/annotation-with-maker/tutorial.json", + "slides": "https://training.galaxyproject.org/training-material/api/topics/genome-annotation/tutorials/annotation-with-maker/tutorial.json" + }, + "version": 37, + "video": false, + "video_versions": 0, + "video_view": 0, + "visit_duration": 114, + "visitors": 11167, + "workflows": [ + { + "creators": [ + { + "class": "Person", + "identifier": "0000-0003-0914-2470", + "name": "Anthony Bretaudeau" + }, + { + "class": "Organization", + "name": "French National Institute for Agriculture, Food, and Environment (INRAE)" + } + ], + "license": "GPL-3.0-or-later", + "mermaid": "flowchart TD\n 0[\"\u2139\ufe0f Input Dataset\\nEST and/or cDNA\"];\n style 0 stroke:#2c3143,stroke-width:4px;\n 1[\"\u2139\ufe0f Input Dataset\\nProtein sequences\"];\n style 1 stroke:#2c3143,stroke-width:4px;\n 2[\"\u2139\ufe0f Input Dataset\\nGenome sequence\"];\n style 2 stroke:#2c3143,stroke-width:4px;\n 3[\"Maker\"];\n 0 -->|output| 3;\n 2 -->|output| 3;\n 1 -->|output| 3;\n 4[\"Fasta Statistics\"];\n 2 -->|output| 4;\n 2d3829e6-f5b9-46d1-83e3-17efff814e09[\"Output\\nFasta Statistics on input dataset(s): Fasta summary stats\"];\n 4 --> 2d3829e6-f5b9-46d1-83e3-17efff814e09;\n style 2d3829e6-f5b9-46d1-83e3-17efff814e09 stroke:#2c3143,stroke-width:4px;\n 5[\"Busco\"];\n 2 -->|output| 5;\n a15e1214-23a2-484b-9c75-1635fcd61fe3[\"Output\\nBUSCO: short summary\"];\n 5 --> a15e1214-23a2-484b-9c75-1635fcd61fe3;\n style a15e1214-23a2-484b-9c75-1635fcd61fe3 stroke:#2c3143,stroke-width:4px;\n 7806088d-f657-4389-9d54-6a3bdfa233b2[\"Output\\nBUSCO: list of missing ortholog genes\"];\n 5 --> 7806088d-f657-4389-9d54-6a3bdfa233b2;\n style 7806088d-f657-4389-9d54-6a3bdfa233b2 stroke:#2c3143,stroke-width:4px;\n 6a27d0f6-d83c-42b1-a7af-5f99ba5debe2[\"Output\\nBUSCO: full table\"];\n 5 --> 6a27d0f6-d83c-42b1-a7af-5f99ba5debe2;\n style 6a27d0f6-d83c-42b1-a7af-5f99ba5debe2 stroke:#2c3143,stroke-width:4px;\n 6[\"Train Augustus\"];\n 2 -->|output| 6;\n 3 -->|output_gff| 6;\n 7[\"Genome annotation statistics\"];\n 3 -->|output_gff| 7;\n 2 -->|output| 7;\n 2bed9aa6-5f22-494d-9caa-f0ece8464f15[\"Output\\nGenome annotation statistics: graphs (first round)\"];\n 7 --> 2bed9aa6-5f22-494d-9caa-f0ece8464f15;\n style 2bed9aa6-5f22-494d-9caa-f0ece8464f15 stroke:#2c3143,stroke-width:4px;\n 71d41ce2-e90d-435b-9b64-ab4320524770[\"Output\\nGenome annotation statistics: summary (first round)\"];\n 7 --> 71d41ce2-e90d-435b-9b64-ab4320524770;\n style 71d41ce2-e90d-435b-9b64-ab4320524770 stroke:#2c3143,stroke-width:4px;\n 8[\"gffread\"];\n 3 -->|output_gff| 8;\n 2 -->|output| 8;\n 9[\"Train SNAP\"];\n 2 -->|output| 9;\n 3 -->|output_gff| 9;\n 10[\"Busco\"];\n 8 -->|output_exons| 10;\n a79ab19d-9802-42ca-8ffa-8c197a16c8b8[\"Output\\nBusco summary first round\"];\n 10 --> a79ab19d-9802-42ca-8ffa-8c197a16c8b8;\n style a79ab19d-9802-42ca-8ffa-8c197a16c8b8 stroke:#2c3143,stroke-width:4px;\n 11[\"Maker\"];\n 6 -->|output_tar| 11;\n 9 -->|output| 11;\n 2 -->|output| 11;\n 3 -->|output_full| 11;\n 12[\"gffread\"];\n 11 -->|output_gff| 12;\n 2 -->|output| 12;\n 13[\"Train Augustus\"];\n 2 -->|output| 13;\n 11 -->|output_gff| 13;\n 42d5aadd-3a8c-441d-913f-ee8745a99b0b[\"Output\\nAugus: trained model\"];\n 13 --> 42d5aadd-3a8c-441d-913f-ee8745a99b0b;\n style 42d5aadd-3a8c-441d-913f-ee8745a99b0b stroke:#2c3143,stroke-width:4px;\n 14[\"Genome annotation statistics\"];\n 11 -->|output_gff| 14;\n 2 -->|output| 14;\n 9def2c6e-fd3c-4969-b5d4-d13c187c1144[\"Output\\nGenome annotation statistics: graphs (second round)\"];\n 14 --> 9def2c6e-fd3c-4969-b5d4-d13c187c1144;\n style 9def2c6e-fd3c-4969-b5d4-d13c187c1144 stroke:#2c3143,stroke-width:4px;\n 61f7a150-a4a2-46b1-89f5-2170d466656e[\"Output\\nGenome annotation statistics: summary (second round)\"];\n 14 --> 61f7a150-a4a2-46b1-89f5-2170d466656e;\n style 61f7a150-a4a2-46b1-89f5-2170d466656e stroke:#2c3143,stroke-width:4px;\n 15[\"Train SNAP\"];\n 2 -->|output| 15;\n 11 -->|output_gff| 15;\n 458887f7-0661-4bde-ab1a-875aac6caed8[\"Output\\nSNAP: trained model\"];\n 15 --> 458887f7-0661-4bde-ab1a-875aac6caed8;\n style 458887f7-0661-4bde-ab1a-875aac6caed8 stroke:#2c3143,stroke-width:4px;\n 16[\"Busco\"];\n 12 -->|output_exons| 16;\n f52b1b41-0ea5-4384-bd43-4927273c909c[\"Output\\nBusco summary second round\"];\n 16 --> f52b1b41-0ea5-4384-bd43-4927273c909c;\n style f52b1b41-0ea5-4384-bd43-4927273c909c stroke:#2c3143,stroke-width:4px;\n 17[\"Maker\"];\n 13 -->|output_tar| 17;\n 15 -->|output| 17;\n 2 -->|output| 17;\n 11 -->|output_full| 17;\n 18[\"Map annotation ids\"];\n 17 -->|output_gff| 18;\n 63575dcf-54d1-4cb1-b5fb-a2810b4da807[\"Output\\nMap annotation ids: renamed GFF\"];\n 18 --> 63575dcf-54d1-4cb1-b5fb-a2810b4da807;\n style 63575dcf-54d1-4cb1-b5fb-a2810b4da807 stroke:#2c3143,stroke-width:4px;\n 19[\"JBrowse\"];\n 2 -->|output| 19;\n 3 -->|output_gff| 19;\n 18 -->|renamed| 19;\n 11 -->|output_gff| 19;\n 3 -->|output_evidences| 19;\n 11 -->|output_evidences| 19;\n 17 -->|output_evidences| 19;\n 5a38ae83-e177-45f3-aa9f-4c1844584fa5[\"Output\\nJBrowse: report\"];\n 19 --> 5a38ae83-e177-45f3-aa9f-4c1844584fa5;\n style 5a38ae83-e177-45f3-aa9f-4c1844584fa5 stroke:#2c3143,stroke-width:4px;\n 20[\"gffread\"];\n 18 -->|renamed| 20;\n 2 -->|output| 20;\n 52fcac23-b612-4377-a19a-2e3b667a23e3[\"Output\\nGffread: exons\"];\n 20 --> 52fcac23-b612-4377-a19a-2e3b667a23e3;\n style 52fcac23-b612-4377-a19a-2e3b667a23e3 stroke:#2c3143,stroke-width:4px;\n 5c47c25e-0ddb-4ade-a5d0-873ff4add28c[\"Output\\nGffread: translation of cds\"];\n 20 --> 5c47c25e-0ddb-4ade-a5d0-873ff4add28c;\n style 5c47c25e-0ddb-4ade-a5d0-873ff4add28c stroke:#2c3143,stroke-width:4px;\n 84d9183b-9589-464b-9f82-4dae7f87133e[\"Output\\nGffread: cds\"];\n 20 --> 84d9183b-9589-464b-9f82-4dae7f87133e;\n style 84d9183b-9589-464b-9f82-4dae7f87133e stroke:#2c3143,stroke-width:4px;\n 21[\"Genome annotation statistics\"];\n 18 -->|renamed| 21;\n 2 -->|output| 21;\n f5d4a6bf-1ff8-435a-8084-7921e25bff3a[\"Output\\nGenome annotation statistics: graphs (third round)\"];\n 21 --> f5d4a6bf-1ff8-435a-8084-7921e25bff3a;\n style f5d4a6bf-1ff8-435a-8084-7921e25bff3a stroke:#2c3143,stroke-width:4px;\n 5a863bd4-10f2-4ac8-81b6-ce8b7f663e42[\"Output\\nGenome annotation statistics: summary (third round)\"];\n 21 --> 5a863bd4-10f2-4ac8-81b6-ce8b7f663e42;\n style 5a863bd4-10f2-4ac8-81b6-ce8b7f663e42 stroke:#2c3143,stroke-width:4px;\n 22[\"Busco\"];\n 20 -->|output_exons| 22;\n 0bc78676-eecc-4168-894d-34c4ca07cdf3[\"Output\\nBusco summary final 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Graph/Display Data\n tool_shed_url: https://toolshed.g2.bx.psu.edu/\n- name: jcvi_gff_stats\n owner: iuc\n revisions: 8cffbd184762\n tool_panel_section_label: Annotation\n tool_shed_url: https://toolshed.g2.bx.psu.edu/\n- name: maker\n owner: iuc\n revisions: d46d803ca6cc\n tool_panel_section_label: Annotation\n tool_shed_url: https://toolshed.g2.bx.psu.edu/\n- name: maker_map_ids\n owner: iuc\n revisions: e906fa778440\n tool_panel_section_label: Annotation\n tool_shed_url: https://toolshed.g2.bx.psu.edu/\n- name: snap_training\n owner: iuc\n revisions: 2a598c0aa042\n tool_panel_section_label: Annotation\n tool_shed_url: https://toolshed.g2.bx.psu.edu/\n", + "api": "https://training.galaxyproject.org/training-material/api/topics/genome-annotation/tutorials/annotation-with-maker/tutorial.json", + "contributors": [ + { + "affiliations": [ + "gallantries", + "eurosciencegateway", + "elixir-europe" + ], + "contact_for_training": true, + "elixir_node": "fr", + "email": "anthony.bretaudeau@inrae.fr", + "fediverse": "https://genomic.social/@abretaud", + "fediverse_flavor": "mastodon", + "id": "abretaud", + "joined": "2017-09", + "location": { + "country": "FR", + "lat": 48.11, + "lon": -1.64 + }, + "matrix": "abretaud:matrix.org", + "name": "Anthony Bretaudeau", + "orcid": "0000-0003-0914-2470", + "page": "https://training.galaxyproject.org/training-material/hall-of-fame/abretaud/", + "twitter": "abretau", + "url": "https://training.galaxyproject.org/training-material/api/contributors/abretaud.json" + } + ], + "dir": "topics/genome-annotation/tutorials/annotation-with-maker", + "edam_operation": [ + "Genome assembly", + "Sequence annotation", + "Homology-based gene prediction", + "Sequence assembly validation", + "Scaffolding", + "Gene prediction", + "Genome visualisation", + "Ab-initio gene prediction", + "Genome annotation", + "Transcriptome assembly", + "Operation" + ], + "edam_topic": [], + "exact_supported_servers": [ + "UseGalaxy.eu", + "UseGalaxy.no", + "UseGalaxy.org (Main)" + ], + "feedback_mean_note": 3.0, + "feedback_number": 9, + "hands_on": true, + "id": "genome-annotation/annotation-with-maker", + "inexact_supported_servers": [ + "UseGalaxy.cz", + "UseGalaxy.org.au" + ], + "js_requirements": { + "mathjax": null, + "mermaid": false + }, + "key_points": [ + "Maker allows to annotate a eukaryotic genome.", + "BUSCO and JBrowse allow to inspect the quality of an annotation." + ], + "layout": "tutorial_hands_on", + "level": "Advanced", + "license": "CC-BY-4.0", + "mod_date": "2024-02-29", + "objectives": [ + "Load genome into Galaxy", + "Annotate genome with Maker", + "Evaluate annotation quality with BUSCO", + "View annotations in JBrowse" + ], + "pageviews": 21070, + "priority": 4, + "pub_date": "2018-09-24", + "questions": [ + "How to annotate an eukaryotic genome?", + "How to evaluate and visualize annotated genomic features?" + ], + "short_id": "T00166", + "short_tools": [ + "maker", + "busco", + "gffread", + "maker_map_ids", + "augustus_training", + "fasta-stats", + "snap_training", + "jcvi_gff_stats", + "jbrowse" + ], + "slides": false, + "slides_recordings": false, + "subtopic": "eukaryote", + "supported_servers": { + "exact": [ + { + "name": "UseGalaxy.eu", + "url": "https://usegalaxy.eu", + "usegalaxy": true + }, + { + "name": "UseGalaxy.no", + "url": "https://usegalaxy.no/", + "usegalaxy": false + }, + { + "name": "UseGalaxy.org (Main)", + "url": "https://usegalaxy.org", + "usegalaxy": true + } + ], + "inexact": [ + { + "name": "UseGalaxy.cz", + "url": "https://usegalaxy.cz/", + "usegalaxy": false + }, + { + "name": "UseGalaxy.org.au", + "url": "https://usegalaxy.org.au", + "usegalaxy": true + } + ] + }, + "symlink": null, + "tags": [ + "gmod", + "eukaryote", + "jbrowse1", + "maker" + ], + "time_estimation": "4h", + "title": "Genome annotation with Maker", + "tools": [ + "toolshed.g2.bx.psu.edu/repos/bgruening/augustus_training/augustus_training/3.3.3", + "toolshed.g2.bx.psu.edu/repos/devteam/gffread/gffread/2.2.1.2", + "toolshed.g2.bx.psu.edu/repos/iuc/busco/busco/4.1.4", + "toolshed.g2.bx.psu.edu/repos/iuc/fasta_stats/fasta-stats/1.0.1", + "toolshed.g2.bx.psu.edu/repos/iuc/jbrowse/jbrowse/1.16.10+galaxy0", + "toolshed.g2.bx.psu.edu/repos/iuc/jcvi_gff_stats/jcvi_gff_stats/0.8.4", + "toolshed.g2.bx.psu.edu/repos/iuc/maker/maker/2.31.11", + "toolshed.g2.bx.psu.edu/repos/iuc/maker_map_ids/maker_map_ids/2.31.11", + "toolshed.g2.bx.psu.edu/repos/iuc/snap_training/snap_training/2013_11_29+galaxy1" + ], + "topic_name": "genome-annotation", + "topic_name_human": "Genome Annotation", + "tours": false, + "translations": { + "slides": [], + "tutorial": [], + "video": false + }, + "tutorial_name": "annotation-with-maker", + "type": "tutorial", + "url": "https://training.galaxyproject.org//topics/genome-annotation/tutorials/annotation-with-maker/tutorial.html", + "urls": { + "hands_on": "https://training.galaxyproject.org/training-material/api/topics/genome-annotation/tutorials/annotation-with-maker/tutorial.json", + "slides": "https://training.galaxyproject.org/training-material/api/topics/genome-annotation/tutorials/annotation-with-maker/tutorial.json" + }, + "version": 37, + "video": false, + "video_versions": 0, + "video_view": 0, + "visit_duration": 114, + "visitors": 11167, + "workflows": [ + { + "creators": [ + { + "class": "Person", + "identifier": "0000-0003-0914-2470", + "name": "Anthony Bretaudeau" + }, + { + "class": "Organization", + "name": "French National Institute for Agriculture, Food, and Environment (INRAE)" + } + ], + "license": "GPL-3.0-or-later", + "mermaid": "flowchart TD\n 0[\"\u2139\ufe0f Input Dataset\\nEST and/or cDNA\"];\n style 0 stroke:#2c3143,stroke-width:4px;\n 1[\"\u2139\ufe0f Input Dataset\\nProtein sequences\"];\n style 1 stroke:#2c3143,stroke-width:4px;\n 2[\"\u2139\ufe0f Input Dataset\\nGenome sequence\"];\n style 2 stroke:#2c3143,stroke-width:4px;\n 3[\"Maker\"];\n 0 -->|output| 3;\n 2 -->|output| 3;\n 1 -->|output| 3;\n 4[\"Fasta Statistics\"];\n 2 -->|output| 4;\n 2d3829e6-f5b9-46d1-83e3-17efff814e09[\"Output\\nFasta Statistics on input dataset(s): Fasta summary stats\"];\n 4 --> 2d3829e6-f5b9-46d1-83e3-17efff814e09;\n style 2d3829e6-f5b9-46d1-83e3-17efff814e09 stroke:#2c3143,stroke-width:4px;\n 5[\"Busco\"];\n 2 -->|output| 5;\n a15e1214-23a2-484b-9c75-1635fcd61fe3[\"Output\\nBUSCO: short summary\"];\n 5 --> a15e1214-23a2-484b-9c75-1635fcd61fe3;\n style a15e1214-23a2-484b-9c75-1635fcd61fe3 stroke:#2c3143,stroke-width:4px;\n 7806088d-f657-4389-9d54-6a3bdfa233b2[\"Output\\nBUSCO: list of missing ortholog genes\"];\n 5 --> 7806088d-f657-4389-9d54-6a3bdfa233b2;\n style 7806088d-f657-4389-9d54-6a3bdfa233b2 stroke:#2c3143,stroke-width:4px;\n 6a27d0f6-d83c-42b1-a7af-5f99ba5debe2[\"Output\\nBUSCO: full table\"];\n 5 --> 6a27d0f6-d83c-42b1-a7af-5f99ba5debe2;\n style 6a27d0f6-d83c-42b1-a7af-5f99ba5debe2 stroke:#2c3143,stroke-width:4px;\n 6[\"Train Augustus\"];\n 2 -->|output| 6;\n 3 -->|output_gff| 6;\n 7[\"Genome annotation statistics\"];\n 3 -->|output_gff| 7;\n 2 -->|output| 7;\n 2bed9aa6-5f22-494d-9caa-f0ece8464f15[\"Output\\nGenome annotation statistics: graphs (first round)\"];\n 7 --> 2bed9aa6-5f22-494d-9caa-f0ece8464f15;\n style 2bed9aa6-5f22-494d-9caa-f0ece8464f15 stroke:#2c3143,stroke-width:4px;\n 71d41ce2-e90d-435b-9b64-ab4320524770[\"Output\\nGenome annotation statistics: summary (first round)\"];\n 7 --> 71d41ce2-e90d-435b-9b64-ab4320524770;\n style 71d41ce2-e90d-435b-9b64-ab4320524770 stroke:#2c3143,stroke-width:4px;\n 8[\"gffread\"];\n 3 -->|output_gff| 8;\n 2 -->|output| 8;\n 9[\"Train SNAP\"];\n 2 -->|output| 9;\n 3 -->|output_gff| 9;\n 10[\"Busco\"];\n 8 -->|output_exons| 10;\n a79ab19d-9802-42ca-8ffa-8c197a16c8b8[\"Output\\nBusco summary first round\"];\n 10 --> a79ab19d-9802-42ca-8ffa-8c197a16c8b8;\n style a79ab19d-9802-42ca-8ffa-8c197a16c8b8 stroke:#2c3143,stroke-width:4px;\n 11[\"Maker\"];\n 6 -->|output_tar| 11;\n 9 -->|output| 11;\n 2 -->|output| 11;\n 3 -->|output_full| 11;\n 12[\"gffread\"];\n 11 -->|output_gff| 12;\n 2 -->|output| 12;\n 13[\"Train Augustus\"];\n 2 -->|output| 13;\n 11 -->|output_gff| 13;\n 42d5aadd-3a8c-441d-913f-ee8745a99b0b[\"Output\\nAugus: trained model\"];\n 13 --> 42d5aadd-3a8c-441d-913f-ee8745a99b0b;\n style 42d5aadd-3a8c-441d-913f-ee8745a99b0b stroke:#2c3143,stroke-width:4px;\n 14[\"Genome annotation statistics\"];\n 11 -->|output_gff| 14;\n 2 -->|output| 14;\n 9def2c6e-fd3c-4969-b5d4-d13c187c1144[\"Output\\nGenome annotation statistics: graphs (second round)\"];\n 14 --> 9def2c6e-fd3c-4969-b5d4-d13c187c1144;\n style 9def2c6e-fd3c-4969-b5d4-d13c187c1144 stroke:#2c3143,stroke-width:4px;\n 61f7a150-a4a2-46b1-89f5-2170d466656e[\"Output\\nGenome annotation statistics: summary (second round)\"];\n 14 --> 61f7a150-a4a2-46b1-89f5-2170d466656e;\n style 61f7a150-a4a2-46b1-89f5-2170d466656e stroke:#2c3143,stroke-width:4px;\n 15[\"Train SNAP\"];\n 2 -->|output| 15;\n 11 -->|output_gff| 15;\n 458887f7-0661-4bde-ab1a-875aac6caed8[\"Output\\nSNAP: trained model\"];\n 15 --> 458887f7-0661-4bde-ab1a-875aac6caed8;\n style 458887f7-0661-4bde-ab1a-875aac6caed8 stroke:#2c3143,stroke-width:4px;\n 16[\"Busco\"];\n 12 -->|output_exons| 16;\n f52b1b41-0ea5-4384-bd43-4927273c909c[\"Output\\nBusco summary second round\"];\n 16 --> f52b1b41-0ea5-4384-bd43-4927273c909c;\n style f52b1b41-0ea5-4384-bd43-4927273c909c stroke:#2c3143,stroke-width:4px;\n 17[\"Maker\"];\n 13 -->|output_tar| 17;\n 15 -->|output| 17;\n 2 -->|output| 17;\n 11 -->|output_full| 17;\n 18[\"Map annotation ids\"];\n 17 -->|output_gff| 18;\n 63575dcf-54d1-4cb1-b5fb-a2810b4da807[\"Output\\nMap annotation ids: renamed GFF\"];\n 18 --> 63575dcf-54d1-4cb1-b5fb-a2810b4da807;\n style 63575dcf-54d1-4cb1-b5fb-a2810b4da807 stroke:#2c3143,stroke-width:4px;\n 19[\"JBrowse\"];\n 2 -->|output| 19;\n 3 -->|output_gff| 19;\n 18 -->|renamed| 19;\n 11 -->|output_gff| 19;\n 3 -->|output_evidences| 19;\n 11 -->|output_evidences| 19;\n 17 -->|output_evidences| 19;\n 5a38ae83-e177-45f3-aa9f-4c1844584fa5[\"Output\\nJBrowse: report\"];\n 19 --> 5a38ae83-e177-45f3-aa9f-4c1844584fa5;\n style 5a38ae83-e177-45f3-aa9f-4c1844584fa5 stroke:#2c3143,stroke-width:4px;\n 20[\"gffread\"];\n 18 -->|renamed| 20;\n 2 -->|output| 20;\n 52fcac23-b612-4377-a19a-2e3b667a23e3[\"Output\\nGffread: exons\"];\n 20 --> 52fcac23-b612-4377-a19a-2e3b667a23e3;\n style 52fcac23-b612-4377-a19a-2e3b667a23e3 stroke:#2c3143,stroke-width:4px;\n 5c47c25e-0ddb-4ade-a5d0-873ff4add28c[\"Output\\nGffread: translation of cds\"];\n 20 --> 5c47c25e-0ddb-4ade-a5d0-873ff4add28c;\n style 5c47c25e-0ddb-4ade-a5d0-873ff4add28c stroke:#2c3143,stroke-width:4px;\n 84d9183b-9589-464b-9f82-4dae7f87133e[\"Output\\nGffread: cds\"];\n 20 --> 84d9183b-9589-464b-9f82-4dae7f87133e;\n style 84d9183b-9589-464b-9f82-4dae7f87133e stroke:#2c3143,stroke-width:4px;\n 21[\"Genome annotation statistics\"];\n 18 -->|renamed| 21;\n 2 -->|output| 21;\n f5d4a6bf-1ff8-435a-8084-7921e25bff3a[\"Output\\nGenome annotation statistics: graphs (third round)\"];\n 21 --> f5d4a6bf-1ff8-435a-8084-7921e25bff3a;\n style f5d4a6bf-1ff8-435a-8084-7921e25bff3a stroke:#2c3143,stroke-width:4px;\n 5a863bd4-10f2-4ac8-81b6-ce8b7f663e42[\"Output\\nGenome annotation statistics: summary (third round)\"];\n 21 --> 5a863bd4-10f2-4ac8-81b6-ce8b7f663e42;\n style 5a863bd4-10f2-4ac8-81b6-ce8b7f663e42 stroke:#2c3143,stroke-width:4px;\n 22[\"Busco\"];\n 20 -->|output_exons| 22;\n 0bc78676-eecc-4168-894d-34c4ca07cdf3[\"Output\\nBusco summary final round\"];\n 22 --> 0bc78676-eecc-4168-894d-34c4ca07cdf3;\n style 0bc78676-eecc-4168-894d-34c4ca07cdf3 stroke:#2c3143,stroke-width:4px;", + "modified": "2024-06-24 00:06:31 +0000", + "name": "Genome annotation with Maker ", + "path": "topics/genome-annotation/tutorials/annotation-with-maker/workflows/main_workflow.ga", + "test_results": null, + "tests": false, + "title": "Genome annotation with Maker ", + "trs_endpoint": "https://training.galaxyproject.org/training-material/api/ga4gh/trs/v2/tools/genome-annotation-annotation-with-maker/versions/main_workflow", + "url": "https://training.galaxyproject.org/training-material/topics/genome-annotation/tutorials/annotation-with-maker/workflows/main_workflow.ga", + "wfid": "genome-annotation-annotation-with-maker", + "wfname": "main_workflow", + "workflow": "main_workflow.ga" + } + ], + "zenodo_link": "https://doi.org/10.5281/zenodo.4406623" + }, + { + "abbreviations": { + "OGS": "Official Gene Set" + }, + "admin_install": { + "install_repository_dependencies": true, + "install_resolver_dependencies": true, + "install_tool_dependencies": true, + "tools": [ + { + "name": "apollo_create_or_update", + "owner": "gga", + "revisions": "4abaab60f9e1", + "tool_panel_section_label": "Apollo", + "tool_shed_url": "https://toolshed.g2.bx.psu.edu/" + }, + { + "name": "apollo_iframe", + "owner": "gga", + 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"topics/microbiome/tutorials/pathogen-detection-from-nanopore-foodborne-data/workflows/allele_based_pathogen_identification.ga", + "test_results": null, + "tests": true, + "title": "Allele-based Pathogen Identification", + "trs_endpoint": "https://training.galaxyproject.org/training-material/api/ga4gh/trs/v2/tools/microbiome-pathogen-detection-from-nanopore-foodborne-data/versions/allele_based_pathogen_identification", + "url": "https://training.galaxyproject.org/training-material/topics/microbiome/tutorials/pathogen-detection-from-nanopore-foodborne-data/workflows/allele_based_pathogen_identification.ga", + "wfid": "microbiome-pathogen-detection-from-nanopore-foodborne-data", + "wfname": "allele_based_pathogen_identification", + "workflow": "allele_based_pathogen_identification.ga" + }, + { + "creators": [ + { + "class": "Person", + "identifier": "0000-0001-9047-4215", + "name": "Engy Nasr", + "url": "https://orcid.org/0000-0001-9047-4215" + }, + { + "class": "Person", + "identifier": "0000-0001-9852-1987", + "name": "B\u00e9r\u00e9nice Batut", + "url": "https://orcid.org/0000-0001-9852-1987" + }, + { + "class": "Person", + "identifier": "0000-0003-2982-388X", + "name": "Paul Zierep" + } + ], + "license": "MIT", + "mermaid": "flowchart TD\n 0[\"\u2139\ufe0f Input Collection\\ncollection_of_preprocessed_samples\"];\n style 0 stroke:#2c3143,stroke-width:4px;\n 1[\"Extract element identifiers\"];\n 0 -->|output| 1;\n d82a93c6-23a9-4f85-879c-ecd759a31087[\"Output\\nextracted_samples_IDs\"];\n 1 --> d82a93c6-23a9-4f85-879c-ecd759a31087;\n style d82a93c6-23a9-4f85-879c-ecd759a31087 stroke:#2c3143,stroke-width:4px;\n 2[\"Build list\"];\n 0 -->|output| 2;\n f5b5b256-8ddf-4da8-8111-b1f6d3025a0d[\"Output\\nlist_of_lists_of_preprocessed_samples\"];\n 2 --> f5b5b256-8ddf-4da8-8111-b1f6d3025a0d;\n style f5b5b256-8ddf-4da8-8111-b1f6d3025a0d stroke:#2c3143,stroke-width:4px;\n 3[\"Split file\"];\n 1 -->|output| 3;\n eb5317bd-4bad-4cad-9219-3ac379221d6e[\"Output\\nsplitted_extracted_samples_IDs\"];\n 3 --> eb5317bd-4bad-4cad-9219-3ac379221d6e;\n style eb5317bd-4bad-4cad-9219-3ac379221d6e stroke:#2c3143,stroke-width:4px;\n 4[\"Flye\"];\n 2 -->|output| 4;\n ff0d8c7b-2ac7-4c6d-a110-f71418dad938[\"Output\\nflye_consensus_fasta\"];\n 4 --> ff0d8c7b-2ac7-4c6d-a110-f71418dad938;\n style ff0d8c7b-2ac7-4c6d-a110-f71418dad938 stroke:#2c3143,stroke-width:4px;\n 2385436f-fbe8-4d77-a40b-27a097d02941[\"Output\\nflye_assembly_graph\"];\n 4 --> 2385436f-fbe8-4d77-a40b-27a097d02941;\n style 2385436f-fbe8-4d77-a40b-27a097d02941 stroke:#2c3143,stroke-width:4px;\n 0e42ce51-c46b-45d8-baa4-45c0e98ac712[\"Output\\nflye_assembly_gfa\"];\n 4 --> 0e42ce51-c46b-45d8-baa4-45c0e98ac712;\n style 0e42ce51-c46b-45d8-baa4-45c0e98ac712 stroke:#2c3143,stroke-width:4px;\n 3f297aed-7cee-4999-bbf4-69d84de6b64f[\"Output\\nflye_assembly_info_tabular\"];\n 4 --> 3f297aed-7cee-4999-bbf4-69d84de6b64f;\n style 3f297aed-7cee-4999-bbf4-69d84de6b64f stroke:#2c3143,stroke-width:4px;\n 5[\"Parse parameter value\"];\n 3 -->|list_output_txt| 5;\n 3608e170-c462-42a2-8003-f3f65baa3834[\"Output\\nparsed_extracted_samples_IDs_to_text\"];\n 5 --> 3608e170-c462-42a2-8003-f3f65baa3834;\n style 3608e170-c462-42a2-8003-f3f65baa3834 stroke:#2c3143,stroke-width:4px;\n 6[\"medaka consensus pipeline\"];\n 4 -->|consensus| 6;\n 0 -->|output| 6;\n 9e5ad6ec-b408-4132-ba07-dec9fa626923[\"Output\\nmedaka_gaps_in_draft_bed_file\"];\n 6 --> 9e5ad6ec-b408-4132-ba07-dec9fa626923;\n style 9e5ad6ec-b408-4132-ba07-dec9fa626923 stroke:#2c3143,stroke-width:4px;\n fcbd3e3f-2e93-4798-b696-dad7db9f2efd[\"Output\\nmedaka_log_file\"];\n 6 --> fcbd3e3f-2e93-4798-b696-dad7db9f2efd;\n style fcbd3e3f-2e93-4798-b696-dad7db9f2efd stroke:#2c3143,stroke-width:4px;\n 60656aac-ad2a-4c9b-9a68-b9fb18ae5595[\"Output\\nmedaka_propability_h5_file\"];\n 6 --> 60656aac-ad2a-4c9b-9a68-b9fb18ae5595;\n style 60656aac-ad2a-4c9b-9a68-b9fb18ae5595 stroke:#2c3143,stroke-width:4px;\n c790d434-8e78-4df0-a0d4-8f9da0692158[\"Output\\nmedaka_calls_of_draft_bam_file\"];\n 6 --> c790d434-8e78-4df0-a0d4-8f9da0692158;\n style c790d434-8e78-4df0-a0d4-8f9da0692158 stroke:#2c3143,stroke-width:4px;\n df361e19-b6d1-405b-96cc-b48c1ab7c604[\"Output\\nsample_all_contigs\"];\n 6 --> df361e19-b6d1-405b-96cc-b48c1ab7c604;\n style df361e19-b6d1-405b-96cc-b48c1ab7c604 stroke:#2c3143,stroke-width:4px;\n 7[\"Bandage Image\"];\n 4 -->|assembly_gfa| 7;\n 9612a851-1f94-4d54-b001-5d082bcc9055[\"Output\\nbandage_assembly_graph_image\"];\n 7 --> 9612a851-1f94-4d54-b001-5d082bcc9055;\n style 9612a851-1f94-4d54-b001-5d082bcc9055 stroke:#2c3143,stroke-width:4px;\n 8[\"Compose text parameter value\"];\n 5 -->|text_param| 8;\n 9[\"FASTA-to-Tabular\"];\n 6 -->|out_consensus| 9;\n de06c362-5b0a-4c29-956e-fbf02539789a[\"Output\\nsample_specific_contigs_tabular_file_preparation\"];\n 9 --> de06c362-5b0a-4c29-956e-fbf02539789a;\n style de06c362-5b0a-4c29-956e-fbf02539789a stroke:#2c3143,stroke-width:4px;\n 10[\"ABRicate\"];\n 6 -->|out_consensus| 10;\n 8f227fc7-2d92-4c6f-af64-e841c1315b4f[\"Output\\nabricate_with_vfdb_to_identify_genes_with_VFs\"];\n 10 --> 8f227fc7-2d92-4c6f-af64-e841c1315b4f;\n style 8f227fc7-2d92-4c6f-af64-e841c1315b4f stroke:#2c3143,stroke-width:4px;\n 11[\"ABRicate\"];\n 6 -->|out_consensus| 11;\n fa021d98-d885-4834-ac62-f30d5792260e[\"Output\\nabricate_report_using_ncbi_database_to_indentify_amr\"];\n 11 --> fa021d98-d885-4834-ac62-f30d5792260e;\n style fa021d98-d885-4834-ac62-f30d5792260e stroke:#2c3143,stroke-width:4px;\n 12[\"Replace\"];\n 8 -->|out1| 12;\n 9 -->|output| 12;\n 174cb2c3-ed28-453c-8afb-85150e0b51ad[\"Output\\nsample_specific_contigs_tabular_file\"];\n 12 --> 174cb2c3-ed28-453c-8afb-85150e0b51ad;\n style 174cb2c3-ed28-453c-8afb-85150e0b51ad stroke:#2c3143,stroke-width:4px;\n 13[\"Replace\"];\n 8 -->|out1| 13;\n 10 -->|report| 13;\n 19e56e00-6eef-4a4e-9cc6-d93dbf9420ad[\"Output\\nvfs\"];\n 13 --> 19e56e00-6eef-4a4e-9cc6-d93dbf9420ad;\n style 19e56e00-6eef-4a4e-9cc6-d93dbf9420ad stroke:#2c3143,stroke-width:4px;\n 14[\"Replace\"];\n 8 -->|out1| 14;\n 11 -->|report| 14;\n a8eb74a5-362b-4f0f-a944-672e27981a41[\"Output\\namrs\"];\n 14 --> a8eb74a5-362b-4f0f-a944-672e27981a41;\n style a8eb74a5-362b-4f0f-a944-672e27981a41 stroke:#2c3143,stroke-width:4px;\n 15[\"Tabular-to-FASTA\"];\n 12 -->|outfile| 15;\n a5bb1cbe-1bde-49a1-ad86-6d57b1319fbe[\"Output\\ncontigs\"];\n 15 --> a5bb1cbe-1bde-49a1-ad86-6d57b1319fbe;\n style a5bb1cbe-1bde-49a1-ad86-6d57b1319fbe stroke:#2c3143,stroke-width:4px;", + "modified": "2024-06-24 00:06:32 +0000", + "name": "Gene-based Pathogen Identification", + "path": "topics/microbiome/tutorials/pathogen-detection-from-nanopore-foodborne-data/workflows/gene_based_pathogen_identification.ga", + "test_results": null, + "tests": true, + "title": "Gene-based Pathogen Identification", + "trs_endpoint": "https://training.galaxyproject.org/training-material/api/ga4gh/trs/v2/tools/microbiome-pathogen-detection-from-nanopore-foodborne-data/versions/gene_based_pathogen_identification", + "url": "https://training.galaxyproject.org/training-material/topics/microbiome/tutorials/pathogen-detection-from-nanopore-foodborne-data/workflows/gene_based_pathogen_identification.ga", + "wfid": "microbiome-pathogen-detection-from-nanopore-foodborne-data", + "wfname": "gene_based_pathogen_identification", + "workflow": "gene_based_pathogen_identification.ga" + }, + { + "creators": [ + { + "class": "Person", + "identifier": "0000-0001-9852-1987", + "name": "B\u00e9r\u00e9nice Batut", + "url": "https://orcid.org/0000-0001-9852-1987" + }, + { + "class": "Person", + "identifier": "0000-0001-9047-4215", + "name": "Engy Nasr", + "url": "https://orcid.org/0000-0001-9047-4215" + }, + { + "class": "Person", + "identifier": "0000-0003-2982-388X", + "name": "Paul Zierep" + } + ], + "license": "MIT", + "mermaid": "flowchart TD\n 0[\"\u2139\ufe0f Input Parameter\\nsamples_profile\"];\n style 0 fill:#ded,stroke:#393,stroke-width:4px;\n 1[\"\u2139\ufe0f Input Collection\\ncollection_of_all_samples\"];\n style 1 stroke:#2c3143,stroke-width:4px;\n 2[\"Porechop\"];\n 1 -->|output| 2;\n 34ea26db-11cb-41ee-85c3-75af8a53a2c0[\"Output\\nporechop_output_trimmed_reads\"];\n 2 --> 34ea26db-11cb-41ee-85c3-75af8a53a2c0;\n style 34ea26db-11cb-41ee-85c3-75af8a53a2c0 stroke:#2c3143,stroke-width:4px;\n 3[\"NanoPlot\"];\n 1 -->|output| 3;\n 15ecf5b1-e0eb-405a-ac3a-359feb66d4cd[\"Output\\nnanoplot_qc_on_reads_before_preprocessing_nanostats\"];\n 3 --> 15ecf5b1-e0eb-405a-ac3a-359feb66d4cd;\n style 15ecf5b1-e0eb-405a-ac3a-359feb66d4cd stroke:#2c3143,stroke-width:4px;\n 304110f9-60d0-4ba2-8b3b-fae0e2a49554[\"Output\\nnanoplot_on_reads_before_preprocessing_nanostats_post_filtering\"];\n 3 --> 304110f9-60d0-4ba2-8b3b-fae0e2a49554;\n style 304110f9-60d0-4ba2-8b3b-fae0e2a49554 stroke:#2c3143,stroke-width:4px;\n f2bd0a1f-cd60-4a36-a7d0-8025cc19ea2e[\"Output\\nnanoplot_qc_on_reads_before_preprocessing_html_report\"];\n 3 --> f2bd0a1f-cd60-4a36-a7d0-8025cc19ea2e;\n style f2bd0a1f-cd60-4a36-a7d0-8025cc19ea2e stroke:#2c3143,stroke-width:4px;\n 4[\"FastQC\"];\n 1 -->|output| 4;\n d0a64624-05d0-4068-835b-a025fc011760[\"Output\\nfastqc_quality_check_before_preprocessing_html_file\"];\n 4 --> d0a64624-05d0-4068-835b-a025fc011760;\n style d0a64624-05d0-4068-835b-a025fc011760 stroke:#2c3143,stroke-width:4px;\n e61fef5d-1bc8-4c8e-be6a-f74e210e9920[\"Output\\nfastqc_quality_check_before_preprocessing_text_file\"];\n 4 --> e61fef5d-1bc8-4c8e-be6a-f74e210e9920;\n style e61fef5d-1bc8-4c8e-be6a-f74e210e9920 stroke:#2c3143,stroke-width:4px;\n 5[\"fastp\"];\n 2 -->|outfile| 5;\n a2219483-50b7-4aed-98dd-333ad2e12eb8[\"Output\\nnanopore_sequenced_reads_processed_with_fastp_after_host_removal\"];\n 5 --> a2219483-50b7-4aed-98dd-333ad2e12eb8;\n style a2219483-50b7-4aed-98dd-333ad2e12eb8 stroke:#2c3143,stroke-width:4px;\n 2a9a8b4d-458b-40e7-9a21-fb7108d5bbe4[\"Output\\nnanopore_sequenced_reads_processed_with_fastp_after_host_removal_html_report\"];\n 5 --> 2a9a8b4d-458b-40e7-9a21-fb7108d5bbe4;\n style 2a9a8b4d-458b-40e7-9a21-fb7108d5bbe4 stroke:#2c3143,stroke-width:4px;\n 6[\"MultiQC\"];\n 4 -->|text_file| 6;\n ebffe782-a56c-431f-8af4-c0cb8d7a02fc[\"Output\\nmultiQC_stats_before_preprocessing\"];\n 6 --> ebffe782-a56c-431f-8af4-c0cb8d7a02fc;\n style ebffe782-a56c-431f-8af4-c0cb8d7a02fc stroke:#2c3143,stroke-width:4px;\n 0f92196d-047d-4918-819d-c0ff7cd3ae85[\"Output\\nmultiQC_html_report_before_preprocessing\"];\n 6 --> 0f92196d-047d-4918-819d-c0ff7cd3ae85;\n style 0f92196d-047d-4918-819d-c0ff7cd3ae85 stroke:#2c3143,stroke-width:4px;\n 7[\"Map with minimap2\"];\n 0 -->|output| 7;\n 5 -->|out1| 7;\n 9d7bb3b7-09a1-401f-a132-bb35a53375ea[\"Output\\nbam_map_to_host\"];\n 7 --> 9d7bb3b7-09a1-401f-a132-bb35a53375ea;\n style 9d7bb3b7-09a1-401f-a132-bb35a53375ea stroke:#2c3143,stroke-width:4px;\n 8[\"NanoPlot\"];\n 5 -->|out1| 8;\n b5899290-4c57-4662-ad22-860654652ade[\"Output\\nnanoplot_qc_on_reads_after_preprocessing_html_report\"];\n 8 --> b5899290-4c57-4662-ad22-860654652ade;\n style b5899290-4c57-4662-ad22-860654652ade stroke:#2c3143,stroke-width:4px;\n 949bfdf5-3d79-4dad-bdd8-c3a25e6af4cf[\"Output\\nnanoplot_on_reads_after_preprocessing_nanostats_post_filtering\"];\n 8 --> 949bfdf5-3d79-4dad-bdd8-c3a25e6af4cf;\n style 949bfdf5-3d79-4dad-bdd8-c3a25e6af4cf stroke:#2c3143,stroke-width:4px;\n 42db7f93-919e-4bbb-81a1-06411a9da410[\"Output\\nnanoplot_qc_on_reads_after_preprocessing_nanostats\"];\n 8 --> 42db7f93-919e-4bbb-81a1-06411a9da410;\n style 42db7f93-919e-4bbb-81a1-06411a9da410 stroke:#2c3143,stroke-width:4px;\n 9[\"FastQC\"];\n 5 -->|out1| 9;\n 09306471-afa0-4106-9cc7-259b93dfc862[\"Output\\nfastqc_quality_check_after_preprocessing_text_file\"];\n 9 --> 09306471-afa0-4106-9cc7-259b93dfc862;\n style 09306471-afa0-4106-9cc7-259b93dfc862 stroke:#2c3143,stroke-width:4px;\n 084f982f-20f1-457e-8012-91ebbb85633d[\"Output\\nfastqc_quality_check_after_preprocessing_html_file\"];\n 9 --> 084f982f-20f1-457e-8012-91ebbb85633d;\n style 084f982f-20f1-457e-8012-91ebbb85633d stroke:#2c3143,stroke-width:4px;\n 10[\"Split BAM by reads mapping status\"];\n 7 -->|alignment_output| 10;\n 14a53fe2-f296-43aa-86b7-243278c1050c[\"Output\\nnon_host_sequences_bam\"];\n 10 --> 14a53fe2-f296-43aa-86b7-243278c1050c;\n style 14a53fe2-f296-43aa-86b7-243278c1050c stroke:#2c3143,stroke-width:4px;\n 3b1e626f-6bc1-484c-be01-366534361b73[\"Output\\nhost_sequences_bam\"];\n 10 --> 3b1e626f-6bc1-484c-be01-366534361b73;\n style 3b1e626f-6bc1-484c-be01-366534361b73 stroke:#2c3143,stroke-width:4px;\n 11[\"Select\"];\n 9 -->|text_file| 11;\n a809853b-119f-44d2-986b-8d2006439fbe[\"Output\\ntotal_sequences_before_hosts_sequences_removal\"];\n 11 --> a809853b-119f-44d2-986b-8d2006439fbe;\n style a809853b-119f-44d2-986b-8d2006439fbe stroke:#2c3143,stroke-width:4px;\n 12[\"Samtools fastx\"];\n 10 -->|mapped| 12;\n 10d4eaec-81d8-444e-8075-7b77a1fb6870[\"Output\\nhost_sequences_fastq\"];\n 12 --> 10d4eaec-81d8-444e-8075-7b77a1fb6870;\n style 10d4eaec-81d8-444e-8075-7b77a1fb6870 stroke:#2c3143,stroke-width:4px;\n 13[\"Samtools fastx\"];\n 10 -->|unmapped| 13;\n 0c2dd74d-ac4f-45cf-839c-50386a7ece28[\"Output\\nnon_host_sequences_fastq\"];\n 13 --> 0c2dd74d-ac4f-45cf-839c-50386a7ece28;\n style 0c2dd74d-ac4f-45cf-839c-50386a7ece28 stroke:#2c3143,stroke-width:4px;\n 14[\"Collapse Collection\"];\n 11 -->|out_file1| 14;\n 15[\"Filter failed datasets\"];\n 12 -->|output| 15;\n 16[\"Kraken2\"];\n 13 -->|output| 16;\n 203d303e-8f3a-4242-971f-b345842ebdb8[\"Output\\nkraken2_with_kalamri_database_output\"];\n 16 --> 203d303e-8f3a-4242-971f-b345842ebdb8;\n style 203d303e-8f3a-4242-971f-b345842ebdb8 stroke:#2c3143,stroke-width:4px;\n 843afd4d-23a8-46e7-b945-8b67dd7ae341[\"Output\\nkraken2_with_kalamri_database_report\"];\n 16 --> 843afd4d-23a8-46e7-b945-8b67dd7ae341;\n style 843afd4d-23a8-46e7-b945-8b67dd7ae341 stroke:#2c3143,stroke-width:4px;\n 17[\"Cut\"];\n 14 -->|output| 17;\n d07be9f1-d250-4008-91ee-59a68521eb56[\"Output\\nquality_retained_all_reads\"];\n 17 --> d07be9f1-d250-4008-91ee-59a68521eb56;\n style d07be9f1-d250-4008-91ee-59a68521eb56 stroke:#2c3143,stroke-width:4px;\n 18[\"FastQC\"];\n 15 -->|output| 18;\n b0ee6e31-0eb1-437d-8c04-fc3640b9a0b7[\"Output\\nhosts_qc_text_file\"];\n 18 --> b0ee6e31-0eb1-437d-8c04-fc3640b9a0b7;\n style b0ee6e31-0eb1-437d-8c04-fc3640b9a0b7 stroke:#2c3143,stroke-width:4px;\n b72ff57b-0921-43bf-a817-6cd444c8f3cb[\"Output\\nhosts_qc_html\"];\n 18 --> b72ff57b-0921-43bf-a817-6cd444c8f3cb;\n style b72ff57b-0921-43bf-a817-6cd444c8f3cb stroke:#2c3143,stroke-width:4px;\n 19[\"Krakentools: Extract Kraken Reads By ID\"];\n 5 -->|out1| 19;\n 16 -->|report_output| 19;\n 16 -->|output| 19;\n 57e3b725-8e13-40b2-9acc-31fd56ebc80a[\"Output\\ncollection_of_preprocessed_samples\"];\n 19 --> 57e3b725-8e13-40b2-9acc-31fd56ebc80a;\n style 57e3b725-8e13-40b2-9acc-31fd56ebc80a stroke:#2c3143,stroke-width:4px;\n 20[\"Select\"];\n 18 -->|text_file| 20;\n 3ba35c71-32f0-4741-98d4-ea8522e27500[\"Output\\ntotal_sequences_after_hosts_sequences_removal\"];\n 20 --> 3ba35c71-32f0-4741-98d4-ea8522e27500;\n style 3ba35c71-32f0-4741-98d4-ea8522e27500 stroke:#2c3143,stroke-width:4px;\n 21[\"Collapse Collection\"];\n 20 -->|out_file1| 21;\n 22[\"Cut\"];\n 21 -->|output| 22;\n cef36c68-4549-4fd6-b7c8-71fb21df012f[\"Output\\nquality_retained_hosts_reads\"];\n 22 --> cef36c68-4549-4fd6-b7c8-71fb21df012f;\n style cef36c68-4549-4fd6-b7c8-71fb21df012f stroke:#2c3143,stroke-width:4px;\n 23[\"Column join\"];\n 17 -->|out_file1| 23;\n 22 -->|out_file1| 23;\n 24[\"Compute\"];\n 23 -->|tabular_output| 24;\n 25[\"Column Regex Find And Replace\"];\n 24 -->|out_file1| 25;\n 470892ee-dab9-48d7-ad97-45dbd52afaa7[\"Output\\nremoved_hosts_percentage_tabular\"];\n 25 --> 470892ee-dab9-48d7-ad97-45dbd52afaa7;\n style 470892ee-dab9-48d7-ad97-45dbd52afaa7 stroke:#2c3143,stroke-width:4px;\n 26[\"MultiQC\"];\n 9 -->|text_file| 26;\n 25 -->|out_file1| 26;\n 0b1b5a73-36ee-42a2-a220-1ced6ec7378b[\"Output\\nmultiQC_html_report_after_preprocessing\"];\n 26 --> 0b1b5a73-36ee-42a2-a220-1ced6ec7378b;\n style 0b1b5a73-36ee-42a2-a220-1ced6ec7378b stroke:#2c3143,stroke-width:4px;\n 13cbf6c7-6954-4458-aa66-a5b020c63822[\"Output\\nmultiQC_stats_after_preprocessing\"];\n 26 --> 13cbf6c7-6954-4458-aa66-a5b020c63822;\n style 13cbf6c7-6954-4458-aa66-a5b020c63822 stroke:#2c3143,stroke-width:4px;", + "modified": "2024-06-24 00:06:32 +0000", + "name": "Nanopore Preprocessing", + "path": "topics/microbiome/tutorials/pathogen-detection-from-nanopore-foodborne-data/workflows/nanopore_preprocessing.ga", + "test_results": null, + "tests": true, + "title": "Nanopore Preprocessing", + "trs_endpoint": "https://training.galaxyproject.org/training-material/api/ga4gh/trs/v2/tools/microbiome-pathogen-detection-from-nanopore-foodborne-data/versions/nanopore_preprocessing", + "url": "https://training.galaxyproject.org/training-material/topics/microbiome/tutorials/pathogen-detection-from-nanopore-foodborne-data/workflows/nanopore_preprocessing.ga", + "wfid": "microbiome-pathogen-detection-from-nanopore-foodborne-data", + "wfname": "nanopore_preprocessing", + "workflow": "nanopore_preprocessing.ga" + }, + { + "creators": [ + { + "class": "Person", + "identifier": "0000-0001-9047-4215", + "name": "Engy Nasr", + "url": "https://orcid.org/0000-0001-9047-4215" + }, + { + "class": "Person", + "identifier": "0000-0001-9852-1987", + "name": "B\u00e9r\u00e9nice Batut", + "url": "https://orcid.org/0000-0001-9852-1987" + }, + { + "class": "Person", + "identifier": "0000-0003-2982-388X", + "name": "Paul Zierep" + } + ], + "license": "MIT", + "mermaid": "flowchart TD\n 0[\"\u2139\ufe0f Input Collection\\namr_identified_by_ncbi\"];\n style 0 stroke:#2c3143,stroke-width:4px;\n 1[\"\u2139\ufe0f Input Collection\\nvfs_of_genes_identified_by_vfdb\"];\n style 1 stroke:#2c3143,stroke-width:4px;\n 2[\"\u2139\ufe0f Input Dataset\\nmetadata\"];\n style 2 stroke:#2c3143,stroke-width:4px;\n 3[\"\u2139\ufe0f Input Dataset\\nremoved_hosts_percentage_tabular\"];\n style 3 stroke:#2c3143,stroke-width:4px;\n 4[\"\u2139\ufe0f Input Dataset\\nmapping_mean_depth_per_sample\"];\n style 4 stroke:#2c3143,stroke-width:4px;\n 5[\"\u2139\ufe0f Input Collection\\namrs\"];\n style 5 stroke:#2c3143,stroke-width:4px;\n 6[\"\u2139\ufe0f Input Dataset\\nmapping_coverage_percentage_per_sample\"];\n style 6 stroke:#2c3143,stroke-width:4px;\n 7[\"\u2139\ufe0f Input Dataset\\nnumber_of_variants_per_sample\"];\n style 7 stroke:#2c3143,stroke-width:4px;\n 8[\"\u2139\ufe0f Input Collection\\ncontigs\"];\n style 8 stroke:#2c3143,stroke-width:4px;\n 9[\"\u2139\ufe0f Input Collection\\nvfs\"];\n style 9 stroke:#2c3143,stroke-width:4px;\n 10[\"Filter failed datasets\"];\n 0 -->|output| 10;\n 11[\"Filter failed datasets\"];\n 1 -->|output| 11;\n 12[\"Bar chart\"];\n 3 -->|output| 12;\n 0961166e-38b6-44e3-93db-100d821b0070[\"Output\\nnumber_of_reads_before_host_removal_and_number_of_host_reads_found_per_sample_fig\"];\n 12 --> 0961166e-38b6-44e3-93db-100d821b0070;\n style 0961166e-38b6-44e3-93db-100d821b0070 stroke:#2c3143,stroke-width:4px;\n 13[\"Bar chart\"];\n 3 -->|output| 13;\n 548975e5-4619-49fc-9e95-4d7f4d761dfd[\"Output\\nremoved_host_percentage_fig\"];\n 13 --> 548975e5-4619-49fc-9e95-4d7f4d761dfd;\n style 548975e5-4619-49fc-9e95-4d7f4d761dfd stroke:#2c3143,stroke-width:4px;\n 14[\"Bar chart\"];\n 4 -->|output| 14;\n a71ebb67-1154-4f25-a62d-e8fa2b839e2e[\"Output\\nmapping_mean_depth_per_sample_fig\"];\n 14 --> a71ebb67-1154-4f25-a62d-e8fa2b839e2e;\n style a71ebb67-1154-4f25-a62d-e8fa2b839e2e stroke:#2c3143,stroke-width:4px;\n 15[\"Filter failed datasets\"];\n 5 -->|output| 15;\n 16[\"Bar chart\"];\n 6 -->|output| 16;\n 2567360e-d39f-4316-84f5-77aaf8e8198c[\"Output\\nmapping_coverage_percentage_per_sample_fig\"];\n 16 --> 2567360e-d39f-4316-84f5-77aaf8e8198c;\n style 2567360e-d39f-4316-84f5-77aaf8e8198c stroke:#2c3143,stroke-width:4px;\n 17[\"Bar chart\"];\n 7 -->|output| 17;\n 68c76f06-d2ea-4280-9e6f-a7b4f1568389[\"Output\\nnumber_of_Variants_and_SNPs_indentified_fig\"];\n 17 --> 68c76f06-d2ea-4280-9e6f-a7b4f1568389;\n style 68c76f06-d2ea-4280-9e6f-a7b4f1568389 stroke:#2c3143,stroke-width:4px;\n 18[\"Filter failed datasets\"];\n 8 -->|output| 18;\n 19[\"Filter failed datasets\"];\n 9 -->|output| 19;\n 20[\"Remove beginning\"];\n 10 -->|output| 20;\n 21[\"Remove beginning\"];\n 11 -->|output| 21;\n 22[\"Remove beginning\"];\n 15 -->|output| 22;\n 23[\"Collapse Collection\"];\n 18 -->|output| 23;\n 86910e39-57bf-4a76-ac4c-739340fd2387[\"Output\\nall_samples_contigs_in_one_fasta_file\"];\n 23 --> 86910e39-57bf-4a76-ac4c-739340fd2387;\n style 86910e39-57bf-4a76-ac4c-739340fd2387 stroke:#2c3143,stroke-width:4px;\n 24[\"Collapse Collection\"];\n 19 -->|output| 24;\n 02b996c6-a912-4e4e-b3ec-49601faaa452[\"Output\\nall_vfs_in_one_tabular\"];\n 24 --> 02b996c6-a912-4e4e-b3ec-49601faaa452;\n style 02b996c6-a912-4e4e-b3ec-49601faaa452 stroke:#2c3143,stroke-width:4px;\n 25[\"Remove beginning\"];\n 19 -->|output| 25;\n 26[\"Count\"];\n 20 -->|out_file1| 26;\n 27[\"Count\"];\n 21 -->|out_file1| 27;\n 28[\"Group\"];\n 21 -->|out_file1| 28;\n 29[\"Unique\"];\n 22 -->|out_file1| 29;\n 30[\"Split by group\"];\n 24 -->|output| 30;\n 59f8cb09-424b-47b1-b94b-a612c2610cab[\"Output\\nsplit_by_group_collection\"];\n 30 --> 59f8cb09-424b-47b1-b94b-a612c2610cab;\n style 59f8cb09-424b-47b1-b94b-a612c2610cab stroke:#2c3143,stroke-width:4px;\n 31[\"Unique\"];\n 25 -->|out_file1| 31;\n 32[\"Cut\"];\n 26 -->|out_file1| 32;\n 33[\"Cut\"];\n 27 -->|out_file1| 33;\n 34[\"Filter empty datasets\"];\n 28 -->|out_file1| 34;\n 35[\"Cut\"];\n 29 -->|outfile| 35;\n 36[\"Cut\"];\n 30 -->|split_output| 36;\n eeb25a51-ea21-4a19-a196-55d5bd919b10[\"Output\\nadjusted_abricate_vfs_tabular_part1\"];\n 36 --> eeb25a51-ea21-4a19-a196-55d5bd919b10;\n style eeb25a51-ea21-4a19-a196-55d5bd919b10 stroke:#2c3143,stroke-width:4px;\n 37[\"Cut\"];\n 31 -->|outfile| 37;\n 38[\"Collapse Collection\"];\n 32 -->|out_file1| 38;\n 39[\"Collapse Collection\"];\n 33 -->|out_file1| 39;\n 40[\"Column join\"];\n 34 -->|output| 40;\n 41[\"bedtools getfasta\"];\n 23 -->|output| 41;\n 35 -->|out_file1| 41;\n 42[\"Remove beginning\"];\n 36 -->|out_file1| 42;\n aaaa4446-0817-4e5c-aa1b-9ec384f2a363[\"Output\\nadjusted_abricate_vfs_tabular_part2\"];\n 42 --> aaaa4446-0817-4e5c-aa1b-9ec384f2a363;\n style aaaa4446-0817-4e5c-aa1b-9ec384f2a363 stroke:#2c3143,stroke-width:4px;\n 43[\"bedtools getfasta\"];\n 23 -->|output| 43;\n 37 -->|out_file1| 43;\n 44[\"Column Regex Find And Replace\"];\n 38 -->|output| 44;\n 4809c36b-31ef-4664-8e4e-47f0f72152de[\"Output\\namrs_count\"];\n 44 --> 4809c36b-31ef-4664-8e4e-47f0f72152de;\n style 4809c36b-31ef-4664-8e4e-47f0f72152de stroke:#2c3143,stroke-width:4px;\n 45[\"Column Regex Find And Replace\"];\n 39 -->|output| 45;\n 87efc81d-4d84-4af3-831f-dfe033c59f78[\"Output\\nvfs_count\"];\n 45 --> 87efc81d-4d84-4af3-831f-dfe033c59f78;\n style 87efc81d-4d84-4af3-831f-dfe033c59f78 stroke:#2c3143,stroke-width:4px;\n 46[\"Column Regex Find And Replace\"];\n 40 -->|tabular_output| 46;\n f5c221e3-00ef-4834-9a5f-a94c97fd6764[\"Output\\nheatmap_table\"];\n 46 --> f5c221e3-00ef-4834-9a5f-a94c97fd6764;\n style f5c221e3-00ef-4834-9a5f-a94c97fd6764 stroke:#2c3143,stroke-width:4px;\n 47[\"Regex Find And Replace\"];\n 41 -->|output| 47;\n 48[\"bedtools getfasta\"];\n 23 -->|output| 48;\n 42 -->|out_file1| 48;\n 82ce2107-89a3-438c-95bb-dc871b5258b7[\"Output\\nfiltered_sequences_with_vfs_fasta\"];\n 48 --> 82ce2107-89a3-438c-95bb-dc871b5258b7;\n style 82ce2107-89a3-438c-95bb-dc871b5258b7 stroke:#2c3143,stroke-width:4px;\n 49[\"Regex Find And Replace\"];\n 43 -->|output| 49;\n 50[\"Multi-Join\"];\n 45 -->|out_file1| 50;\n 44 -->|out_file1| 50;\n 51[\"Heatmap w ggplot\"];\n 46 -->|out_file1| 51;\n c0417c91-a513-4c6a-9a62-3aac2f1f8e85[\"Output\\nheatmap_pdf\"];\n 51 --> c0417c91-a513-4c6a-9a62-3aac2f1f8e85;\n style c0417c91-a513-4c6a-9a62-3aac2f1f8e85 stroke:#2c3143,stroke-width:4px;\n 97816bc2-fd0c-4077-a721-8dd1470879d1[\"Output\\nheatmap_png\"];\n 51 --> 97816bc2-fd0c-4077-a721-8dd1470879d1;\n style 97816bc2-fd0c-4077-a721-8dd1470879d1 stroke:#2c3143,stroke-width:4px;\n 52[\"Filter empty datasets\"];\n 47 -->|out_file1| 52;\n 53[\"ClustalW\"];\n 48 -->|output| 53;\n 9b7bd78c-f112-480b-a7df-c10711af254c[\"Output\\nclustalw_on_input_dnd\"];\n 53 --> 9b7bd78c-f112-480b-a7df-c10711af254c;\n style 9b7bd78c-f112-480b-a7df-c10711af254c stroke:#2c3143,stroke-width:4px;\n 6af20322-24da-4036-80de-37bed2d25848[\"Output\\nclustalw_on_input_clustal\"];\n 53 --> 6af20322-24da-4036-80de-37bed2d25848;\n style 6af20322-24da-4036-80de-37bed2d25848 stroke:#2c3143,stroke-width:4px;\n 54[\"Filter empty datasets\"];\n 49 -->|out_file1| 54;\n 55[\"Replace Text\"];\n 50 -->|outfile| 55;\n 91745da0-0b8d-4a7a-b927-b36107f17ec5[\"Output\\nvfs_amrs_count_table\"];\n 55 --> 91745da0-0b8d-4a7a-b927-b36107f17ec5;\n style 91745da0-0b8d-4a7a-b927-b36107f17ec5 stroke:#2c3143,stroke-width:4px;\n 56[\"FASTA-to-Tabular\"];\n 52 -->|output| 56;\n 57[\"Filter empty datasets\"];\n 53 -->|output| 57;\n 32d00c1d-68c4-4069-8d5d-023aabdfadbe[\"Output\\nfiltered_empty_datasets\"];\n 57 --> 32d00c1d-68c4-4069-8d5d-023aabdfadbe;\n style 32d00c1d-68c4-4069-8d5d-023aabdfadbe stroke:#2c3143,stroke-width:4px;\n 58[\"FASTA-to-Tabular\"];\n 54 -->|output| 58;\n 59[\"Cut\"];\n 56 -->|output| 59;\n 60[\"FASTTREE\"];\n 57 -->|output| 60;\n aacdfe45-eb0c-4f6e-a479-eeb170774757[\"Output\\nfasttree_nhx\"];\n 60 --> aacdfe45-eb0c-4f6e-a479-eeb170774757;\n style aacdfe45-eb0c-4f6e-a479-eeb170774757 stroke:#2c3143,stroke-width:4px;\n 61[\"Cut\"];\n 58 -->|output| 61;\n 62[\"Group\"];\n 59 -->|out_file1| 62;\n 63[\"Newick Display\"];\n 60 -->|output| 63;\n 0c22178c-dc85-4137-80e2-f3040b92bd20[\"Output\\nnewick_genes_tree_graphs_collection\"];\n 63 --> 0c22178c-dc85-4137-80e2-f3040b92bd20;\n style 0c22178c-dc85-4137-80e2-f3040b92bd20 stroke:#2c3143,stroke-width:4px;\n 64[\"Group\"];\n 61 -->|out_file1| 64;\n 65[\"Tabular-to-FASTA\"];\n 62 -->|out_file1| 65;\n 66[\"Tabular-to-FASTA\"];\n 64 -->|out_file1| 66;\n 67[\"FASTA Merge Files and Filter Unique Sequences\"];\n 65 -->|output| 67;\n 68[\"FASTA Merge Files and Filter Unique Sequences\"];\n 66 -->|output| 68;\n 69[\"ClustalW\"];\n 67 -->|output| 69;\n 70[\"ClustalW\"];\n 68 -->|output| 70;\n 71[\"FASTTREE\"];\n 69 -->|output| 71;\n 72[\"FASTTREE\"];\n 70 -->|output| 72;\n 73[\"Newick Display\"];\n 71 -->|output| 73;\n 1f9cb2cf-219f-48de-8058-d6d45f3b3158[\"Output\\nall_samples_phylogenetic_tree_based_amrs\"];\n 73 --> 1f9cb2cf-219f-48de-8058-d6d45f3b3158;\n style 1f9cb2cf-219f-48de-8058-d6d45f3b3158 stroke:#2c3143,stroke-width:4px;\n 74[\"Newick Display\"];\n 72 -->|output| 74;\n 6bb4b32b-7cca-4e04-8120-be9f64ccba39[\"Output\\nall_samples_phylogenetic_tree_based_vfs\"];\n 74 --> 6bb4b32b-7cca-4e04-8120-be9f64ccba39;\n style 6bb4b32b-7cca-4e04-8120-be9f64ccba39 stroke:#2c3143,stroke-width:4px;", + "modified": "2024-06-24 00:06:32 +0000", + "name": "Pathogen Detection PathoGFAIR Samples Aggregation and Visualisation", + "path": "topics/microbiome/tutorials/pathogen-detection-from-nanopore-foodborne-data/workflows/pathogen_detection_pathoGFAIR_samples_aggregation_and_visualisation.ga", + "test_results": null, + "tests": false, + "title": "Pathogen Detection PathoGFAIR Samples Aggregation and Visualisation", + "trs_endpoint": "https://training.galaxyproject.org/training-material/api/ga4gh/trs/v2/tools/microbiome-pathogen-detection-from-nanopore-foodborne-data/versions/pathogen_detection_pathogfair_samples_aggrtion_and_visualisation", + "url": "https://training.galaxyproject.org/training-material/topics/microbiome/tutorials/pathogen-detection-from-nanopore-foodborne-data/workflows/pathogen_detection_pathoGFAIR_samples_aggregation_and_visualisation.ga", + "wfid": "microbiome-pathogen-detection-from-nanopore-foodborne-data", + "wfname": "pathogen_detection_pathogfair_samples_aggrtion_and_visualisation", + "workflow": "pathogen_detection_pathoGFAIR_samples_aggregation_and_visualisation.ga" + }, + { + "creators": [ + { + "class": "Person", + "identifier": "0000-0001-9047-4215", + "name": "Engy Nasr", + "url": "https://orcid.org/0000-0001-9047-4215" + }, + { + "class": "Person", + "identifier": "0000-0001-9852-1987", + "name": "B\u00e9r\u00e9nice Batut", + "url": "https://orcid.org/0000-0001-9852-1987" + }, + { + "class": "Person", + "identifier": "0000-0003-2982-388X", + "name": "Paul Zierep" + } + ], + "license": "MIT", + "mermaid": "flowchart TD\n 0[\"\u2139\ufe0f Input Collection\\ncollection_of_preprocessed_samples\"];\n 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"__input_ext": "\"input\"", + "__workflow_invocation_uuid__": "\"9ef558e0f65111eda6f2001e67d2ec02\"", + "chromInfo": "\"/opt/galaxy/tool-data/shared/ucsc/chrom/?.len\"", + "dbkey": "\"?\"", + "params": "{\"__current_case__\": 0, \"settingsType\": \"default\"}", + "rrbs": "{\"__current_case__\": 0, \"settingsType\": \"default\"}", + "singlePaired": "{\"__current_case__\": 1, \"input_mate1\": {\"values\": [{\"id\": 137415404, \"src\": \"hda\"}]}, \"input_mate2\": {\"values\": [{\"id\": 137415461, \"src\": \"hda\"}]}, \"sPaired\": \"paired\", \"three_prime_clip_R1\": null, \"three_prime_clip_R2\": null, \"trim1\": false, \"trimming\": {\"__current_case__\": 0, \"trimming_select\": \"\"}}" + }, + "state": "ok", + "stderr": "Path to Cutadapt set as: 'cutadapt' (default)\nCutadapt seems to be working fine (tested command 'cutadapt --version')\n\n\nAUTO-DETECTING ADAPTER TYPE\n===========================\nAttempting to auto-detect adapter type from the first 1 million sequences of the first file (>> input_1.fastq.gz <<)\n\nFound perfect matches for the following adapter sequences:\nAdapter type\tCount\tSequence\tSequences analysed\tPercentage\nIllumina\t2\tAGATCGGAAGAGC\t50000\t0.00\nsmallRNA\t0\tTGGAATTCTCGG\t50000\t0.00\nNextera\t0\tCTGTCTCTTATA\t50000\t0.00\nUsing Illumina adapter for trimming (count: 2). Second best hit was smallRNA (count: 0)\n\nWriting report to './input_1.fastq.gz_trimming_report.txt'\n\nSUMMARISING RUN PARAMETERS\n==========================\nInput filename: input_1.fastq.gz\nTrimming mode: paired-end\nTrim Galore version: 0.4.3\nCutadapt version: 1.13\nQuality Phred score cutoff: 20\nQuality encoding type selected: ASCII+33\nAdapter sequence: 'AGATCGGAAGAGC' (Illumina TruSeq, Sanger iPCR; auto-detected)\nMaximum trimming error rate: 0.1 (default)\nMinimum required adapter overlap (stringency): 1 bp\nMinimum required sequence length for both reads before a sequence pair gets removed: 20 bp\nOutput file(s) will be GZIP compressed\n\nWriting final adapter and quality trimmed output to input_1_trimmed.fq.gz\n\n\n >>> Now performing quality (cutoff 20) and adapter trimming in a single pass for the adapter sequence: 'AGATCGGAAGAGC' from file input_1.fastq.gz <<< \nThis is cutadapt 1.13 with Python 3.6.1\nCommand line parameters: -f fastq -e 0.1 -q 20 -O 1 -a AGATCGGAAGAGC input_1.fastq.gz\nTrimming 1 adapter with at most 10.0% errors in single-end mode ...\nFinished in 0.85 s (17 us/read; 3.52 M reads/minute).\n\n=== Summary ===\n\nTotal reads processed: 50,000\nReads with adapters: 14,011 (28.0%)\nReads written (passing filters): 50,000 (100.0%)\n\nTotal basepairs processed: 2,550,000 bp\nQuality-trimmed: 34,012 bp (1.3%)\nTotal written (filtered): 2,495,795 bp (97.9%)\n\n=== Adapter 1 ===\n\nSequence: AGATCGGAAGAGC; Type: regular 3'; Length: 13; Trimmed: 14011 times.\n\nNo. of allowed errors:\n0-9 bp: 0; 10-13 bp: 1\n\nBases preceding removed adapters:\n A: 27.3%\n C: 33.3%\n G: 24.8%\n T: 14.6%\n none/other: 0.0%\n\nOverview of removed sequences\nlength\tcount\texpect\tmax.err\terror counts\n1\t9379\t12500.0\t0\t9379\n2\t3646\t3125.0\t0\t3646\n3\t791\t781.2\t0\t791\n4\t134\t195.3\t0\t134\n5\t33\t48.8\t0\t33\n6\t9\t12.2\t0\t9\n7\t3\t3.1\t0\t3\n8\t1\t0.8\t0\t1\n9\t1\t0.2\t0\t0 1\n10\t3\t0.0\t1\t0 3\n11\t1\t0.0\t1\t1\n15\t1\t0.0\t1\t1\n16\t1\t0.0\t1\t0 1\n19\t1\t0.0\t1\t1\n27\t1\t0.0\t1\t0 1\n28\t1\t0.0\t1\t0 1\n32\t1\t0.0\t1\t0 1\n36\t1\t0.0\t1\t0 1\n43\t1\t0.0\t1\t0 1\n49\t1\t0.0\t1\t0 1\n50\t1\t0.0\t1\t0 1\n\n\nRUN STATISTICS FOR INPUT FILE: input_1.fastq.gz\n=============================================\n50000 sequences processed in total\nThe length threshold of paired-end sequences gets evaluated later on (in the validation step)\n\nWriting report to './input_2.fastq.gz_trimming_report.txt'\n\nSUMMARISING RUN PARAMETERS\n==========================\nInput filename: input_2.fastq.gz\nTrimming mode: paired-end\nTrim Galore version: 0.4.3\nCutadapt version: 1.13\nQuality Phred score cutoff: 20\nQuality encoding type selected: ASCII+33\nAdapter sequence: 'AGATCGGAAGAGC' (Illumina TruSeq, Sanger iPCR; auto-detected)\nMaximum trimming error rate: 0.1 (default)\nMinimum required adapter overlap (stringency): 1 bp\nMinimum required sequence length for both reads before a sequence pair gets removed: 20 bp\nOutput file(s) will be GZIP compressed\n\nWriting final adapter and quality trimmed output to input_2_trimmed.fq.gz\n\n\n >>> Now performing quality (cutoff 20) and adapter trimming in a single pass for the adapter sequence: 'AGATCGGAAGAGC' from file input_2.fastq.gz <<< \nThis is cutadapt 1.13 with Python 3.6.1\nCommand line parameters: -f fastq -e 0.1 -q 20 -O 1 -a AGATCGGAAGAGC input_2.fastq.gz\nTrimming 1 adapter with at most 10.0% errors in single-end mode ...\nFinished in 0.91 s (18 us/read; 3.30 M reads/minute).\n\n=== Summary ===\n\nTotal reads processed: 50,000\nReads with adapters: 13,787 (27.6%)\nReads written (passing filters): 50,000 (100.0%)\n\nTotal basepairs processed: 2,550,000 bp\nQuality-trimmed: 120,340 bp (4.7%)\nTotal written (filtered): 2,410,008 bp (94.5%)\n\n=== Adapter 1 ===\n\nSequence: AGATCGGAAGAGC; Type: regular 3'; Length: 13; Trimmed: 13787 times.\n\nNo. of allowed errors:\n0-9 bp: 0; 10-13 bp: 1\n\nBases preceding removed adapters:\n A: 27.6%\n C: 32.8%\n G: 24.6%\n T: 15.0%\n none/other: 0.0%\n\nOverview of removed sequences\nlength\tcount\texpect\tmax.err\terror counts\n1\t9236\t12500.0\t0\t9236\n2\t3546\t3125.0\t0\t3546\n3\t827\t781.2\t0\t827\n4\t131\t195.3\t0\t131\n5\t36\t48.8\t0\t36\n6\t3\t12.2\t0\t3\n7\t2\t3.1\t0\t2\n9\t1\t0.2\t0\t0 1\n11\t2\t0.0\t1\t1 1\n15\t1\t0.0\t1\t1\n19\t1\t0.0\t1\t1\n42\t1\t0.0\t1\t0 1\n\n\nRUN STATISTICS FOR INPUT FILE: input_2.fastq.gz\n=============================================\n50000 sequences processed in total\nThe length threshold of paired-end sequences gets evaluated later on (in the validation step)\n\nValidate paired-end files input_1_trimmed.fq.gz and input_2_trimmed.fq.gz\nfile_1: input_1_trimmed.fq.gz, file_2: input_2_trimmed.fq.gz\n\n\n>>>>> Now validing the length of the 2 paired-end infiles: input_1_trimmed.fq.gz and input_2_trimmed.fq.gz <<<<<\nWriting validated paired-end read 1 reads to input_1_val_1.fq.gz\nWriting validated paired-end read 2 reads to input_2_val_2.fq.gz\n\nTotal number of sequences analysed: 50000\n\nNumber of sequence pairs removed because at least one read was shorter than the length cutoff (20 bp): 1639 (3.28%)\n\nDeleting both intermediate output files input_1_trimmed.fq.gz and input_2_trimmed.fq.gz\n\n====================================================================================================\n\n\ngzip: stdout: Broken pipe\n", + "stdout": "1.13\n", + "tool_id": "toolshed.g2.bx.psu.edu/repos/bgruening/trim_galore/trim_galore/0.4.3.1", + "tool_stderr": "Path to Cutadapt set as: 'cutadapt' (default)\nCutadapt seems to be working fine (tested command 'cutadapt --version')\n\n\nAUTO-DETECTING ADAPTER TYPE\n===========================\nAttempting to auto-detect adapter type from the first 1 million sequences of the first file (>> input_1.fastq.gz <<)\n\nFound perfect matches for the following adapter sequences:\nAdapter type\tCount\tSequence\tSequences analysed\tPercentage\nIllumina\t2\tAGATCGGAAGAGC\t50000\t0.00\nsmallRNA\t0\tTGGAATTCTCGG\t50000\t0.00\nNextera\t0\tCTGTCTCTTATA\t50000\t0.00\nUsing Illumina adapter for trimming (count: 2). Second best hit was smallRNA (count: 0)\n\nWriting report to './input_1.fastq.gz_trimming_report.txt'\n\nSUMMARISING RUN PARAMETERS\n==========================\nInput filename: input_1.fastq.gz\nTrimming mode: paired-end\nTrim Galore version: 0.4.3\nCutadapt version: 1.13\nQuality Phred score cutoff: 20\nQuality encoding type selected: ASCII+33\nAdapter sequence: 'AGATCGGAAGAGC' (Illumina TruSeq, Sanger iPCR; auto-detected)\nMaximum trimming error rate: 0.1 (default)\nMinimum required adapter overlap (stringency): 1 bp\nMinimum required sequence length for both reads before a sequence pair gets removed: 20 bp\nOutput file(s) will be GZIP compressed\n\nWriting final adapter and quality trimmed output to input_1_trimmed.fq.gz\n\n\n >>> Now performing quality (cutoff 20) and adapter trimming in a single pass for the adapter sequence: 'AGATCGGAAGAGC' from file input_1.fastq.gz <<< \nThis is cutadapt 1.13 with Python 3.6.1\nCommand line parameters: -f fastq -e 0.1 -q 20 -O 1 -a AGATCGGAAGAGC input_1.fastq.gz\nTrimming 1 adapter with at most 10.0% errors in single-end mode ...\nFinished in 0.85 s (17 us/read; 3.52 M reads/minute).\n\n=== Summary ===\n\nTotal reads processed: 50,000\nReads with adapters: 14,011 (28.0%)\nReads written (passing filters): 50,000 (100.0%)\n\nTotal basepairs processed: 2,550,000 bp\nQuality-trimmed: 34,012 bp (1.3%)\nTotal written (filtered): 2,495,795 bp (97.9%)\n\n=== Adapter 1 ===\n\nSequence: AGATCGGAAGAGC; Type: regular 3'; Length: 13; Trimmed: 14011 times.\n\nNo. of allowed errors:\n0-9 bp: 0; 10-13 bp: 1\n\nBases preceding removed adapters:\n A: 27.3%\n C: 33.3%\n G: 24.8%\n T: 14.6%\n none/other: 0.0%\n\nOverview of removed sequences\nlength\tcount\texpect\tmax.err\terror counts\n1\t9379\t12500.0\t0\t9379\n2\t3646\t3125.0\t0\t3646\n3\t791\t781.2\t0\t791\n4\t134\t195.3\t0\t134\n5\t33\t48.8\t0\t33\n6\t9\t12.2\t0\t9\n7\t3\t3.1\t0\t3\n8\t1\t0.8\t0\t1\n9\t1\t0.2\t0\t0 1\n10\t3\t0.0\t1\t0 3\n11\t1\t0.0\t1\t1\n15\t1\t0.0\t1\t1\n16\t1\t0.0\t1\t0 1\n19\t1\t0.0\t1\t1\n27\t1\t0.0\t1\t0 1\n28\t1\t0.0\t1\t0 1\n32\t1\t0.0\t1\t0 1\n36\t1\t0.0\t1\t0 1\n43\t1\t0.0\t1\t0 1\n49\t1\t0.0\t1\t0 1\n50\t1\t0.0\t1\t0 1\n\n\nRUN STATISTICS FOR INPUT FILE: input_1.fastq.gz\n=============================================\n50000 sequences processed in total\nThe length threshold of paired-end sequences gets evaluated later on (in the validation step)\n\nWriting report to './input_2.fastq.gz_trimming_report.txt'\n\nSUMMARISING RUN PARAMETERS\n==========================\nInput filename: input_2.fastq.gz\nTrimming mode: paired-end\nTrim Galore version: 0.4.3\nCutadapt version: 1.13\nQuality Phred score cutoff: 20\nQuality encoding type selected: ASCII+33\nAdapter sequence: 'AGATCGGAAGAGC' (Illumina TruSeq, Sanger iPCR; auto-detected)\nMaximum trimming error rate: 0.1 (default)\nMinimum required adapter overlap (stringency): 1 bp\nMinimum required sequence length for both reads before a sequence pair gets removed: 20 bp\nOutput file(s) will be GZIP compressed\n\nWriting final adapter and quality trimmed output to input_2_trimmed.fq.gz\n\n\n >>> Now performing quality (cutoff 20) and adapter trimming in a single pass for the adapter sequence: 'AGATCGGAAGAGC' from file input_2.fastq.gz <<< \nThis is cutadapt 1.13 with Python 3.6.1\nCommand line parameters: -f fastq -e 0.1 -q 20 -O 1 -a AGATCGGAAGAGC input_2.fastq.gz\nTrimming 1 adapter with at most 10.0% errors in single-end mode ...\nFinished in 0.91 s (18 us/read; 3.30 M reads/minute).\n\n=== Summary ===\n\nTotal reads processed: 50,000\nReads with adapters: 13,787 (27.6%)\nReads written (passing filters): 50,000 (100.0%)\n\nTotal basepairs processed: 2,550,000 bp\nQuality-trimmed: 120,340 bp (4.7%)\nTotal written (filtered): 2,410,008 bp (94.5%)\n\n=== Adapter 1 ===\n\nSequence: AGATCGGAAGAGC; Type: regular 3'; Length: 13; Trimmed: 13787 times.\n\nNo. of allowed errors:\n0-9 bp: 0; 10-13 bp: 1\n\nBases preceding removed adapters:\n A: 27.6%\n C: 32.8%\n G: 24.6%\n T: 15.0%\n none/other: 0.0%\n\nOverview of removed sequences\nlength\tcount\texpect\tmax.err\terror counts\n1\t9236\t12500.0\t0\t9236\n2\t3546\t3125.0\t0\t3546\n3\t827\t781.2\t0\t827\n4\t131\t195.3\t0\t131\n5\t36\t48.8\t0\t36\n6\t3\t12.2\t0\t3\n7\t2\t3.1\t0\t2\n9\t1\t0.2\t0\t0 1\n11\t2\t0.0\t1\t1 1\n15\t1\t0.0\t1\t1\n19\t1\t0.0\t1\t1\n42\t1\t0.0\t1\t0 1\n\n\nRUN STATISTICS FOR INPUT FILE: input_2.fastq.gz\n=============================================\n50000 sequences processed in total\nThe length threshold of paired-end sequences gets evaluated later on (in the validation step)\n\nValidate paired-end files input_1_trimmed.fq.gz and input_2_trimmed.fq.gz\nfile_1: input_1_trimmed.fq.gz, file_2: input_2_trimmed.fq.gz\n\n\n>>>>> Now validing the length of the 2 paired-end infiles: input_1_trimmed.fq.gz and input_2_trimmed.fq.gz <<<<<\nWriting validated paired-end read 1 reads to input_1_val_1.fq.gz\nWriting validated paired-end read 2 reads to input_2_val_2.fq.gz\n\nTotal number of sequences analysed: 50000\n\nNumber of sequence pairs removed because at least one read was shorter than the length cutoff (20 bp): 1639 (3.28%)\n\nDeleting both intermediate output files input_1_trimmed.fq.gz and input_2_trimmed.fq.gz\n\n====================================================================================================\n\n\ngzip: stdout: Broken pipe\n", + "tool_stdout": "1.13\n", + "traceback": null, + "update_time": "2023-05-19T14:31:12.868610", + "user_email": "hxr@informatik.uni-freiburg.de" + } + ], + "model_class": "WorkflowInvocationStep", + "order_index": 4, + "output_collections": {}, + "outputs": { + "trimmed_reads_pair1": { + "id": "4838ba20a6d86765439b06bcf8677007", + "src": "hda", + "uuid": "64981dba-44f7-4768-8b63-e63defba2ecf" + }, + "trimmed_reads_pair2": { + "id": "4838ba20a6d867658f72f66dd1d7e6cc", + "src": "hda", + "uuid": "0764cea4-72ed-4a8c-93b2-88bd066afc4e" + } + }, + "state": "scheduled", + "subworkflow": null, + "subworkflow_invocation_id": null, + "update_time": "2023-05-19T14:31:12.847743", + "workflow_step_id": "0cdaaf28f2c92138", + "workflow_step_label": null, + "workflow_step_uuid": "ba2566a8-447f-45ed-98d1-1fd1a5ba6ab4" + }, + "5. 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bfc499ee-630a-498b-9be6-696d9bb78837;\n style bfc499ee-630a-498b-9be6-696d9bb78837 stroke:#2c3143,stroke-width:4px;\n 4[\"Plot\"];\n 0 -->|output| 4;\n f2c78ef3-7d31-4930-954c-0133cce27a41[\"Output\\nScatter - mito x genes\"];\n 4 --> f2c78ef3-7d31-4930-954c-0133cce27a41;\n style f2c78ef3-7d31-4930-954c-0133cce27a41 stroke:#2c3143,stroke-width:4px;\n 5[\"Plot\"];\n 0 -->|output| 5;\n 348818e6-9def-41fd-a390-6f8525c57cd8[\"Output\\nViolin - genotype - log\"];\n 5 --> 348818e6-9def-41fd-a390-6f8525c57cd8;\n style 348818e6-9def-41fd-a390-6f8525c57cd8 stroke:#2c3143,stroke-width:4px;\n 6[\"Plot\"];\n 0 -->|output| 6;\n 844f5e7c-78c8-4f28-8e15-cea35ada8fef[\"Output\\nViolin - batch - log\"];\n 6 --> 844f5e7c-78c8-4f28-8e15-cea35ada8fef;\n style 844f5e7c-78c8-4f28-8e15-cea35ada8fef stroke:#2c3143,stroke-width:4px;\n 7[\"Inspect AnnData\"];\n 0 -->|output| 7;\n 8[\"Plot\"];\n 0 -->|output| 8;\n 3b1232c5-d16a-434d-806e-79fd77f7c05f[\"Output\\nScatter - mito x UMIs\"];\n 8 --> 3b1232c5-d16a-434d-806e-79fd77f7c05f;\n style 3b1232c5-d16a-434d-806e-79fd77f7c05f stroke:#2c3143,stroke-width:4px;\n 9[\"Inspect AnnData\"];\n 0 -->|output| 9;\n 10[\"Plot\"];\n 0 -->|output| 10;\n a639cdc0-da40-4df9-8348-23117528b24a[\"Output\\nViolin - sex - log\"];\n 10 --> a639cdc0-da40-4df9-8348-23117528b24a;\n style a639cdc0-da40-4df9-8348-23117528b24a stroke:#2c3143,stroke-width:4px;\n 11[\"Plot\"];\n 2 -->|output_h5ad| 11;\n 69bf3e42-63e2-4b5b-9d63-5aac9d6b5691[\"Output\\nViolin - Filterbygenes\"];\n 11 --> 69bf3e42-63e2-4b5b-9d63-5aac9d6b5691;\n style 69bf3e42-63e2-4b5b-9d63-5aac9d6b5691 stroke:#2c3143,stroke-width:4px;\n 12[\"Scanpy FilterCells\"];\n 2 -->|output_h5ad| 12;\n f378cb4e-0a77-49d9-b92a-752dbea4b09a[\"Output\\nCounts-filtered Object\"];\n 12 --> f378cb4e-0a77-49d9-b92a-752dbea4b09a;\n style f378cb4e-0a77-49d9-b92a-752dbea4b09a stroke:#2c3143,stroke-width:4px;\n 13[\"Inspect AnnData\"];\n 2 -->|output_h5ad| 13;\n f9e862db-eb22-4124-8fe0-0fdcfcfb8393[\"Output\\nGeneral - Filterbygenes\"];\n 13 --> f9e862db-eb22-4124-8fe0-0fdcfcfb8393;\n style f9e862db-eb22-4124-8fe0-0fdcfcfb8393 stroke:#2c3143,stroke-width:4px;\n 14[\"Inspect AnnData\"];\n 12 -->|output_h5ad| 14;\n 794f72b5-c2c3-46a0-ac15-b9f1e94941d2[\"Output\\nGeneral - Filterbycounts\"];\n 14 --> 794f72b5-c2c3-46a0-ac15-b9f1e94941d2;\n style 794f72b5-c2c3-46a0-ac15-b9f1e94941d2 stroke:#2c3143,stroke-width:4px;\n 15[\"Scanpy FilterCells\"];\n 12 -->|output_h5ad| 15;\n b915da66-6435-4871-baa0-3e494ba73c96[\"Output\\nMito-filtered Object\"];\n 15 --> b915da66-6435-4871-baa0-3e494ba73c96;\n style b915da66-6435-4871-baa0-3e494ba73c96 stroke:#2c3143,stroke-width:4px;\n 16[\"Plot\"];\n 12 -->|output_h5ad| 16;\n 3aef86d7-d34d-4b24-bc97-bf8c97d8d2fa[\"Output\\nViolin - Filterbycounts\"];\n 16 --> 3aef86d7-d34d-4b24-bc97-bf8c97d8d2fa;\n style 3aef86d7-d34d-4b24-bc97-bf8c97d8d2fa stroke:#2c3143,stroke-width:4px;\n 17[\"Inspect AnnData\"];\n 15 -->|output_h5ad| 17;\n cd94a4c6-5665-4bdf-88ea-4f4d41efa893[\"Output\\nGeneral - Filterbymito\"];\n 17 --> cd94a4c6-5665-4bdf-88ea-4f4d41efa893;\n style cd94a4c6-5665-4bdf-88ea-4f4d41efa893 stroke:#2c3143,stroke-width:4px;\n 18[\"Scanpy FilterGenes\"];\n 15 -->|output_h5ad| 18;\n ee63ef0a-98ed-45cb-b144-1154f84ae452[\"Output\\nFiltered Object\"];\n 18 --> ee63ef0a-98ed-45cb-b144-1154f84ae452;\n style ee63ef0a-98ed-45cb-b144-1154f84ae452 stroke:#2c3143,stroke-width:4px;\n 19[\"Plot\"];\n 15 -->|output_h5ad| 19;\n 7e48a14f-08fd-45ab-b613-606bf64dcf9d[\"Output\\nViolin - Filterbymito\"];\n 19 --> 7e48a14f-08fd-45ab-b613-606bf64dcf9d;\n style 7e48a14f-08fd-45ab-b613-606bf64dcf9d stroke:#2c3143,stroke-width:4px;\n 20[\"Inspect AnnData\"];\n 18 -->|output_h5ad| 20;\n d59efa9b-d049-4f0e-8bd8-8ae982a45d0a[\"Output\\nGeneral - Filtered object\"];\n 20 --> d59efa9b-d049-4f0e-8bd8-8ae982a45d0a;\n style d59efa9b-d049-4f0e-8bd8-8ae982a45d0a stroke:#2c3143,stroke-width:4px;\n 21[\"Scanpy NormaliseData\"];\n 18 -->|output_h5ad| 21;\n 22[\"Scanpy FindVariableGenes\"];\n 21 -->|output_h5ad| 22;\n 23[\"Scanpy ScaleData\"];\n 22 -->|output_h5ad| 23;\n 24[\"Scanpy RunPCA\"];\n 23 -->|output_h5ad| 24;\n 25[\"Plot\"];\n 24 -->|output_h5ad| 25;\n 993dea99-990f-460a-beb9-46e5c97ee898[\"Output\\nPCA Variance\"];\n 25 --> 993dea99-990f-460a-beb9-46e5c97ee898;\n style 993dea99-990f-460a-beb9-46e5c97ee898 stroke:#2c3143,stroke-width:4px;\n 26[\"Scanpy ComputeGraph\"];\n 24 -->|output_h5ad| 26;\n 27[\"Scanpy RunTSNE\"];\n 26 -->|output_h5ad| 27;\n 28[\"Scanpy RunUMAP\"];\n 27 -->|output_h5ad| 28;\n 29[\"Scanpy FindCluster\"];\n 28 -->|output_h5ad| 29;\n 30[\"Scanpy FindMarkers\"];\n 29 -->|output_h5ad| 30;\n 308b4961-4d50-442b-9bca-bbb1992426ba[\"Output\\nMarkers - cluster\"];\n 30 --> 308b4961-4d50-442b-9bca-bbb1992426ba;\n style 308b4961-4d50-442b-9bca-bbb1992426ba stroke:#2c3143,stroke-width:4px;\n 035bbbce-fb57-48c8-83d5-2b0cd0376541[\"Output\\nFinal object\"];\n 30 --> 035bbbce-fb57-48c8-83d5-2b0cd0376541;\n style 035bbbce-fb57-48c8-83d5-2b0cd0376541 stroke:#2c3143,stroke-width:4px;\n 31[\"Scanpy FindMarkers\"];\n 29 -->|output_h5ad| 31;\n 1705e219-192a-4f52-9b26-64fcbcd303ea[\"Output\\nMarkers - genotype\"];\n 31 --> 1705e219-192a-4f52-9b26-64fcbcd303ea;\n style 1705e219-192a-4f52-9b26-64fcbcd303ea stroke:#2c3143,stroke-width:4px;\n 32[\"Scanpy PlotEmbed\"];\n 30 -->|output_h5ad| 32;\n 33[\"Scanpy PlotEmbed\"];\n 30 -->|output_h5ad| 33;\n 34[\"Manipulate AnnData\"];\n 30 -->|output_h5ad| 34;\n 35[\"Scanpy PlotEmbed\"];\n 30 -->|output_h5ad| 35;\n 36[\"Inspect AnnData\"];\n 30 -->|output_h5ad| 36;\n 37[\"AnnData Operations\"];\n 34 -->|anndata| 37;\n 30 -->|output_h5ad| 37;\n 38[\"Join two Datasets\"];\n 30 -->|output_tsv| 38;\n 36 -->|var| 38;\n 39[\"Join two Datasets\"];\n 31 -->|output_tsv| 39;\n 36 -->|var| 39;\n 40[\"AnnData Operations\"];\n 37 -->|output_h5ad| 40;\n a6d48df0-403f-4efc-a75f-9504a960884e[\"Output\\nFinal cell annotated object\"];\n 40 --> a6d48df0-403f-4efc-a75f-9504a960884e;\n style a6d48df0-403f-4efc-a75f-9504a960884e stroke:#2c3143,stroke-width:4px;\n 41[\"Cut\"];\n 38 -->|out_file1| 41;\n 0ee7f9b6-b065-4e26-93df-6e6e2fe458a9[\"Output\\nMarkers - cluster - named\"];\n 41 --> 0ee7f9b6-b065-4e26-93df-6e6e2fe458a9;\n style 0ee7f9b6-b065-4e26-93df-6e6e2fe458a9 stroke:#2c3143,stroke-width:4px;\n 42[\"Cut\"];\n 39 -->|out_file1| 42;\n fdb88faa-9b76-4edb-b89b-427c098a473e[\"Output\\nMarkers - genotype - named\"];\n 42 --> fdb88faa-9b76-4edb-b89b-427c098a473e;\n style fdb88faa-9b76-4edb-b89b-427c098a473e stroke:#2c3143,stroke-width:4px;\n 43[\"Scanpy PlotEmbed\"];\n 40 -->|output_h5ad| 43;", + "modified": "2024-06-24 00:06:33 +0000", + "name": "CS3_Filter, Plot and Explore Single-cell RNA-seq Data", + "path": "topics/single-cell/tutorials/scrna-case_basic-pipeline/workflows/CS3_Filter,_Plot_and_Explore_Single-cell_RNA-seq_Data.ga", + "test_results": null, + "tests": false, + "title": "CS3_Filter, Plot and Explore Single-cell RNA-seq Data", + "trs_endpoint": "https://training.galaxyproject.org/training-material/api/ga4gh/trs/v2/tools/single-cell-scrna-case_basic-pipeline/versions/cs3_filter,_plot_and_explore_single-cell_rna-seq_data", + "url": "https://training.galaxyproject.org/training-material/topics/single-cell/tutorials/scrna-case_basic-pipeline/workflows/CS3_Filter,_Plot_and_Explore_Single-cell_RNA-seq_Data.ga", + "wfid": "single-cell-scrna-case_basic-pipeline", + "wfname": "cs3_filter,_plot_and_explore_single-cell_rna-seq_data", + "workflow": "CS3_Filter,_Plot_and_Explore_Single-cell_RNA-seq_Data.ga" + }, + { + "creators": [ + { + "class": "Person", + "identifier": "0000-0002-8170-8806", + "name": "Wendi Bacon" + }, + { + "class": "Person", + "identifier": "0009-0001-2017-8805", + "name": "Julia Jakiela" + } + ], + "license": "CC-BY-4.0", + "mermaid": "flowchart TD\n 0[\"\u2139\ufe0f Input Dataset\\nMito-counted AnnData\"];\n style 0 stroke:#2c3143,stroke-width:4px;\n 1[\"Inspect AnnData\"];\n 0 -->|output| 1;\n 2[\"Scanpy FilterCells\"];\n 0 -->|output| 2;\n 6a00b14b-d3f8-4771-963a-76115c8eaf2f[\"Output\\nGenes-filtered Object\"];\n 2 --> 6a00b14b-d3f8-4771-963a-76115c8eaf2f;\n style 6a00b14b-d3f8-4771-963a-76115c8eaf2f stroke:#2c3143,stroke-width:4px;\n 3[\"Plot\"];\n 0 -->|output| 3;\n c123842e-b2d2-43ad-81f0-6ba3b45d4021[\"Output\\nScatter - genes x UMIs\"];\n 3 --> c123842e-b2d2-43ad-81f0-6ba3b45d4021;\n style c123842e-b2d2-43ad-81f0-6ba3b45d4021 stroke:#2c3143,stroke-width:4px;\n 4[\"Plot\"];\n 0 -->|output| 4;\n f1020d4d-555e-42fa-ba21-52a151b91a5b[\"Output\\nScatter - mito x genes\"];\n 4 --> f1020d4d-555e-42fa-ba21-52a151b91a5b;\n style f1020d4d-555e-42fa-ba21-52a151b91a5b stroke:#2c3143,stroke-width:4px;\n 5[\"Plot\"];\n 0 -->|output| 5;\n a7fe6020-8f1e-470a-9ed3-f2b5e95516e0[\"Output\\nViolin - genotype - log\"];\n 5 --> a7fe6020-8f1e-470a-9ed3-f2b5e95516e0;\n style a7fe6020-8f1e-470a-9ed3-f2b5e95516e0 stroke:#2c3143,stroke-width:4px;\n 6[\"Plot\"];\n 0 -->|output| 6;\n 15e501bc-8ddb-4519-89eb-dde431ea96c1[\"Output\\nViolin - batch - log\"];\n 6 --> 15e501bc-8ddb-4519-89eb-dde431ea96c1;\n style 15e501bc-8ddb-4519-89eb-dde431ea96c1 stroke:#2c3143,stroke-width:4px;\n 7[\"Inspect AnnData\"];\n 0 -->|output| 7;\n 8[\"Plot\"];\n 0 -->|output| 8;\n eff22f46-baa6-4e00-ba82-d5e12ce26ff0[\"Output\\nScatter - mito x UMIs\"];\n 8 --> eff22f46-baa6-4e00-ba82-d5e12ce26ff0;\n style eff22f46-baa6-4e00-ba82-d5e12ce26ff0 stroke:#2c3143,stroke-width:4px;\n 9[\"Inspect AnnData\"];\n 0 -->|output| 9;\n 10[\"Plot\"];\n 0 -->|output| 10;\n 56677ca4-129c-476c-85ec-69d1bb3d800d[\"Output\\nViolin - sex - log\"];\n 10 --> 56677ca4-129c-476c-85ec-69d1bb3d800d;\n style 56677ca4-129c-476c-85ec-69d1bb3d800d stroke:#2c3143,stroke-width:4px;\n 11[\"Plot\"];\n 2 -->|output_h5ad| 11;\n acb61ea4-bcb9-45db-beef-e2bf1a176701[\"Output\\nViolin - Filterbygenes\"];\n 11 --> acb61ea4-bcb9-45db-beef-e2bf1a176701;\n style acb61ea4-bcb9-45db-beef-e2bf1a176701 stroke:#2c3143,stroke-width:4px;\n 12[\"Scanpy FilterCells\"];\n 2 -->|output_h5ad| 12;\n 51853662-4519-4229-a2ea-b22a53e7ef73[\"Output\\nCounts-filtered Object\"];\n 12 --> 51853662-4519-4229-a2ea-b22a53e7ef73;\n style 51853662-4519-4229-a2ea-b22a53e7ef73 stroke:#2c3143,stroke-width:4px;\n 13[\"Inspect AnnData\"];\n 2 -->|output_h5ad| 13;\n 362a7fe6-24bb-4398-ae48-870f4b4bb774[\"Output\\nGeneral - Filterbygenes\"];\n 13 --> 362a7fe6-24bb-4398-ae48-870f4b4bb774;\n style 362a7fe6-24bb-4398-ae48-870f4b4bb774 stroke:#2c3143,stroke-width:4px;\n 14[\"Inspect AnnData\"];\n 12 -->|output_h5ad| 14;\n edf24149-9341-4fe7-b10c-3fcf092faaa5[\"Output\\nGeneral - Filterbycounts\"];\n 14 --> edf24149-9341-4fe7-b10c-3fcf092faaa5;\n style edf24149-9341-4fe7-b10c-3fcf092faaa5 stroke:#2c3143,stroke-width:4px;\n 15[\"Scanpy FilterCells\"];\n 12 -->|output_h5ad| 15;\n a88ec405-265f-4a59-a75e-34e3b05b0096[\"Output\\nMito-filtered Object\"];\n 15 --> a88ec405-265f-4a59-a75e-34e3b05b0096;\n style a88ec405-265f-4a59-a75e-34e3b05b0096 stroke:#2c3143,stroke-width:4px;\n 16[\"Plot\"];\n 12 -->|output_h5ad| 16;\n a7c8b0d9-82d3-4438-a212-b5f7c56d36b8[\"Output\\nViolin - Filterbycounts\"];\n 16 --> a7c8b0d9-82d3-4438-a212-b5f7c56d36b8;\n style a7c8b0d9-82d3-4438-a212-b5f7c56d36b8 stroke:#2c3143,stroke-width:4px;\n 17[\"Inspect AnnData\"];\n 15 -->|output_h5ad| 17;\n 56882809-e19f-451a-8010-bc55dcee482f[\"Output\\nGeneral - Filterbymito\"];\n 17 --> 56882809-e19f-451a-8010-bc55dcee482f;\n style 56882809-e19f-451a-8010-bc55dcee482f stroke:#2c3143,stroke-width:4px;\n 18[\"Scanpy FilterGenes\"];\n 15 -->|output_h5ad| 18;\n 00846477-dec5-408a-83b2-105fff7ce05b[\"Output\\nFiltered Object\"];\n 18 --> 00846477-dec5-408a-83b2-105fff7ce05b;\n style 00846477-dec5-408a-83b2-105fff7ce05b stroke:#2c3143,stroke-width:4px;\n 19[\"Plot\"];\n 15 -->|output_h5ad| 19;\n 7582e113-2004-4255-a1f3-d3123373f342[\"Output\\nViolin - Filterbymito\"];\n 19 --> 7582e113-2004-4255-a1f3-d3123373f342;\n style 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9d66a217-4269-4fb4-9a4e-520eedbb0c9d[\"Output\\nExtracted cell annotations (obs)\"];\n 2 --> 9d66a217-4269-4fb4-9a4e-520eedbb0c9d;\n style 9d66a217-4269-4fb4-9a4e-520eedbb0c9d stroke:#2c3143,stroke-width:4px;\n 3[\"Extract gene annotations\"];\n 0 -->|output| 3;\n 03537e97-d668-4ce2-ba6b-2952a18a2752[\"Output\\nExtracted gene annotations (var)\"];\n 3 --> 03537e97-d668-4ce2-ba6b-2952a18a2752;\n style 03537e97-d668-4ce2-ba6b-2952a18a2752 stroke:#2c3143,stroke-width:4px;\n 4[\"Extract clean expression matrix\"];\n 1 -->|output| 4;\n 963462c8-4ae0-486c-8173-d98339127850[\"Output\\nUnprocessed expression matrix\"];\n 4 --> 963462c8-4ae0-486c-8173-d98339127850;\n style 963462c8-4ae0-486c-8173-d98339127850 stroke:#2c3143,stroke-width:4px;\n 5[\"Filter out unwanted cell types\"];\n 2 -->|obs| 5;\n 5dcb7029-8d03-445d-bb3e-a3119e502be4[\"Output\\nCells without macrophages\"];\n 5 --> 5dcb7029-8d03-445d-bb3e-a3119e502be4;\n style 5dcb7029-8d03-445d-bb3e-a3119e502be4 stroke:#2c3143,stroke-width:4px;\n 6[\"gene_short_name annotation\"];\n 3 -->|var| 6;\n 15fd415b-2d73-4a1d-92ad-200bb2e73853[\"Output\\nGenes table with gene_short_name colname\"];\n 6 --> 15fd415b-2d73-4a1d-92ad-200bb2e73853;\n style 15fd415b-2d73-4a1d-92ad-200bb2e73853 stroke:#2c3143,stroke-width:4px;\n 7[\"Cut cells IDs\"];\n 5 -->|out_file1| 7;\n f9bde249-43b9-4424-8285-e437549223e2[\"Output\\nFiltered cells IDs \"];\n 7 --> f9bde249-43b9-4424-8285-e437549223e2;\n style f9bde249-43b9-4424-8285-e437549223e2 stroke:#2c3143,stroke-width:4px;\n 8[\"Cut genes IDs\"];\n 6 -->|out_file1| 8;\n b22efa1d-db99-435f-9357-74150ac9a437[\"Output\\nGenes IDs\"];\n 8 --> b22efa1d-db99-435f-9357-74150ac9a437;\n style b22efa1d-db99-435f-9357-74150ac9a437 stroke:#2c3143,stroke-width:4px;\n 9[\"Filter matrix by cells\"];\n 7 -->|out_file1| 9;\n 4 -->|X| 9;\n b45f18b6-e385-41a1-a8f0-f94e6616e2b3[\"Output\\nPre-filtered matrix (by cells)\"];\n 9 --> b45f18b6-e385-41a1-a8f0-f94e6616e2b3;\n style b45f18b6-e385-41a1-a8f0-f94e6616e2b3 stroke:#2c3143,stroke-width:4px;\n 10[\"Remove duplicate column cells IDs\"];\n 9 -->|out_file1| 10;\n f4d6a581-9b42-41dc-9a4a-278fe5d6591c[\"Output\\nFiltered matrix (by cells) \"];\n 10 --> f4d6a581-9b42-41dc-9a4a-278fe5d6591c;\n style f4d6a581-9b42-41dc-9a4a-278fe5d6591c stroke:#2c3143,stroke-width:4px;\n 11[\"Transpose matrix\"];\n 10 -->|output| 11;\n e4eb8fb3-2028-41fb-8aca-b1c0a6e6bfe5[\"Output\\nfiltered matrix (by cells) transposed\"];\n 11 --> e4eb8fb3-2028-41fb-8aca-b1c0a6e6bfe5;\n style e4eb8fb3-2028-41fb-8aca-b1c0a6e6bfe5 stroke:#2c3143,stroke-width:4px;\n 12[\"Filter matrix by genes\"];\n 8 -->|out_file1| 12;\n 11 -->|out_file| 12;\n c0481255-0187-407c-9f29-f4c0069d9285[\"Output\\nPre-filtered matrix (by cells & genes)\"];\n 12 --> c0481255-0187-407c-9f29-f4c0069d9285;\n style c0481255-0187-407c-9f29-f4c0069d9285 stroke:#2c3143,stroke-width:4px;\n 13[\"Remove duplicate column genes IDs\"];\n 12 -->|out_file1| 13;\n 4dcd8e60-1298-4886-be8f-af1eac9f20f7[\"Output\\nFiltered matrix (by cells & genes)\"];\n 13 --> 4dcd8e60-1298-4886-be8f-af1eac9f20f7;\n style 4dcd8e60-1298-4886-be8f-af1eac9f20f7 stroke:#2c3143,stroke-width:4px;", + "modified": "2024-06-24 00:06:33 +0000", + "name": "AnnData object to Monocle input files", + "path": "topics/single-cell/tutorials/scrna-case_monocle3-trajectories/workflows/Galaxy-Workflow-AnnData_object_to_Monocle_input_files.ga", + "test_results": null, + "tests": false, + "title": "AnnData object to Monocle input files", + "trs_endpoint": "https://training.galaxyproject.org/training-material/api/ga4gh/trs/v2/tools/single-cell-scrna-case_monocle3-trajectories/versions/galaxy-workflow-anndata_object_to_monocle_input_files", + "url": "https://training.galaxyproject.org/training-material/topics/single-cell/tutorials/scrna-case_monocle3-trajectories/workflows/Galaxy-Workflow-AnnData_object_to_Monocle_input_files.ga", + "wfid": "single-cell-scrna-case_monocle3-trajectories", + "wfname": "galaxy-workflow-anndata_object_to_monocle_input_files", + "workflow": "Galaxy-Workflow-AnnData_object_to_Monocle_input_files.ga" + }, + { + "creators": [], + "license": null, + "mermaid": "flowchart TD\n 0[\"\u2139\ufe0f Input Dataset\\nExpression matrix input\"];\n style 0 stroke:#2c3143,stroke-width:4px;\n 1[\"\u2139\ufe0f Input Dataset\\nCell annotations input\"];\n style 1 stroke:#2c3143,stroke-width:4px;\n 2[\"\u2139\ufe0f Input Dataset\\nGene annotations input\"];\n style 2 stroke:#2c3143,stroke-width:4px;\n 3[\"Monocle3 create\"];\n 1 -->|output| 3;\n 0 -->|output| 3;\n 2 -->|output| 3;\n 30db218d-f9e5-426d-ac9b-7823743ab5fb[\"Output\\nMonocle3 create on input dataset(s): cds3\"];\n 3 --> 30db218d-f9e5-426d-ac9b-7823743ab5fb;\n style 30db218d-f9e5-426d-ac9b-7823743ab5fb stroke:#2c3143,stroke-width:4px;\n 4[\"Monocle3 preprocess\"];\n 3 -->|output_rds| 4;\n 38996ee0-0c00-4a01-ad18-f2e505eaf780[\"Output\\nMonocle3 preprocess on input dataset(s): cds3\"];\n 4 --> 38996ee0-0c00-4a01-ad18-f2e505eaf780;\n style 38996ee0-0c00-4a01-ad18-f2e505eaf780 stroke:#2c3143,stroke-width:4px;\n 5[\"Monocle3 reduceDim\"];\n 4 -->|output_rds| 5;\n 21edf68f-72df-457d-aad7-c5fc5afa2b6b[\"Output\\nMonocle3 reduceDim on input dataset(s): cds3\"];\n 5 --> 21edf68f-72df-457d-aad7-c5fc5afa2b6b;\n style 21edf68f-72df-457d-aad7-c5fc5afa2b6b stroke:#2c3143,stroke-width:4px;\n 6[\"Monocle3 partition\"];\n 5 -->|output_rds| 6;\n fdb0e520-27c4-43a0-8f27-a25832a0816d[\"Output\\nMonocle3 partition on input dataset(s): cds3\"];\n 6 --> fdb0e520-27c4-43a0-8f27-a25832a0816d;\n style fdb0e520-27c4-43a0-8f27-a25832a0816d stroke:#2c3143,stroke-width:4px;\n 7[\"Plot cell type\"];\n 5 -->|output_rds| 7;\n 5e309658-e20f-4da1-8841-818f16fa8f01[\"Output\\nCell type plot\"];\n 7 --> 5e309658-e20f-4da1-8841-818f16fa8f01;\n style 5e309658-e20f-4da1-8841-818f16fa8f01 stroke:#2c3143,stroke-width:4px;\n 8[\"Plot genotype\"];\n 5 -->|output_rds| 8;\n aca3b2c0-2d3b-4a4e-8732-cf6ddc7844a4[\"Output\\nGenotype plot\"];\n 8 --> aca3b2c0-2d3b-4a4e-8732-cf6ddc7844a4;\n style aca3b2c0-2d3b-4a4e-8732-cf6ddc7844a4 stroke:#2c3143,stroke-width:4px;\n 9[\"Plot batch\"];\n 5 -->|output_rds| 9;\n c8eec2e4-1a64-47bb-812b-4002968c57c8[\"Output\\nBatch plot\"];\n 9 --> c8eec2e4-1a64-47bb-812b-4002968c57c8;\n style c8eec2e4-1a64-47bb-812b-4002968c57c8 stroke:#2c3143,stroke-width:4px;\n 10[\"Plot sex\"];\n 5 -->|output_rds| 10;\n c75954dd-34d5-4a20-bcf3-e5cd92d74885[\"Output\\nSex plot\"];\n 10 --> c75954dd-34d5-4a20-bcf3-e5cd92d74885;\n style c75954dd-34d5-4a20-bcf3-e5cd92d74885 stroke:#2c3143,stroke-width:4px;\n 11[\"Monocle3 top markers\"];\n 6 -->|output_rds| 11;\n 12[\"Plot genes\"];\n 6 -->|output_rds| 12;\n f91c22a3-8c6d-432a-92c5-cb010115fa86[\"Output\\nGene expression plot\"];\n 12 --> f91c22a3-8c6d-432a-92c5-cb010115fa86;\n style f91c22a3-8c6d-432a-92c5-cb010115fa86 stroke:#2c3143,stroke-width:4px;\n 13[\"Plot partition\"];\n 6 -->|output_rds| 13;\n f4c5f89b-edd7-489c-b4a2-a7a3e7d6a89d[\"Output\\nPartition plot\"];\n 13 --> f4c5f89b-edd7-489c-b4a2-a7a3e7d6a89d;\n style f4c5f89b-edd7-489c-b4a2-a7a3e7d6a89d stroke:#2c3143,stroke-width:4px;\n 14[\"Plot cluster\"];\n 6 -->|output_rds| 14;\n a2c1a628-d79d-4e37-ad33-de92033021a8[\"Output\\nCluster plot\"];\n 14 --> a2c1a628-d79d-4e37-ad33-de92033021a8;\n style a2c1a628-d79d-4e37-ad33-de92033021a8 stroke:#2c3143,stroke-width:4px;\n 15[\"Monocle3 learnGraph\"];\n 6 -->|output_rds| 15;\n ed2003aa-4680-409f-8d79-e79b878a786c[\"Output\\nMonocle3 learnGraph on input dataset(s): cds3\"];\n 15 --> ed2003aa-4680-409f-8d79-e79b878a786c;\n style ed2003aa-4680-409f-8d79-e79b878a786c stroke:#2c3143,stroke-width:4px;\n 16[\"Plot learned trajectory\"];\n 15 -->|output_rds| 16;\n 6fa555f1-b4f2-4e5d-bce1-ed462d8e056d[\"Output\\nCell types & learned trajectory path plot\"];\n 16 --> 6fa555f1-b4f2-4e5d-bce1-ed462d8e056d;\n style 6fa555f1-b4f2-4e5d-bce1-ed462d8e056d stroke:#2c3143,stroke-width:4px;\n 17[\"Monocle3 orderCells\"];\n 15 -->|output_rds| 17;\n 96d10412-3d7f-4970-b541-6f4367fa3f37[\"Output\\nMonocle3 orderCells on input dataset(s): cds3\"];\n 17 --> 96d10412-3d7f-4970-b541-6f4367fa3f37;\n style 96d10412-3d7f-4970-b541-6f4367fa3f37 stroke:#2c3143,stroke-width:4px;\n 18[\"Monocle3 diffExp\"];\n 17 -->|output_rds| 18;\n db1dc457-2d6e-47cc-b7ce-d0a4d23e9498[\"Output\\nDifferential expression of genes - table\"];\n 18 --> db1dc457-2d6e-47cc-b7ce-d0a4d23e9498;\n style db1dc457-2d6e-47cc-b7ce-d0a4d23e9498 stroke:#2c3143,stroke-width:4px;\n 19[\"Plot pseudotime\"];\n 17 -->|output_rds| 19;\n d1b3efd2-0a81-4dad-8652-589c2678fbaa[\"Output\\nPseudotime plot\"];\n 19 --> d1b3efd2-0a81-4dad-8652-589c2678fbaa;\n style d1b3efd2-0a81-4dad-8652-589c2678fbaa stroke:#2c3143,stroke-width:4px;", + "modified": "2024-06-24 00:06:33 +0000", + "name": "Monocle3 workflow", + "path": "topics/single-cell/tutorials/scrna-case_monocle3-trajectories/workflows/Galaxy-Workflow-Monocle3_workflow.ga", + "test_results": null, + "tests": false, + "title": "Monocle3 workflow", + "trs_endpoint": "https://training.galaxyproject.org/training-material/api/ga4gh/trs/v2/tools/single-cell-scrna-case_monocle3-trajectories/versions/galaxy-workflow-monocle3_workflow", + "url": "https://training.galaxyproject.org/training-material/topics/single-cell/tutorials/scrna-case_monocle3-trajectories/workflows/Galaxy-Workflow-Monocle3_workflow.ga", + "wfid": "single-cell-scrna-case_monocle3-trajectories", + "wfname": "galaxy-workflow-monocle3_workflow", + "workflow": "Galaxy-Workflow-Monocle3_workflow.ga" + }, + { + "creators": [ + { + "class": "Organization", + "identifier": "0009-0001-2017-8805", + "name": "Julia Jakiela" + } + ], + "license": "CC-BY-4.0", + "mermaid": "flowchart TD\n 0[\"\u2139\ufe0f Input Dataset\\nAnnData to extract genes & cells annotations\"];\n style 0 stroke:#2c3143,stroke-width:4px;\n 1[\"\u2139\ufe0f Input Dataset\\nAnnData before processing to extract clean expression matrix\"];\n style 1 stroke:#2c3143,stroke-width:4px;\n 2[\"Extract cell annotations\"];\n 0 -->|output| 2;\n 0646798d-3a5d-420a-a7cf-cde6cec722d5[\"Output\\nExtracted cell annotations (obs)\"];\n 2 --> 0646798d-3a5d-420a-a7cf-cde6cec722d5;\n style 0646798d-3a5d-420a-a7cf-cde6cec722d5 stroke:#2c3143,stroke-width:4px;\n 3[\"Extract gene annotations\"];\n 0 -->|output| 3;\n 84162745-68b0-41a5-b06e-a285440ba095[\"Output\\nExtracted gene annotations (var)\"];\n 3 --> 84162745-68b0-41a5-b06e-a285440ba095;\n style 84162745-68b0-41a5-b06e-a285440ba095 stroke:#2c3143,stroke-width:4px;\n 4[\"Extract clean expression matrix\"];\n 1 -->|output| 4;\n 3f03af0c-ac41-4450-b5ba-639a79ea543b[\"Output\\nUnprocessed expression matrix\"];\n 4 --> 3f03af0c-ac41-4450-b5ba-639a79ea543b;\n style 3f03af0c-ac41-4450-b5ba-639a79ea543b stroke:#2c3143,stroke-width:4px;\n 5[\"Filter out macrophages\"];\n 2 -->|obs| 5;\n fb81e9a1-0603-419a-83f5-0c9f94939b16[\"Output\\nCells without macrophages\"];\n 5 --> fb81e9a1-0603-419a-83f5-0c9f94939b16;\n style fb81e9a1-0603-419a-83f5-0c9f94939b16 stroke:#2c3143,stroke-width:4px;\n 6[\"gene_short_name annotation\"];\n 3 -->|var| 6;\n 94a60e6c-cdf7-48e5-8b55-d221cef22688[\"Output\\nGenes table with gene_short_name colname\"];\n 6 --> 94a60e6c-cdf7-48e5-8b55-d221cef22688;\n style 94a60e6c-cdf7-48e5-8b55-d221cef22688 stroke:#2c3143,stroke-width:4px;\n 7[\"Cut cells IDs\"];\n 5 -->|out_file1| 7;\n 34146c30-2666-481f-8d49-a849d5d3aa91[\"Output\\nFiltered cells IDs \"];\n 7 --> 34146c30-2666-481f-8d49-a849d5d3aa91;\n style 34146c30-2666-481f-8d49-a849d5d3aa91 stroke:#2c3143,stroke-width:4px;\n 8[\"Cut genes IDs\"];\n 6 -->|out_file1| 8;\n 8f38fb93-cb30-4fbc-90c5-7f6bd023feea[\"Output\\nGenes IDs\"];\n 8 --> 8f38fb93-cb30-4fbc-90c5-7f6bd023feea;\n style 8f38fb93-cb30-4fbc-90c5-7f6bd023feea stroke:#2c3143,stroke-width:4px;\n 9[\"Filter matrix by cells\"];\n 7 -->|out_file1| 9;\n 4 -->|X| 9;\n f654c045-5fea-43f9-96d6-492c810593d2[\"Output\\nPre-filtered matrix (by cells)\"];\n 9 --> f654c045-5fea-43f9-96d6-492c810593d2;\n style f654c045-5fea-43f9-96d6-492c810593d2 stroke:#2c3143,stroke-width:4px;\n 10[\"Remove duplicate column cells IDs\"];\n 9 -->|out_file1| 10;\n f633c125-a4d0-4fa7-ab87-022349f4ac70[\"Output\\nFiltered matrix (by cells) \"];\n 10 --> f633c125-a4d0-4fa7-ab87-022349f4ac70;\n style f633c125-a4d0-4fa7-ab87-022349f4ac70 stroke:#2c3143,stroke-width:4px;\n 11[\"Transpose matrix\"];\n 10 -->|output| 11;\n 3202091e-1afc-4f50-8922-1f9e50c490fc[\"Output\\nfiltered matrix (by cells) transposed\"];\n 11 --> 3202091e-1afc-4f50-8922-1f9e50c490fc;\n style 3202091e-1afc-4f50-8922-1f9e50c490fc stroke:#2c3143,stroke-width:4px;\n 12[\"Filter matrix by genes\"];\n 8 -->|out_file1| 12;\n 11 -->|out_file| 12;\n 6c23cbd5-a1b5-428e-8ff9-ebac4893ad88[\"Output\\nPre-filtered matrix (by cells & genes)\"];\n 12 --> 6c23cbd5-a1b5-428e-8ff9-ebac4893ad88;\n style 6c23cbd5-a1b5-428e-8ff9-ebac4893ad88 stroke:#2c3143,stroke-width:4px;\n 13[\"Remove duplicate column genes IDs\"];\n 12 -->|out_file1| 13;\n 25839326-0b5d-4e74-91ec-24e22c08f7c5[\"Output\\nFiltered matrix (by cells & genes)\"];\n 13 --> 25839326-0b5d-4e74-91ec-24e22c08f7c5;\n style 25839326-0b5d-4e74-91ec-24e22c08f7c5 stroke:#2c3143,stroke-width:4px;\n 14[\"Monocle3 create\"];\n 5 -->|out_file1| 14;\n 13 -->|output| 14;\n 6 -->|out_file1| 14;\n f8c5589c-a70d-416e-a9de-ebc987f61fc3[\"Output\\nMonocle3 create on input dataset(s): cds3\"];\n 14 --> f8c5589c-a70d-416e-a9de-ebc987f61fc3;\n style f8c5589c-a70d-416e-a9de-ebc987f61fc3 stroke:#2c3143,stroke-width:4px;\n 15[\"Monocle3 preprocess\"];\n 14 -->|output_rds| 15;\n c134942f-60e7-4cd0-8c32-da4a303b879d[\"Output\\nMonocle3 preprocess on input dataset(s): cds3\"];\n 15 --> c134942f-60e7-4cd0-8c32-da4a303b879d;\n style c134942f-60e7-4cd0-8c32-da4a303b879d stroke:#2c3143,stroke-width:4px;\n 16[\"Monocle3 reduceDim\"];\n 15 -->|output_rds| 16;\n 2c9fb108-2a1c-41df-a8d8-81a18b66d2a1[\"Output\\nMonocle3 reduceDim on input dataset(s): cds3\"];\n 16 --> 2c9fb108-2a1c-41df-a8d8-81a18b66d2a1;\n style 2c9fb108-2a1c-41df-a8d8-81a18b66d2a1 stroke:#2c3143,stroke-width:4px;\n 17[\"Monocle3 partition\"];\n 16 -->|output_rds| 17;\n 93371c10-6583-47fc-830f-9a90dc980766[\"Output\\nMonocle3 partition on input dataset(s): cds3\"];\n 17 --> 93371c10-6583-47fc-830f-9a90dc980766;\n style 93371c10-6583-47fc-830f-9a90dc980766 stroke:#2c3143,stroke-width:4px;\n 18[\"Plot cell type\"];\n 16 -->|output_rds| 18;\n f096d060-82e8-4847-9e8f-b22c9288f565[\"Output\\nCell type plot\"];\n 18 --> f096d060-82e8-4847-9e8f-b22c9288f565;\n style f096d060-82e8-4847-9e8f-b22c9288f565 stroke:#2c3143,stroke-width:4px;\n 19[\"Plot genotype\"];\n 16 -->|output_rds| 19;\n aab28bb1-e2e7-4b7d-9b2b-1cab64bea287[\"Output\\nGenotype plot\"];\n 19 --> aab28bb1-e2e7-4b7d-9b2b-1cab64bea287;\n style aab28bb1-e2e7-4b7d-9b2b-1cab64bea287 stroke:#2c3143,stroke-width:4px;\n 20[\"Plot batch\"];\n 16 -->|output_rds| 20;\n 9b362eee-9238-47c5-a4c2-85f3995430f4[\"Output\\nBatch plot\"];\n 20 --> 9b362eee-9238-47c5-a4c2-85f3995430f4;\n style 9b362eee-9238-47c5-a4c2-85f3995430f4 stroke:#2c3143,stroke-width:4px;\n 21[\"Plot sex\"];\n 16 -->|output_rds| 21;\n c2a1e104-ef2d-4b66-ac2b-bf0251714434[\"Output\\nSex plot\"];\n 21 --> c2a1e104-ef2d-4b66-ac2b-bf0251714434;\n style c2a1e104-ef2d-4b66-ac2b-bf0251714434 stroke:#2c3143,stroke-width:4px;\n 22[\"Monocle3 top markers\"];\n 17 -->|output_rds| 22;\n 23[\"Plot genes\"];\n 17 -->|output_rds| 23;\n e8867379-82c6-4c32-a2d3-0b24670269ae[\"Output\\nGene expression plot\"];\n 23 --> e8867379-82c6-4c32-a2d3-0b24670269ae;\n style e8867379-82c6-4c32-a2d3-0b24670269ae stroke:#2c3143,stroke-width:4px;\n 24[\"Plot partition\"];\n 17 -->|output_rds| 24;\n 3f7b0a7c-85d6-430b-8385-fa9a76385c0e[\"Output\\nPartition plot\"];\n 24 --> 3f7b0a7c-85d6-430b-8385-fa9a76385c0e;\n style 3f7b0a7c-85d6-430b-8385-fa9a76385c0e stroke:#2c3143,stroke-width:4px;\n 25[\"Plot cluster\"];\n 17 -->|output_rds| 25;\n 40b65826-f73e-4054-a56d-b241985d6996[\"Output\\nCluster plot\"];\n 25 --> 40b65826-f73e-4054-a56d-b241985d6996;\n style 40b65826-f73e-4054-a56d-b241985d6996 stroke:#2c3143,stroke-width:4px;\n 26[\"Monocle3 learnGraph\"];\n 17 -->|output_rds| 26;\n 294b1a49-cec6-49d0-bcd4-3a954da67bec[\"Output\\nMonocle3 learnGraph on input dataset(s): cds3\"];\n 26 --> 294b1a49-cec6-49d0-bcd4-3a954da67bec;\n style 294b1a49-cec6-49d0-bcd4-3a954da67bec stroke:#2c3143,stroke-width:4px;\n 27[\"Plot learned trajectory\"];\n 26 -->|output_rds| 27;\n c85ddefe-8eaf-4d21-93ed-ca0859286bf0[\"Output\\nCell types & learned trajectory path plot\"];\n 27 --> c85ddefe-8eaf-4d21-93ed-ca0859286bf0;\n style c85ddefe-8eaf-4d21-93ed-ca0859286bf0 stroke:#2c3143,stroke-width:4px;\n 28[\"Monocle3 orderCells\"];\n 26 -->|output_rds| 28;\n 5aea5752-b767-4fc8-8741-1f4d1c659a41[\"Output\\nMonocle3 orderCells on input dataset(s): cds3\"];\n 28 --> 5aea5752-b767-4fc8-8741-1f4d1c659a41;\n style 5aea5752-b767-4fc8-8741-1f4d1c659a41 stroke:#2c3143,stroke-width:4px;\n 29[\"Monocle3 diffExp\"];\n 28 -->|output_rds| 29;\n 3c298624-9f06-4e52-b2a8-fb834b87c296[\"Output\\nDifferential expression of genes - table\"];\n 29 --> 3c298624-9f06-4e52-b2a8-fb834b87c296;\n style 3c298624-9f06-4e52-b2a8-fb834b87c296 stroke:#2c3143,stroke-width:4px;\n 30[\"Plot pseudotime\"];\n 28 -->|output_rds| 30;\n 813d15bb-0771-4aed-9226-4874c1ddba59[\"Output\\nPseudotime plot\"];\n 30 --> 813d15bb-0771-4aed-9226-4874c1ddba59;\n style 813d15bb-0771-4aed-9226-4874c1ddba59 stroke:#2c3143,stroke-width:4px;", + "modified": "2024-06-24 00:06:33 +0000", + "name": "Trajectory analysis using Monocle3 - full tutorial workflow", + "path": "topics/single-cell/tutorials/scrna-case_monocle3-trajectories/workflows/Trajectory-analysis-using-Monocle3---full-tutorial-workflow.ga", + "test_results": null, + "tests": true, + "title": "Trajectory analysis using Monocle3 - full tutorial workflow", + "trs_endpoint": "https://training.galaxyproject.org/training-material/api/ga4gh/trs/v2/tools/single-cell-scrna-case_monocle3-trajectories/versions/trajectory-analysis-using-monocle3---full-tutorial-workflow", + "url": "https://training.galaxyproject.org/training-material/topics/single-cell/tutorials/scrna-case_monocle3-trajectories/workflows/Trajectory-analysis-using-Monocle3---full-tutorial-workflow.ga", + "wfid": "single-cell-scrna-case_monocle3-trajectories", + "wfname": "trajectory-analysis-using-monocle3---full-tutorial-workflow", + "workflow": "Trajectory-analysis-using-Monocle3---full-tutorial-workflow.ga" + } + ], + "zenodo_link": "https://zenodo.org/records/10397366" + }, + { + "admin_install": { + "install_repository_dependencies": true, + "install_resolver_dependencies": true, + "install_tool_dependencies": true, + "tools": [ + { + "name": "music_construct_eset", + "owner": "bgruening", + "revisions": "282819d09a4f", + "tool_panel_section_label": "Single-cell", + "tool_shed_url": "https://toolshed.g2.bx.psu.edu/" + }, + { + "name": "music_manipulate_eset", + "owner": "bgruening", + "revisions": "22232092be53", + "tool_panel_section_label": "Single-cell", + "tool_shed_url": "https://toolshed.g2.bx.psu.edu/" + }, + { + "name": "text_processing", + "owner": "bgruening", + "revisions": "d698c222f354", + "tool_panel_section_label": "Text Manipulation", + "tool_shed_url": "https://toolshed.g2.bx.psu.edu/" + }, + { + "name": "text_processing", + "owner": "bgruening", + "revisions": "d698c222f354", + "tool_panel_section_label": "Text Manipulation", + "tool_shed_url": "https://toolshed.g2.bx.psu.edu/" + }, + { + "name": "text_processing", + "owner": "bgruening", + "revisions": 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Info #171: KMP_AFFINITY: OS proc 61 maps to package 61 \nOMP: Info #171: KMP_AFFINITY: OS proc 62 maps to package 62 \nOMP: Info #171: KMP_AFFINITY: OS proc 63 maps to package 63 \nOMP: Info #171: KMP_AFFINITY: OS proc 64 maps to package 64 \nOMP: Info #171: KMP_AFFINITY: OS proc 65 maps to package 65 \nOMP: Info #171: KMP_AFFINITY: OS proc 66 maps to package 66 \nOMP: Info #171: KMP_AFFINITY: OS proc 67 maps to package 67 \nOMP: Info #171: KMP_AFFINITY: OS proc 68 maps to package 68 \nOMP: Info #171: KMP_AFFINITY: OS proc 69 maps to package 69 \nOMP: Info #171: KMP_AFFINITY: OS proc 70 maps to package 70 \nOMP: Info #171: KMP_AFFINITY: OS proc 71 maps to package 71 \nOMP: Info #171: KMP_AFFINITY: OS proc 72 maps to package 72 \nOMP: Info #171: KMP_AFFINITY: OS proc 73 maps to package 73 \nOMP: Info #171: KMP_AFFINITY: OS proc 74 maps to package 74 \nOMP: Info #171: KMP_AFFINITY: OS proc 75 maps to package 75 \nOMP: Info #171: KMP_AFFINITY: OS proc 76 maps to package 76 \nOMP: Info #171: KMP_AFFINITY: OS proc 77 maps to package 77 \nOMP: Info #171: KMP_AFFINITY: OS proc 78 maps to package 78 \nOMP: Info #171: KMP_AFFINITY: OS proc 79 maps to package 79 \nOMP: Info #171: KMP_AFFINITY: OS proc 80 maps to package 80 \nOMP: Info #171: KMP_AFFINITY: OS proc 81 maps to package 81 \nOMP: Info #171: KMP_AFFINITY: OS proc 82 maps to package 82 \nOMP: Info #171: KMP_AFFINITY: OS proc 83 maps to package 83 \nOMP: Info #171: KMP_AFFINITY: OS proc 84 maps to package 84 \nOMP: Info #171: KMP_AFFINITY: OS proc 85 maps to package 85 \nOMP: Info #171: KMP_AFFINITY: OS proc 86 maps to package 86 \nOMP: Info #171: KMP_AFFINITY: OS proc 87 maps to package 87 \nOMP: Info #171: KMP_AFFINITY: OS proc 88 maps to package 88 \nOMP: Info #171: KMP_AFFINITY: OS proc 89 maps to package 89 \nOMP: Info #171: KMP_AFFINITY: OS proc 90 maps to package 90 \nOMP: Info #171: KMP_AFFINITY: OS proc 91 maps to package 91 \nOMP: Info #171: KMP_AFFINITY: OS proc 92 maps to package 92 \nOMP: Info #171: KMP_AFFINITY: OS proc 93 maps to package 93 \nOMP: Info #171: KMP_AFFINITY: OS proc 94 maps to package 94 \nOMP: Info #171: KMP_AFFINITY: OS proc 95 maps to package 95 \nOMP: Info #171: KMP_AFFINITY: OS proc 96 maps to package 96 \nOMP: Info #171: KMP_AFFINITY: OS proc 97 maps to package 97 \nOMP: Info #171: KMP_AFFINITY: OS proc 98 maps to package 98 \nOMP: Info #171: KMP_AFFINITY: OS proc 99 maps to package 99 \nOMP: Info #171: KMP_AFFINITY: OS proc 100 maps to package 100 \nOMP: Info #171: KMP_AFFINITY: OS proc 101 maps to package 101 \nOMP: Info #171: KMP_AFFINITY: OS proc 102 maps to package 102 \nOMP: Info #171: KMP_AFFINITY: OS proc 103 maps to package 103 \nOMP: Info #171: KMP_AFFINITY: OS proc 104 maps to package 104 \nOMP: Info #171: KMP_AFFINITY: OS proc 105 maps to package 105 \nOMP: Info #171: KMP_AFFINITY: OS proc 106 maps to package 106 \nOMP: Info #171: KMP_AFFINITY: OS proc 107 maps to package 107 \nOMP: Info #171: KMP_AFFINITY: OS proc 108 maps to package 108 \nOMP: Info #171: KMP_AFFINITY: OS proc 109 maps to package 109 \nOMP: Info #171: KMP_AFFINITY: OS proc 110 maps to package 110 \nOMP: Info #171: KMP_AFFINITY: OS proc 111 maps to package 111 \nOMP: Info #171: KMP_AFFINITY: OS proc 112 maps to package 112 \nOMP: Info #171: KMP_AFFINITY: OS proc 113 maps to package 113 \nOMP: Info #171: KMP_AFFINITY: OS proc 114 maps to package 114 \nOMP: Info #171: KMP_AFFINITY: OS proc 115 maps to package 115 \nOMP: Info #171: KMP_AFFINITY: OS proc 116 maps to package 116 \nOMP: Info #171: KMP_AFFINITY: OS proc 117 maps to package 117 \nOMP: Info #171: KMP_AFFINITY: OS proc 118 maps to package 118 \nOMP: Info #171: KMP_AFFINITY: OS proc 119 maps to package 119 \nOMP: Info #171: KMP_AFFINITY: OS proc 120 maps to package 120 \nOMP: Info #171: KMP_AFFINITY: OS proc 121 maps to package 121 \nOMP: Info #171: KMP_AFFINITY: OS proc 122 maps to package 122 \nOMP: Info #171: KMP_AFFINITY: OS proc 123 maps to package 123 \nOMP: Info #171: KMP_AFFINITY: OS proc 124 maps to package 124 \nOMP: Info #250: KMP_AFFINITY: pid 2936157 tid 2936157 thread 0 bound to OS proc set 0\nUsing TensorFlow backend.\n/usr/local/tools/_conda/envs/mulled-v1-26f90eb9c8055941081cb6eaef4d0dffb23aadd383641e5d6e58562e0bb08f59/lib/python3.6/site-packages/sklearn/externals/joblib/__init__.py:15: DeprecationWarning: sklearn.externals.joblib is deprecated in 0.21 and will be removed in 0.23. Please import this functionality directly from joblib, which can be installed with: pip install joblib. If this warning is raised when loading pickled models, you may need to re-serialize those models with scikit-learn 0.21+.\n warnings.warn(msg, category=DeprecationWarning)\n", + "stdout": "", + "tool_id": "toolshed.g2.bx.psu.edu/repos/bgruening/sklearn_svm_classifier/sklearn_svm_classifier/1.0.8.1", + "tool_stderr": "OMP: Info #212: KMP_AFFINITY: decoding x2APIC ids.\nOMP: Info #210: KMP_AFFINITY: Affinity capable, using global cpuid leaf 11 info\nOMP: Info #154: KMP_AFFINITY: Initial OS proc set respected: 0-124\nOMP: Info #156: KMP_AFFINITY: 125 available OS procs\nOMP: Info #157: KMP_AFFINITY: Uniform topology\nOMP: Info #179: KMP_AFFINITY: 125 packages x 1 cores/pkg x 1 threads/core (125 total cores)\nOMP: Info #214: KMP_AFFINITY: OS proc to physical thread map:\nOMP: Info #171: KMP_AFFINITY: OS proc 0 maps to package 0 \nOMP: Info #171: KMP_AFFINITY: OS proc 1 maps to package 1 \nOMP: Info #171: KMP_AFFINITY: OS proc 2 maps to package 2 \nOMP: Info #171: KMP_AFFINITY: OS proc 3 maps to package 3 \nOMP: Info #171: KMP_AFFINITY: OS proc 4 maps to package 4 \nOMP: Info #171: KMP_AFFINITY: OS proc 5 maps to package 5 \nOMP: Info #171: KMP_AFFINITY: OS proc 6 maps to package 6 \nOMP: Info #171: KMP_AFFINITY: OS proc 7 maps to package 7 \nOMP: Info #171: KMP_AFFINITY: OS proc 8 maps to package 8 \nOMP: Info #171: KMP_AFFINITY: OS proc 9 maps to package 9 \nOMP: Info #171: KMP_AFFINITY: OS proc 10 maps to package 10 \nOMP: Info #171: KMP_AFFINITY: OS proc 11 maps to package 11 \nOMP: Info #171: KMP_AFFINITY: OS proc 12 maps to package 12 \nOMP: Info #171: KMP_AFFINITY: OS proc 13 maps to package 13 \nOMP: Info #171: KMP_AFFINITY: OS proc 14 maps to package 14 \nOMP: Info #171: KMP_AFFINITY: OS proc 15 maps to package 15 \nOMP: Info #171: KMP_AFFINITY: OS proc 16 maps to package 16 \nOMP: Info #171: KMP_AFFINITY: OS proc 17 maps to package 17 \nOMP: Info #171: KMP_AFFINITY: OS proc 18 maps to package 18 \nOMP: Info #171: KMP_AFFINITY: OS proc 19 maps to package 19 \nOMP: Info #171: KMP_AFFINITY: OS proc 20 maps to package 20 \nOMP: Info #171: KMP_AFFINITY: OS proc 21 maps to package 21 \nOMP: Info #171: KMP_AFFINITY: OS proc 22 maps to package 22 \nOMP: Info #171: KMP_AFFINITY: OS proc 23 maps to package 23 \nOMP: Info #171: KMP_AFFINITY: OS proc 24 maps to package 24 \nOMP: Info #171: KMP_AFFINITY: OS proc 25 maps to package 25 \nOMP: Info #171: KMP_AFFINITY: OS proc 26 maps to package 26 \nOMP: Info #171: KMP_AFFINITY: OS proc 27 maps to package 27 \nOMP: Info #171: KMP_AFFINITY: OS proc 28 maps to package 28 \nOMP: Info #171: KMP_AFFINITY: OS proc 29 maps to package 29 \nOMP: Info #171: KMP_AFFINITY: OS proc 30 maps to package 30 \nOMP: Info #171: KMP_AFFINITY: OS proc 31 maps to package 31 \nOMP: Info #171: KMP_AFFINITY: OS proc 32 maps to package 32 \nOMP: Info #171: KMP_AFFINITY: OS proc 33 maps to package 33 \nOMP: Info #171: KMP_AFFINITY: OS proc 34 maps to package 34 \nOMP: Info #171: KMP_AFFINITY: OS proc 35 maps to package 35 \nOMP: Info #171: KMP_AFFINITY: OS proc 36 maps to package 36 \nOMP: Info #171: KMP_AFFINITY: OS proc 37 maps to package 37 \nOMP: Info #171: KMP_AFFINITY: OS proc 38 maps to package 38 \nOMP: Info #171: KMP_AFFINITY: OS proc 39 maps to package 39 \nOMP: Info #171: KMP_AFFINITY: OS proc 40 maps to package 40 \nOMP: Info #171: KMP_AFFINITY: OS proc 41 maps to package 41 \nOMP: Info #171: KMP_AFFINITY: OS proc 42 maps to package 42 \nOMP: Info #171: KMP_AFFINITY: OS proc 43 maps to package 43 \nOMP: Info #171: KMP_AFFINITY: OS proc 44 maps to package 44 \nOMP: Info #171: KMP_AFFINITY: OS proc 45 maps to package 45 \nOMP: Info #171: KMP_AFFINITY: OS proc 46 maps to package 46 \nOMP: Info #171: KMP_AFFINITY: OS proc 47 maps to package 47 \nOMP: Info #171: KMP_AFFINITY: OS proc 48 maps to package 48 \nOMP: Info #171: KMP_AFFINITY: OS proc 49 maps to package 49 \nOMP: Info #171: KMP_AFFINITY: OS proc 50 maps to package 50 \nOMP: Info #171: KMP_AFFINITY: OS proc 51 maps to package 51 \nOMP: Info #171: KMP_AFFINITY: OS proc 52 maps to package 52 \nOMP: Info #171: KMP_AFFINITY: OS proc 53 maps to package 53 \nOMP: Info #171: KMP_AFFINITY: OS proc 54 maps to package 54 \nOMP: Info #171: KMP_AFFINITY: OS proc 55 maps to package 55 \nOMP: Info #171: KMP_AFFINITY: OS proc 56 maps to package 56 \nOMP: Info #171: KMP_AFFINITY: OS proc 57 maps to package 57 \nOMP: Info #171: KMP_AFFINITY: OS proc 58 maps to package 58 \nOMP: Info #171: KMP_AFFINITY: OS proc 59 maps to package 59 \nOMP: Info #171: KMP_AFFINITY: OS proc 60 maps to package 60 \nOMP: Info #171: KMP_AFFINITY: OS proc 61 maps to package 61 \nOMP: Info #171: KMP_AFFINITY: OS proc 62 maps to package 62 \nOMP: Info #171: KMP_AFFINITY: OS proc 63 maps to package 63 \nOMP: Info #171: KMP_AFFINITY: OS proc 64 maps to package 64 \nOMP: Info #171: KMP_AFFINITY: OS proc 65 maps to package 65 \nOMP: Info #171: KMP_AFFINITY: OS proc 66 maps to package 66 \nOMP: Info #171: KMP_AFFINITY: OS proc 67 maps to package 67 \nOMP: Info #171: KMP_AFFINITY: OS proc 68 maps to package 68 \nOMP: Info #171: KMP_AFFINITY: OS proc 69 maps to package 69 \nOMP: Info #171: KMP_AFFINITY: OS proc 70 maps to package 70 \nOMP: Info #171: KMP_AFFINITY: OS proc 71 maps to package 71 \nOMP: Info #171: KMP_AFFINITY: OS proc 72 maps to package 72 \nOMP: Info #171: KMP_AFFINITY: OS proc 73 maps to package 73 \nOMP: Info #171: KMP_AFFINITY: OS proc 74 maps to package 74 \nOMP: Info #171: KMP_AFFINITY: OS proc 75 maps to package 75 \nOMP: Info #171: KMP_AFFINITY: OS proc 76 maps to package 76 \nOMP: Info #171: KMP_AFFINITY: OS proc 77 maps to package 77 \nOMP: Info #171: KMP_AFFINITY: OS proc 78 maps to package 78 \nOMP: Info #171: KMP_AFFINITY: OS proc 79 maps to package 79 \nOMP: Info #171: KMP_AFFINITY: OS proc 80 maps to package 80 \nOMP: Info #171: KMP_AFFINITY: OS proc 81 maps to package 81 \nOMP: Info #171: KMP_AFFINITY: OS proc 82 maps to package 82 \nOMP: Info #171: KMP_AFFINITY: OS proc 83 maps to package 83 \nOMP: Info #171: KMP_AFFINITY: OS proc 84 maps to package 84 \nOMP: Info #171: KMP_AFFINITY: OS proc 85 maps to package 85 \nOMP: Info #171: KMP_AFFINITY: OS proc 86 maps to package 86 \nOMP: Info #171: KMP_AFFINITY: OS proc 87 maps to package 87 \nOMP: Info #171: KMP_AFFINITY: OS proc 88 maps to package 88 \nOMP: Info #171: KMP_AFFINITY: OS proc 89 maps to package 89 \nOMP: Info #171: KMP_AFFINITY: OS proc 90 maps to package 90 \nOMP: Info #171: KMP_AFFINITY: OS proc 91 maps to package 91 \nOMP: Info #171: KMP_AFFINITY: OS proc 92 maps to package 92 \nOMP: Info #171: KMP_AFFINITY: OS proc 93 maps to package 93 \nOMP: Info #171: KMP_AFFINITY: OS proc 94 maps to package 94 \nOMP: Info #171: KMP_AFFINITY: OS proc 95 maps to package 95 \nOMP: Info #171: KMP_AFFINITY: OS proc 96 maps to package 96 \nOMP: Info #171: KMP_AFFINITY: OS proc 97 maps to package 97 \nOMP: Info #171: KMP_AFFINITY: OS proc 98 maps to package 98 \nOMP: Info #171: KMP_AFFINITY: OS proc 99 maps to package 99 \nOMP: Info #171: KMP_AFFINITY: OS proc 100 maps to package 100 \nOMP: Info #171: KMP_AFFINITY: OS proc 101 maps to package 101 \nOMP: Info #171: KMP_AFFINITY: OS proc 102 maps to package 102 \nOMP: Info #171: KMP_AFFINITY: OS proc 103 maps to package 103 \nOMP: Info #171: KMP_AFFINITY: OS proc 104 maps to package 104 \nOMP: Info #171: KMP_AFFINITY: OS proc 105 maps to package 105 \nOMP: Info #171: KMP_AFFINITY: OS proc 106 maps to package 106 \nOMP: Info #171: KMP_AFFINITY: OS proc 107 maps to package 107 \nOMP: Info #171: KMP_AFFINITY: OS proc 108 maps to package 108 \nOMP: Info #171: KMP_AFFINITY: OS proc 109 maps to package 109 \nOMP: Info #171: KMP_AFFINITY: OS proc 110 maps to package 110 \nOMP: Info #171: KMP_AFFINITY: OS proc 111 maps to package 111 \nOMP: Info #171: KMP_AFFINITY: OS proc 112 maps to package 112 \nOMP: Info #171: KMP_AFFINITY: OS proc 113 maps to package 113 \nOMP: Info #171: KMP_AFFINITY: OS proc 114 maps to package 114 \nOMP: Info #171: KMP_AFFINITY: OS proc 115 maps to package 115 \nOMP: Info #171: KMP_AFFINITY: OS proc 116 maps to package 116 \nOMP: Info #171: KMP_AFFINITY: OS proc 117 maps to package 117 \nOMP: Info #171: KMP_AFFINITY: OS proc 118 maps to package 118 \nOMP: Info #171: KMP_AFFINITY: OS proc 119 maps to package 119 \nOMP: Info #171: KMP_AFFINITY: OS proc 120 maps to package 120 \nOMP: Info #171: KMP_AFFINITY: OS proc 121 maps to package 121 \nOMP: Info #171: KMP_AFFINITY: OS proc 122 maps to package 122 \nOMP: Info #171: KMP_AFFINITY: OS proc 123 maps to package 123 \nOMP: Info #171: KMP_AFFINITY: OS proc 124 maps to package 124 \nOMP: Info #250: KMP_AFFINITY: pid 2936157 tid 2936157 thread 0 bound to OS proc set 0\nUsing TensorFlow backend.\n/usr/local/tools/_conda/envs/mulled-v1-26f90eb9c8055941081cb6eaef4d0dffb23aadd383641e5d6e58562e0bb08f59/lib/python3.6/site-packages/sklearn/externals/joblib/__init__.py:15: DeprecationWarning: sklearn.externals.joblib is deprecated in 0.21 and will be removed in 0.23. Please import this functionality directly from joblib, which can be installed with: pip install joblib. If this warning is raised when loading pickled models, you may need to re-serialize those models with scikit-learn 0.21+.\n warnings.warn(msg, category=DeprecationWarning)\n", + "tool_stdout": "", + "traceback": null, + "update_time": "2023-05-23T11:03:10.618603", + "user_email": "hxr@informatik.uni-freiburg.de" + } + ], + "model_class": "WorkflowInvocationStep", + "order_index": 2, + "output_collections": {}, + "outputs": { + "outfile_fit": { + "id": "4838ba20a6d86765b565f48d5ac913b1", + "src": "hda", + "uuid": "1a17ebe9-c0e0-4ebd-bae0-5159331683bb" + } + }, + "state": "scheduled", + "subworkflow": null, + "subworkflow_invocation_id": null, + "update_time": "2023-05-23T11:03:10.590397", + "workflow_step_id": "3500dc3fab19a665", + "workflow_step_label": null, + "workflow_step_uuid": "159f5d0f-2ff6-46f3-8e42-7b7c1f9c346e" + }, + "3. Unnamed step": { + "action": null, + "id": "06d0cb6c722a3ad0", + "job_id": "11ac94870d0bb33a7b20a18462093261", + "jobs": [ + { + "command_line": "python '/data/jwd02f/main/060/156/60156805/configs/tmpsg_iolk6' '/data/jwd02f/main/060/156/60156805/configs/tmpbg5s306r'", + "command_version": "1.0.8.1", + "create_time": "2023-05-23T10:59:49.663928", + "dependencies": [ + { + "cacheable": false, + "dependency_resolver": { + "auto_init": true, + "auto_install": false, + "can_uninstall_dependencies": true, + "ensure_channels": "conda-forge,bioconda", + "model_class": "CondaDependencyResolver", + "prefix": "/usr/local/tools/_conda", + "read_only": false, + "resolver_type": "conda", + "resolves_simple_dependencies": true, + "use_local": false, + "versionless": false + }, + "dependency_type": "conda", + "environment_path": "/usr/local/tools/_conda/envs/mulled-v1-26f90eb9c8055941081cb6eaef4d0dffb23aadd383641e5d6e58562e0bb08f59", + "exact": true, + "model_class": "MergedCondaDependency", + "name": "python", + "version": "3.6" + }, + { + "cacheable": false, + "dependency_resolver": { + "auto_init": true, + "auto_install": false, + "can_uninstall_dependencies": true, + "ensure_channels": "conda-forge,bioconda", + "model_class": "CondaDependencyResolver", + "prefix": "/usr/local/tools/_conda", + "read_only": false, + "resolver_type": "conda", + "resolves_simple_dependencies": true, + "use_local": false, + "versionless": false + }, + "dependency_type": "conda", + "environment_path": "/usr/local/tools/_conda/envs/mulled-v1-26f90eb9c8055941081cb6eaef4d0dffb23aadd383641e5d6e58562e0bb08f59", + "exact": true, + "model_class": "MergedCondaDependency", + "name": "Galaxy-ML", + "version": "0.8.1" + } + ], + "exit_code": 0, + "external_id": "43631439", + "galaxy_version": "23.0", + "history_id": "2507553dd7587c83", + "id": "11ac94870d0bb33a7b20a18462093261", + "inputs": { + "infile_data": { + "id": "4838ba20a6d867654de1bc22fefb9f52", + "src": "hda", + "uuid": 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11 -->|signal_unique_str1| 19;\n 20[\"merge coverage unique strand 2\"];\n 8 -->|signal_unique_str2| 20;\n 11 -->|signal_unique_str2| 20;\n 21[\"Combine FastQC results\"];\n 13 -->|text_file| 21;\n 8d0ce9ee-e4e4-4c0c-8261-420ce756ecfd[\"Output\\nmultiqc_fastqc_html\"];\n 21 --> 8d0ce9ee-e4e4-4c0c-8261-420ce756ecfd;\n style 8d0ce9ee-e4e4-4c0c-8261-420ce756ecfd stroke:#2c3143,stroke-width:4px;\n 22[\"Combine STAR Results\"];\n 15 -->|output| 22;\n 204e3f6c-6f54-46f0-b07c-1f31113265e7[\"Output\\nmultiqc_star_html\"];\n 22 --> 204e3f6c-6f54-46f0-b07c-1f31113265e7;\n style 204e3f6c-6f54-46f0-b07c-1f31113265e7 stroke:#2c3143,stroke-width:4px;\n 23[\"Remove statistics from STAR counts\"];\n 16 -->|output| 23;\n 24[\"Determine library strandness with STAR\"];\n 16 -->|output| 24;\n fe7b84dd-4466-4fe7-94a8-408f4ac7ed1a[\"Output\\nmultiqc_star_counts_html\"];\n 24 --> fe7b84dd-4466-4fe7-94a8-408f4ac7ed1a;\n style fe7b84dd-4466-4fe7-94a8-408f4ac7ed1a stroke:#2c3143,stroke-width:4px;\n 25[\"merge counts from featureCounts\"];\n 12 -->|output_short| 25;\n 17 -->|output_short| 25;\n c82388f8-cb09-4fdf-8a0e-03cdad579f37[\"Output\\nfeatureCounts\"];\n 25 --> c82388f8-cb09-4fdf-8a0e-03cdad579f37;\n style c82388f8-cb09-4fdf-8a0e-03cdad579f37 stroke:#2c3143,stroke-width:4px;\n 26[\"merge featureCounts summary\"];\n 12 -->|output_summary| 26;\n 17 -->|output_summary| 26;\n 27[\"Determine library strandness with Infer Experiment\"];\n 18 -->|output| 27;\n 7 -->|bed_file| 27;\n 940ec3ec-dd2e-4d50-bbc4-756945eb16b2[\"Output\\ninferexperiment\"];\n 27 --> 940ec3ec-dd2e-4d50-bbc4-756945eb16b2;\n style 940ec3ec-dd2e-4d50-bbc4-756945eb16b2 stroke:#2c3143,stroke-width:4px;\n 28[\"Read Distribution\"];\n 18 -->|output| 28;\n 7 -->|bed_file| 28;\n 29[\"Compute read distribution statistics\"];\n 18 -->|output| 29;\n 7 -->|bed_file| 29;\n 30[\"sample BAM\"];\n 18 -->|output| 30;\n 31[\"Get reads number per chromosome\"];\n 18 -->|output| 31;\n 32[\"Remove duplicates\"];\n 18 -->|output| 32;\n 33[\"Determine library strandness with STAR coverage\"];\n 19 -->|output| 33;\n 20 -->|output| 33;\n 2 -->|output| 33;\n 89e1b053-03c2-467a-95a0-d2dc404670ec[\"Output\\npgt\"];\n 33 --> 89e1b053-03c2-467a-95a0-d2dc404670ec;\n style 89e1b053-03c2-467a-95a0-d2dc404670ec stroke:#2c3143,stroke-width:4px;\n 34[\"Select unstranded counts\"];\n 23 -->|outfile| 34;\n bce755be-ac3b-4346-9ac5-1128a287bf00[\"Output\\ncounts_from_star\"];\n 34 --> bce755be-ac3b-4346-9ac5-1128a287bf00;\n style bce755be-ac3b-4346-9ac5-1128a287bf00 stroke:#2c3143,stroke-width:4px;\n 35[\"Sort counts to get gene with highest count on feature Counts\"];\n 25 -->|output| 35;\n 6aeb4dd1-445f-4c66-b1ce-4bb8faac53db[\"Output\\nfeatureCounts_sorted\"];\n 35 --> 6aeb4dd1-445f-4c66-b1ce-4bb8faac53db;\n style 6aeb4dd1-445f-4c66-b1ce-4bb8faac53db stroke:#2c3143,stroke-width:4px;\n 36[\"Combine read asignments statistics\"];\n 26 -->|output| 36;\n fc72242a-f23c-4ceb-9a8b-5280343ea5d6[\"Output\\nmultiqc_featureCounts_html\"];\n 36 --> fc72242a-f23c-4ceb-9a8b-5280343ea5d6;\n style fc72242a-f23c-4ceb-9a8b-5280343ea5d6 stroke:#2c3143,stroke-width:4px;\n 37[\"Combine read distribution on known features\"];\n 29 -->|output| 37;\n 07dca732-0ac7-432e-9e61-2b77f921a23b[\"Output\\nmultiqc_read_distrib\"];\n 37 --> 07dca732-0ac7-432e-9e61-2b77f921a23b;\n style 07dca732-0ac7-432e-9e61-2b77f921a23b stroke:#2c3143,stroke-width:4px;\n 38[\"Get gene body coverage\"];\n 30 -->|outputsam| 38;\n 7 -->|bed_file| 38;\n 39[\"Combine results on reads per chromosome\"];\n 31 -->|output| 39;\n 7bfa8ae7-8ffd-46a1-a56e-815ed2c9f1cf[\"Output\\nmultiqc_reads_per_chrom\"];\n 39 --> 7bfa8ae7-8ffd-46a1-a56e-815ed2c9f1cf;\n style 7bfa8ae7-8ffd-46a1-a56e-815ed2c9f1cf stroke:#2c3143,stroke-width:4px;\n 40[\"Combine results of duplicate reads\"];\n 32 -->|metrics_file| 40;\n 66553d0f-e851-458b-82c2-f9b30e394bac[\"Output\\nmultiqc_dup\"];\n 40 --> 66553d0f-e851-458b-82c2-f9b30e394bac;\n style 66553d0f-e851-458b-82c2-f9b30e394bac stroke:#2c3143,stroke-width:4px;\n 41[\"Sort counts to get gene with highest count on STAR\"];\n 34 -->|out_file1| 41;\n 383df008-0ccb-4d67-98dd-33fa5e2db81e[\"Output\\ncounts_from_star_sorted\"];\n 41 --> 383df008-0ccb-4d67-98dd-33fa5e2db81e;\n style 383df008-0ccb-4d67-98dd-33fa5e2db81e stroke:#2c3143,stroke-width:4px;\n 42[\"Combine gene body coverage\"];\n 38 -->|outputtxt| 42;\n 8544ea5c-faf2-44c9-85d6-40658fc9b9eb[\"Output\\nmultiqc_gene_body_cov\"];\n 42 --> 8544ea5c-faf2-44c9-85d6-40658fc9b9eb;\n style 8544ea5c-faf2-44c9-85d6-40658fc9b9eb stroke:#2c3143,stroke-width:4px;", + "modified": "2024-06-24 00:06:34 +0000", + "name": "QC + Mapping + Counting (single+paired) - Ref Based RNA Seq - Transcriptomics - GTN", + "path": "topics/transcriptomics/tutorials/ref-based/workflows/qc-mapping-counting-paired-and-single.ga", + "test_results": null, + "tests": true, + "title": "QC + Mapping + Counting (single+paired) - Ref Based RNA Seq - Transcriptomics - GTN", + "trs_endpoint": "https://training.galaxyproject.org/training-material/api/ga4gh/trs/v2/tools/transcriptomics-ref-based/versions/qc-mapping-counting-paired-and-single", + "url": "https://training.galaxyproject.org/training-material/topics/transcriptomics/tutorials/ref-based/workflows/qc-mapping-counting-paired-and-single.ga", + "wfid": "transcriptomics-ref-based", + "wfname": "qc-mapping-counting-paired-and-single", + "workflow": "qc-mapping-counting-paired-and-single.ga" + }, + { + "creators": [ + { + "class": "Person", + "identifier": "https://orcid.org/0000-0001-9852-1987", + "name": "B\u00e9r\u00e9nice Batut" + }, + { + "class": "Person", + "identifier": "https://orcid.org/0000-0003-2949-3921", + "name": "Mallory Freeberg" + }, + { + "class": "Person", + "identifier": "https://orcid.org/0000-0001-9449-9364", + "name": "Mo Heydarian" + }, + { + "class": "Person", + "identifier": "https://orcid.org/0000-0002-7427-6478", + "name": "Anika Erxleben" + }, + { + "class": "Person", + "identifier": "https://orcid.org/0000-0002-5192-126X", + "name": "Pavankumar Videm" + }, + { + "class": "Person", + "identifier": "https://orcid.org/0000-0002-1726-2256", + "name": "Clemens Blank" + }, + { + "class": "Person", + "identifier": "https://orcid.org/0000-0003-4847-8436", + "name": "Maria Doyle" + }, + { + "class": "Person", + "identifier": "https://orcid.org/0000-0003-3627-5340", + "name": "Nicola Soranzo" + }, + { + "class": "Person", + "identifier": "https://orcid.org/0000-0001-6553-5274", + "name": "Peter van Heusden" + }, + { + "class": "Person", + "identifier": "https://orcid.org/0000-0002-1964-4960", + "name": "Lucille Delisle" + } + ], + "license": "MIT", + "mermaid": "flowchart TD\n 0[\"\u2139\ufe0f Input Collection\\nPaired list collection with PE fastqs\"];\n style 0 stroke:#2c3143,stroke-width:4px;\n 1[\"\u2139\ufe0f Input Dataset\\nDrosophila_melanogaster.BDGP6.32.109_UCSC.gtf.gz\"];\n style 1 stroke:#2c3143,stroke-width:4px;\n 2[\"\ud83d\udee0\ufe0f Subworkflow\\nFastQC\"];\n style 2 fill:#edd,stroke:#900,stroke-width:4px;\n 0 -->|output| 2;\n 5f7652f1-e225-4ad1-8dbd-4d21544edb89[\"Output\\nmultiqc_fastqc_html\"];\n 2 --> 5f7652f1-e225-4ad1-8dbd-4d21544edb89;\n style 5f7652f1-e225-4ad1-8dbd-4d21544edb89 stroke:#2c3143,stroke-width:4px;\n 3[\"\ud83d\udee0\ufe0f Subworkflow\\ncutadapt\"];\n style 3 fill:#edd,stroke:#900,stroke-width:4px;\n 0 -->|output| 3;\n 41c7fbb5-655b-457c-8ba7-0e2eeab3d7ee[\"Output\\nmultiqc_cutadapt_html\"];\n 3 --> 41c7fbb5-655b-457c-8ba7-0e2eeab3d7ee;\n style 41c7fbb5-655b-457c-8ba7-0e2eeab3d7ee stroke:#2c3143,stroke-width:4px;\n 4[\"\ud83d\udee0\ufe0f Subworkflow\\nSTAR + multiQC\"];\n style 4 fill:#edd,stroke:#900,stroke-width:4px;\n 1 -->|output| 4;\n 3 -->|out_pairs| 4;\n 8aa5ef30-3f09-4a93-944d-0d89101c056a[\"Output\\nmultiqc_star_html\"];\n 4 --> 8aa5ef30-3f09-4a93-944d-0d89101c056a;\n style 8aa5ef30-3f09-4a93-944d-0d89101c056a stroke:#2c3143,stroke-width:4px;\n 11af5c57-91b4-496c-9b0c-b02904963f81[\"Output\\nSTAR_BAM\"];\n 4 --> 11af5c57-91b4-496c-9b0c-b02904963f81;\n style 11af5c57-91b4-496c-9b0c-b02904963f81 stroke:#2c3143,stroke-width:4px;\n 5[\"\ud83d\udee0\ufe0f Subworkflow\\nmore QC\"];\n style 5 fill:#edd,stroke:#900,stroke-width:4px;\n 1 -->|output| 5;\n 4 -->|STAR_BAM| 5;\n b306cb12-a275-4c6d-b609-47fdc208864b[\"Output\\nmultiqc_reads_per_chrom_html\"];\n 5 --> b306cb12-a275-4c6d-b609-47fdc208864b;\n style b306cb12-a275-4c6d-b609-47fdc208864b stroke:#2c3143,stroke-width:4px;\n 3ea82568-5698-49a7-88fe-91381070aac2[\"Output\\nmultiqc_dup_html\"];\n 5 --> 3ea82568-5698-49a7-88fe-91381070aac2;\n style 3ea82568-5698-49a7-88fe-91381070aac2 stroke:#2c3143,stroke-width:4px;\n f2eed352-ca21-4d65-8810-f5a1d3c282b4[\"Output\\nmultiqc_read_distrib_html\"];\n 5 --> f2eed352-ca21-4d65-8810-f5a1d3c282b4;\n style f2eed352-ca21-4d65-8810-f5a1d3c282b4 stroke:#2c3143,stroke-width:4px;\n 3375d63c-cdc3-4fbb-8a55-6f504c934918[\"Output\\nmultiqc_gene_body_cov_html\"];\n 5 --> 3375d63c-cdc3-4fbb-8a55-6f504c934918;\n style 3375d63c-cdc3-4fbb-8a55-6f504c934918 stroke:#2c3143,stroke-width:4px;\n 6[\"\ud83d\udee0\ufe0f Subworkflow\\nDetermine strandness\"];\n style 6 fill:#edd,stroke:#900,stroke-width:4px;\n 4 -->|STAR_BAM| 6;\n 1 -->|output| 6;\n 4 -->|signal_unique_str1| 6;\n 4 -->|signal_unique_str2| 6;\n 4 -->|reads_per_gene| 6;\n 9727824a-3eb2-4430-92d1-b3c40c3041d1[\"Output\\npgt\"];\n 6 --> 9727824a-3eb2-4430-92d1-b3c40c3041d1;\n style 9727824a-3eb2-4430-92d1-b3c40c3041d1 stroke:#2c3143,stroke-width:4px;\n 105313d8-e31a-405d-8fcd-cc5fd93275e2[\"Output\\nmultiqc_star_counts_html\"];\n 6 --> 105313d8-e31a-405d-8fcd-cc5fd93275e2;\n style 105313d8-e31a-405d-8fcd-cc5fd93275e2 stroke:#2c3143,stroke-width:4px;\n fb810859-f2d0-43f8-ac7c-5c714c5c6805[\"Output\\ninferexperiment\"];\n 6 --> fb810859-f2d0-43f8-ac7c-5c714c5c6805;\n style fb810859-f2d0-43f8-ac7c-5c714c5c6805 stroke:#2c3143,stroke-width:4px;\n 7[\"\ud83d\udee0\ufe0f Subworkflow\\ncount STAR\"];\n style 7 fill:#edd,stroke:#900,stroke-width:4px;\n 1 -->|output| 7;\n 4 -->|reads_per_gene| 7;\n 7b7c698b-4808-4b45-adf1-686f8d273d18[\"Output\\nGene length\"];\n 7 --> 7b7c698b-4808-4b45-adf1-686f8d273d18;\n style 7b7c698b-4808-4b45-adf1-686f8d273d18 stroke:#2c3143,stroke-width:4px;\n bd3388e6-5b45-4fdc-9780-3efd1c34ebf8[\"Output\\ncounts_from_star_sorted\"];\n 7 --> bd3388e6-5b45-4fdc-9780-3efd1c34ebf8;\n style bd3388e6-5b45-4fdc-9780-3efd1c34ebf8 stroke:#2c3143,stroke-width:4px;\n 5fee8aff-4023-43f1-a653-f5af5357d798[\"Output\\ncounts_from_star\"];\n 7 --> 5fee8aff-4023-43f1-a653-f5af5357d798;\n style 5fee8aff-4023-43f1-a653-f5af5357d798 stroke:#2c3143,stroke-width:4px;\n 8[\"\ud83d\udee0\ufe0f Subworkflow\\ncount featureCount\"];\n style 8 fill:#edd,stroke:#900,stroke-width:4px;\n 1 -->|output| 8;\n 4 -->|STAR_BAM| 8;\n 46c7a2e8-7819-4715-a028-7ad1de9ed605[\"Output\\nfeatureCounts\"];\n 8 --> 46c7a2e8-7819-4715-a028-7ad1de9ed605;\n style 46c7a2e8-7819-4715-a028-7ad1de9ed605 stroke:#2c3143,stroke-width:4px;\n 152ba01e-d4f2-4227-8812-87648a1c19ea[\"Output\\nmultiqc_featureCounts_html\"];\n 8 --> 152ba01e-d4f2-4227-8812-87648a1c19ea;\n style 152ba01e-d4f2-4227-8812-87648a1c19ea stroke:#2c3143,stroke-width:4px;\n 8b9d6c76-6e82-4691-b8bc-9996d6ae1594[\"Output\\nfeatureCounts_gene_length\"];\n 8 --> 8b9d6c76-6e82-4691-b8bc-9996d6ae1594;\n style 8b9d6c76-6e82-4691-b8bc-9996d6ae1594 stroke:#2c3143,stroke-width:4px;\n f0de4714-4df8-4506-90d9-384537ad663e[\"Output\\nfeatureCounts_sorted\"];\n 8 --> f0de4714-4df8-4506-90d9-384537ad663e;\n style f0de4714-4df8-4506-90d9-384537ad663e stroke:#2c3143,stroke-width:4px;", + "modified": "2024-06-24 00:06:34 +0000", + "name": "QC + Mapping + Counting - Ref Based RNA Seq - Transcriptomics - GTN - subworkflows", + "path": "topics/transcriptomics/tutorials/ref-based/workflows/qc-mapping-counting.ga", + "test_results": null, + "tests": true, + "title": "QC + Mapping + Counting - Ref Based RNA Seq - Transcriptomics - GTN - subworkflows", + "trs_endpoint": "https://training.galaxyproject.org/training-material/api/ga4gh/trs/v2/tools/transcriptomics-ref-based/versions/qc-mapping-counting", + "url": "https://training.galaxyproject.org/training-material/topics/transcriptomics/tutorials/ref-based/workflows/qc-mapping-counting.ga", + "wfid": "transcriptomics-ref-based", + "wfname": "qc-mapping-counting", + "workflow": "qc-mapping-counting.ga" + } + ], + "zenodo_link": "https://zenodo.org/record/6457007" + }, + { + "admin_install": { + "install_repository_dependencies": true, + "install_resolver_dependencies": true, + "install_tool_dependencies": true, + "tools": [ + { + "name": "text_processing", + "owner": "bgruening", + "revisions": "d698c222f354", + "tool_panel_section_label": "Text Manipulation", + "tool_shed_url": "https://toolshed.g2.bx.psu.edu/" + }, + { + "name": "text_processing", + "owner": "bgruening", + "revisions": 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circos_aln_to_links, bedtools_complementbed, collapse_dataset, circos_interval_to_tiles, fasta_compute_length, Grep1, tp_replace_in_column, tp_sed_tool, addValue, fasta_filter_by_length, bedtools_sortbed, lastz_wrapper_2, tp_sort_header_tool, random_lines1, Cut1, bedtools_intersectbed, tp_cat, mergeCols1, cat1, circos, tp_replace_in_line, datamash_ops, Filter1, jbrowse, upload1, join1 1 2.0 0 0 0.0 0 +Assembly Assembly of metagenomic sequencing data https://training.galaxyproject.org//topics/assembly/tutorials/metagenomics-assembly/tutorial.html Metagenomics, Sequence assembly Data handling, Visualisation, Sequencing quality control, Sequence alignment analysis, Sequence trimming, Read pre-processing, Sequence assembly validation, Primer removal, Sequence file editing, Local alignment, Variant calling, Sequence assembly visualisation, Read mapping, Statistical calculation, Sequence composition calculation, Formatting, Genome assembly, Sequence contamination filtering 2024-06-20 2024-06-20 0 True False False True coverm_contig, tp_cat, bandage_info, megahit_contig2fastg, megahit, collection_column_join, bg_uniq, bamtools, seqtk_subseq, filter_tabular, cutadapt, fastqc, bandage_image, bowtie2, random_lines1, quast, metaspades, ngsutils_bam_filter UseGalaxy.cz, UseGalaxy.eu 0 0 0 0.0 0 +Assembly Genome Assembly of a bacterial genome (MRSA) sequenced using Illumina MiSeq Data https://training.galaxyproject.org//topics/assembly/tutorials/mrsa-illumina/tutorial.html Sequence assembly, Whole genome sequencing, Public health and epidemiology, Genomics, Microbiology Statistical calculation, Visualisation, Sequencing quality control, Sequence assembly validation, Sequence composition calculation, Genome assembly, Sequence assembly visualisation, Sequence contamination filtering 2021-03-24 2024-03-11 28 True False False True fastp, upload1, fastqc, shovill, bandage_image, bandage_info, quast UseGalaxy.cz, UseGalaxy.eu, UseGalaxy.fr, UseGalaxy.org (Main) Galaxy@AuBi, GalaxyTrakr, UseGalaxy.no 8 4.5 0 0 0.0 0 +Assembly Genome Assembly of MRSA from Oxford Nanopore MinION data (and optionally Illumina data) https://training.galaxyproject.org//topics/assembly/tutorials/mrsa-nanopore/tutorial.html Sequence assembly, Whole genome sequencing, Public health and epidemiology, Genomics, Microbiology, Antimicrobial Resistance Box-Whisker plot plotting, Scatter plot plotting, Visualisation, Sequencing error detection, Sequencing quality control, Sequence alignment, Cross-assembly, Sequence assembly validation, Sequence composition calculation, De-novo assembly, Sequence assembly visualisation, Mapping assembly, Read mapping, Statistical calculation, Filtering, Genome assembly, Sequence contamination filtering 2021-03-24 2024-03-13 35 True False False True filtlong, CONVERTER_bz2_to_uncompressed, fastp, upload1, fastqc, bandage_image, bwa_mem2, flye, quast, nanoplot, porechop, polypolish UseGalaxy.eu UseGalaxy.cz 2 4.0 0 0 0.0 0 +Assembly Unicycler Assembly https://training.galaxyproject.org//topics/assembly/tutorials/unicycler-assembly/tutorial.html Sequence assembly, Genomics, Microbiology Coding region prediction, Genome annotation, Validation, Statistical calculation, Visualisation, Sequencing quality control, Aggregation, Gene prediction, Sequence composition calculation, Genome assembly, Sequence assembly validation 2017-10-11 2024-03-11 61 True True False True unicycler, multiqc, fastqc, prokka, quast UseGalaxy.eu, UseGalaxy.org (Main), UseGalaxy.org.au GalaxyTrakr, HyPhy HIV NGS Tools, MISSISSIPPI, UseGalaxy.cz, UseGalaxy.fr, UseGalaxy.no 8 3.75 0 0 0.0 0 +Ecology Checking expected species and contamination in bacterial isolate https://training.galaxyproject.org//topics/ecology/tutorials/bacterial-isolate-species-contamination-checking/tutorial.html Whole genome sequencing, Genomics, Microbiology, Microbial ecology Statistical calculation, Expression analysis, Cross-assembly, Taxonomic classification 2024-03-04 2024-04-04 5 True False False True recentrifuge, est_abundance, kraken2, upload1 UseGalaxy.eu, UseGalaxy.fr Galaxy@AuBi, GalaxyTrakr, UseGalaxy.cz, UseGalaxy.org (Main) 1 5.0 0 0 0.0 0 +Evolution Tree thinking for tuberculosis evolution and epidemiology https://training.galaxyproject.org//topics/evolution/tutorials/mtb_phylogeny/tutorial.html Genomics, Microbiology, Phylogeny, Infectious disease Sequence analysis, Phylogenetic tree analysis 2022-03-16 2023-02-15 19 True False False True upload1, interactive_tool_rstudio, raxml UseGalaxy.eu UseGalaxy.fr, UseGalaxy.org (Main) 6 4.333333333333333 0 0 0.0 0 +Evolution Identifying tuberculosis transmission links: from SNPs to transmission clusters https://training.galaxyproject.org//topics/evolution/tutorials/mtb_transmission/tutorial.html Genomics, Microbiology, Infectious disease, DNA polymorphism Antimicrobial resistance prediction, Variant calling, Phylogenetic tree generation, Phylogenetic tree visualisation 2022-03-16 2024-03-12 15 True False False True snippy, tp_cat, snp_dists, tp_grep_tool, tb_variant_filter, bcftools_consensus, upload1, tp_easyjoin_tool, snp_sites, tb_profiler_profile, addName, collapse_dataset, trimmomatic, samtools_view, Grep1, tp_replace_in_line, __MERGE_COLLECTION__, tp_sed_tool UseGalaxy.eu UseGalaxy.cz, UseGalaxy.no, UseGalaxy.org (Main) 7 4.0 0 0 0.0 0 +Genome Annotation Genome annotation with Prokka https://training.galaxyproject.org//topics/genome-annotation/tutorials/annotation-with-prokka/tutorial.html Genomics, Microbiology, Gene and protein families, Sequence analysis Coding region prediction, Genome annotation, Gene prediction, Genome visualisation 2018-03-06 2024-05-29 43 True True True True jbrowse, prokka UseGalaxy.eu, UseGalaxy.org (Main) Galaxy@AuBi, GalaxyTrakr, MISSISSIPPI, UseGalaxy.cz, UseGalaxy.fr, UseGalaxy.no, UseGalaxy.org.au 17 3.764705882352941 0 0 0.0 0 +Genome Annotation Bacterial Genome Annotation https://training.galaxyproject.org//topics/genome-annotation/tutorials/bacterial-genome-annotation/tutorial.html Genomics, Microbiology, Gene and protein families, Sequence analysis, Whole genome sequencing, Functional genomics, Mobile genetic elements Protein feature detection, Genome annotation, Scaffolding, Structural variation detection, Multilocus sequence typing, Nucleic acid feature detection, Genome assembly, Sequence motif recognition, Genome visualisation 2024-02-01 2024-03-13 8 True False False True Grouping1, isescan, jbrowse, tp_replace_in_column, integron_finder, plasmidfinder, upload1, bakta, tbl2gff3, tp_tail_tool Galaxy@AuBi, UseGalaxy.cz, UseGalaxy.eu, UseGalaxy.fr 1 1.0 0 0 0.0 0 +Genome Annotation Identification of AMR genes in an assembled bacterial genome https://training.galaxyproject.org//topics/genome-annotation/tutorials/amr-gene-detection/tutorial.html Whole genome sequencing, Public health and epidemiology, Genomics, Microbiology, Sequence analysis, Infectious disease, Antimicrobial Resistance Read mapping, Genome annotation, Genome visualisation 2024-01-23 2024-03-13 9 True False False True jbrowse, upload1, bakta, bowtie2, tbl2gff3, Grep1, staramr_search UseGalaxy.cz, UseGalaxy.eu, UseGalaxy.fr Galaxy@AuBi 0 0 0 0.0 0 +Genome Annotation Refining Genome Annotations with Apollo (prokaryotes) https://training.galaxyproject.org//topics/genome-annotation/tutorials/apollo/tutorial.html Genomics, Microbiology, Gene and protein families, Sequence analysis Genome visualisation 2021-06-04 2024-05-29 40 True True True True jbrowse, list_organism, create_account, iframe, create_or_update UseGalaxy.cz, UseGalaxy.eu, UseGalaxy.no 5 4.6 0 0 0.0 0 +Genome Annotation Comparative gene analysis in unannotated genomes https://training.galaxyproject.org//topics/genome-annotation/tutorials/gene-centric/tutorial.html Genomics, Gene and protein families, Sequence analysis, Phylogeny Coding region prediction, Database search, De-novo assembly, Transcriptome assembly, Multiple sequence alignment, Sequence alignment analysis, Phylogenetic tree generation 2022-09-08 2024-03-18 30 True False False True Add_a_column1, tp_split_on_column, Filter1, bg_diamond_makedb, bg_diamond_view, orfipy, cat1, regexColumn1, tab2fasta, collapse_dataset, gops_intersect_1, join1, bg_diamond, rapidnj, Cut1, rbc_mafft UseGalaxy.eu, UseGalaxy.org (Main) UseGalaxy.cz 0 0 0 0.0 0 +Genome Annotation Essential genes detection with Transposon insertion sequencing https://training.galaxyproject.org//topics/genome-annotation/tutorials/tnseq/tutorial.html Genomics, Microbiology, Sequence analysis, Mobile genetic elements Primer removal, Read pre-processing, Transposon prediction, Sequence trimming 2019-07-02 2024-03-15 52 True True False True deeptools_bam_coverage, Add_a_column1, Filter1, gff_to_prot, __EXTRACT_DATASET__, cutadapt, bg_find_subsequences, bowtie_wrapper, tp_sort_header_tool, transit_gumbel, Cut1, tp_easyjoin_tool UseGalaxy.no UseGalaxy.cz, UseGalaxy.eu 4 4.5 0 0 0.0 0 +Genome Annotation Genome annotation with Prokka https://training.galaxyproject.org//topics/genome-annotation/tutorials/annotation-with-prokka/tutorial.html Genomics, Microbiology, Gene and protein families, Sequence analysis Coding region prediction, Genome annotation, Gene prediction, Genome visualisation 2018-03-06 2024-05-29 43 True True True True jbrowse, prokka UseGalaxy.eu, UseGalaxy.org (Main) Galaxy@AuBi, GalaxyTrakr, MISSISSIPPI, UseGalaxy.cz, UseGalaxy.fr, UseGalaxy.no, UseGalaxy.org.au 17 3.764705882352941 0 0 0.0 0 +Variant Analysis Microbial Variant Calling https://training.galaxyproject.org//topics/variant-analysis/tutorials/microbial-variants/tutorial.html Genomics, Sequence assembly, DNA polymorphism, Microbiology, Sequence analysis, Genetic variation Genome visualisation, Variant calling, Phylogenetic tree generation, Phylogenetic tree visualisation 2018-02-26 2024-03-14 36 True False False True snippy, jbrowse UseGalaxy.eu, UseGalaxy.org (Main) GalaxyTrakr, UseGalaxy.cz, UseGalaxy.fr, UseGalaxy.no, UseGalaxy.org.au 12 4.416666666666667 0 0 0.0 0 +Assembly Making sense of a newly assembled genome https://training.galaxyproject.org//topics/assembly/tutorials/ecoli_comparison/tutorial.html Sequence assembly, Genomics, Microbiology Sequence visualisation, Sequence alignment, Mapping, Genome visualisation, Read mapping 2018-06-14 2024-03-11 86 True False False True tp_grep_tool, circos_aln_to_links, bedtools_complementbed, collapse_dataset, circos_interval_to_tiles, fasta_compute_length, Grep1, tp_replace_in_column, tp_sed_tool, addValue, fasta_filter_by_length, bedtools_sortbed, lastz_wrapper_2, tp_sort_header_tool, random_lines1, Cut1, bedtools_intersectbed, tp_cat, mergeCols1, cat1, circos, tp_replace_in_line, datamash_ops, Filter1, jbrowse, upload1, join1 1 2.0 0 0 0.0 0 +Assembly Genome Assembly of MRSA from Oxford Nanopore MinION data (and optionally Illumina data) https://training.galaxyproject.org//topics/assembly/tutorials/mrsa-nanopore/tutorial.html Sequence assembly, Whole genome sequencing, Public health and epidemiology, Genomics, Microbiology, Antimicrobial Resistance Box-Whisker plot plotting, Scatter plot plotting, Visualisation, Sequencing error detection, Sequencing quality control, Sequence alignment, Cross-assembly, Sequence assembly validation, Sequence composition calculation, De-novo assembly, Sequence assembly visualisation, Mapping assembly, Read mapping, Statistical calculation, Filtering, Genome assembly, Sequence contamination filtering 2021-03-24 2024-03-13 35 True False False True filtlong, CONVERTER_bz2_to_uncompressed, fastp, upload1, fastqc, bandage_image, bwa_mem2, flye, quast, nanoplot, porechop, polypolish UseGalaxy.eu UseGalaxy.cz 2 4.0 0 0 0.0 0 +Genome Annotation Bacterial Genome Annotation https://training.galaxyproject.org//topics/genome-annotation/tutorials/bacterial-genome-annotation/tutorial.html Genomics, Microbiology, Gene and protein families, Sequence analysis, Whole genome sequencing, Functional genomics, Mobile genetic elements Protein feature detection, Genome annotation, Scaffolding, Structural variation detection, Multilocus sequence typing, Nucleic acid feature detection, Genome assembly, Sequence motif recognition, Genome visualisation 2024-02-01 2024-03-13 8 True False False True Grouping1, isescan, jbrowse, tp_replace_in_column, integron_finder, plasmidfinder, upload1, bakta, tbl2gff3, tp_tail_tool Galaxy@AuBi, UseGalaxy.cz, UseGalaxy.eu, UseGalaxy.fr 1 1.0 0 0 0.0 0 +Genome Annotation Identification of AMR genes in an assembled bacterial genome https://training.galaxyproject.org//topics/genome-annotation/tutorials/amr-gene-detection/tutorial.html Whole genome sequencing, Public health and epidemiology, Genomics, Microbiology, Sequence analysis, Infectious disease, Antimicrobial Resistance Read mapping, Genome annotation, Genome visualisation 2024-01-23 2024-03-13 9 True False False True jbrowse, upload1, bakta, bowtie2, tbl2gff3, Grep1, staramr_search UseGalaxy.cz, UseGalaxy.eu, UseGalaxy.fr Galaxy@AuBi 0 0 0 0.0 0 +Genome Annotation Refining Genome Annotations with Apollo (prokaryotes) https://training.galaxyproject.org//topics/genome-annotation/tutorials/apollo/tutorial.html Genomics, Microbiology, Gene and protein families, Sequence analysis Genome visualisation 2021-06-04 2024-05-29 40 True True True True jbrowse, list_organism, create_account, iframe, create_or_update UseGalaxy.cz, UseGalaxy.eu, UseGalaxy.no 5 4.6 0 0 0.0 0 +Microbiome Analyses of metagenomics data - The global picture https://training.galaxyproject.org//topics/microbiome/tutorials/general-tutorial/tutorial.html Metagenomics, Microbial ecology, Taxonomy, Sequence analysis DNA barcoding, Sequence clustering, Taxonomic classification, Sequence read processing, Visualisation, Sequencing quality control, Phylogenetic analysis 2017-06-22 2024-03-14 4 True False False True mothur_make_shared, mothur_classify_seqs, mothur_screen_seqs, mothur_align_seqs, mothur_cluster_split, humann2_regroup_table, metaphlan2krona, taxonomy_krona_chart, mothur_make_biom, mothur_unique_seqs, krona-text, humann2, mothur_classify_otu, mothur_filter_seqs, mothur_pre_cluster, mothur_make_group, mothur_summary_seqs, mothur_count_seqs, metaphlan2, mothur_merge_files, humann2_renorm_table UseGalaxy.fr, UseGalaxy.no, UseGalaxy.org.au 16 4.0625 0 0 0.0 0 +Microbiome Pathogen detection from (direct Nanopore) sequencing data using Galaxy - Foodborne Edition https://training.galaxyproject.org//topics/microbiome/tutorials/pathogen-detection-from-nanopore-foodborne-data/tutorial.html Metagenomics, Public health and epidemiology, Taxonomy, Sequence assembly, Pathology, Sequence analysis Box-Whisker plot plotting, Validation, Multiple sequence alignment, Data handling, Antimicrobial resistance prediction, Phylogenetic tree reconstruction, Phylogenetic tree generation, Taxonomic classification, Scatter plot plotting, Visualisation, Sequence assembly, Sequencing quality control, Base-calling, Sequence alignment analysis, Cross-assembly, Phylogenetic tree generation (maximum likelihood and Bayesian methods), De-novo assembly, Variant calling, Multilocus sequence typing, Sequence assembly visualisation, Mapping assembly, Statistical calculation, Aggregation, Pairwise sequence alignment, Phylogenetic tree analysis, Mapping, Phylogenetic tree generation (from molecular sequences), Sequence composition calculation, Genome assembly, Sequence contamination filtering 2023-01-26 2024-06-19 18 True False False True Paste1, tp_multijoin_tool, param_value_from_file, tp_head_tool, clustalw, ggplot2_heatmap, multiqc, collapse_dataset, tp_sorted_uniq, samtools_coverage, Count1, medaka_consensus_pipeline, Grep1, tp_replace_in_column, abricate, table_compute, Add_a_column1, fasta_merge_files_and_filter_unique_sequences, Remove beginning1, __BUILD_LIST__, taxonomy_krona_chart, collection_column_join, __FILTER_EMPTY_DATASETS__, flye, fasta2tab, Cut1, samtools_depth, split_file_to_collection, mlst, clair3, barchart_gnuplot, tp_split_on_column, Grouping1, krakentools_extract_kraken_reads, add_line_to_file, collection_element_identifiers, tp_cut_tool, fastp, regexColumn1, krakentools_kreport2krona, minimap2, fastqc, bamtools_split_mapped, newick_display, bandage_image, snpSift_filter, bedtools_getfastabed, CONVERTER_gz_to_uncompressed, kraken2, snpSift_extractFields, bcftools_norm, compose_text_param, fasttree, porechop, bcftools_consensus, tab2fasta, regex1, samtools_fastx, tp_find_and_replace, nanoplot, __FILTER_FAILED_DATASETS__ UseGalaxy.eu 1 5.0 0 0 0.0 0 +Microbiome Taxonomic Profiling and Visualization of Metagenomic Data https://training.galaxyproject.org//topics/microbiome/tutorials/taxonomic-profiling/tutorial.html Metagenomics, Microbial ecology, Taxonomy, Sequence analysis Statistical calculation, Visualisation, Aggregation, Phylogenetic tree analysis, Nucleic acid sequence analysis, Taxonomic classification 2023-05-03 2024-05-29 4 True False False True est_abundance, kraken2, interactive_tool_phinch, metaphlan, taxonomy_krona_chart, __UNZIP_COLLECTION__, krakentools_kreport2krona, kraken_biom, interactive_tool_pavian UseGalaxy.eu UseGalaxy.org.au 3 4.333333333333333 0 0 0.0 0 +Microbiome Assembly of metagenomic sequencing data https://training.galaxyproject.org//topics/microbiome/tutorials/metagenomics-assembly/tutorial.html Metagenomics, Sequence assembly Data handling, Visualisation, Sequencing quality control, Sequence alignment analysis, Sequence trimming, Read pre-processing, Sequence assembly validation, Primer removal, Sequence file editing, Local alignment, Variant calling, Sequence assembly visualisation, Read mapping, Statistical calculation, Sequence composition calculation, Formatting, Genome assembly, Sequence contamination filtering 2022-12-05 2024-05-29 4 True False False True coverm_contig, tp_cat, bandage_info, megahit_contig2fastg, megahit, collection_column_join, bg_uniq, bamtools, seqtk_subseq, filter_tabular, cutadapt, fastqc, bandage_image, bowtie2, random_lines1, quast, metaspades, ngsutils_bam_filter UseGalaxy.cz, UseGalaxy.eu 0 0 0 0.0 0 +Microbiome Antibiotic resistance detection https://training.galaxyproject.org//topics/microbiome/tutorials/plasmid-metagenomics-nanopore/tutorial.html Metagenomic sequencing, Public health and epidemiology, Metagenomics, Microbiology, Infectious disease, Sequence analysis, Antimicrobial Resistance Box-Whisker plot plotting, Sequence analysis, De-novo assembly, Sequence assembly visualisation, Mapping assembly, Scatter plot plotting, Aggregation, Pairwise sequence alignment, Genome assembly 2019-06-25 2024-05-29 4 True False False True nanoplot, unicycler, PlasFlow, minimap2, bandage_image, racon, miniasm, gfa_to_fa, staramr_search UseGalaxy.eu UseGalaxy.cz, UseGalaxy.no, UseGalaxy.org (Main), UseGalaxy.org.au 10 5.0 0 0 0.0 0 +Microbiome 16S Microbial analysis with Nanopore data https://training.galaxyproject.org//topics/microbiome/tutorials/nanopore-16S-metagenomics/tutorial.html Microbial ecology, Taxonomy, Sequence analysis Visualisation, Statistical calculation, Sequencing quality control, Validation, Sequence composition calculation, Taxonomic classification, Sequence contamination filtering 2020-11-24 2024-03-21 4 True False False True kraken2, Remove beginning1, taxonomy_krona_chart, fastp, multiqc, datamash_reverse, fastqc, tp_replace_in_line, porechop UseGalaxy.eu, UseGalaxy.no, UseGalaxy.org (Main), UseGalaxy.org.au GalaxyTrakr, UseGalaxy.cz 13 4.6923076923076925 0 0 0.0 0 +Microbiome Metatranscriptomics analysis using microbiome RNA-seq data https://training.galaxyproject.org//topics/microbiome/tutorials/metatranscriptomics/tutorial.html Metatranscriptomics, Microbial ecology, Taxonomy, Function analysis, Sequence analysis Validation, Taxonomic classification, Sequence comparison, Visualisation, Sequencing quality control, Phylogenetic tree editing, Phylogenetic inference, Sequence alignment analysis, Phylogenetic analysis, Read pre-processing, Sequence trimming, Primer removal, Sequence similarity search, Conversion, Statistical calculation, Species frequency estimation, Phylogenetic tree analysis, Nucleic acid sequence analysis, Sequence composition calculation, Phylogenetic tree visualisation 2019-11-21 2024-05-29 4 True True False True humann_regroup_table, export2graphlan, multiqc, cutadapt, Grep1, bg_sortmerna, taxonomy_krona_chart, fastq_paired_end_interlacer, humann, humann_renorm_table, Cut1, graphlan, metaphlan, humann_rename_table, humann_unpack_pathways, fastqc, humann_split_stratified_table, combine_metaphlan2_humann2, graphlan_annotate, tp_find_and_replace UseGalaxy.org (Main) UseGalaxy.cz, UseGalaxy.no, UseGalaxy.org.au 6 4.5 0 0 0.0 0 +Microbiome Metatranscriptomics analysis using microbiome RNA-seq data (short) https://training.galaxyproject.org//topics/microbiome/tutorials/metatranscriptomics-short/tutorial.html Metatranscriptomics, Microbial ecology, Taxonomy, Function analysis, Sequence analysis Validation, Taxonomic classification, Sequence comparison, Visualisation, Sequencing quality control, Phylogenetic tree editing, Phylogenetic inference, Sequence alignment analysis, Phylogenetic analysis, Read pre-processing, Sequence trimming, Primer removal, Sequence similarity search, Conversion, Statistical calculation, Species frequency estimation, Phylogenetic tree analysis, Nucleic acid sequence analysis, Sequence composition calculation, Phylogenetic tree visualisation 2020-02-13 2024-05-29 4 True False False True humann_regroup_table, export2graphlan, multiqc, cutadapt, Grep1, bg_sortmerna, taxonomy_krona_chart, fastq_paired_end_interlacer, humann, humann_renorm_table, Cut1, graphlan, metaphlan, humann_rename_table, humann_unpack_pathways, fastqc, humann_split_stratified_table, combine_metaphlan2_humann2, graphlan_annotate, tp_find_and_replace UseGalaxy.org (Main) UseGalaxy.cz, UseGalaxy.no, UseGalaxy.org.au 1 5.0 0 0 0.0 0 +Microbiome Identification of the micro-organisms in a beer using Nanopore sequencing https://training.galaxyproject.org//topics/microbiome/tutorials/beer-data-analysis/tutorial.html Metagenomics, Microbial ecology, Taxonomy, Sequence analysis Visualisation, Statistical calculation, Sequencing quality control, Aggregation, Sequence composition calculation, Taxonomic classification, Sequence contamination filtering 2022-09-29 2024-05-29 4 True False False True kraken2, Filter1, taxonomy_krona_chart, fastp, krakentools_kreport2krona, fastqc, porechop GalaxyTrakr, UseGalaxy.eu, UseGalaxy.org (Main), UseGalaxy.org.au UseGalaxy.cz 3 4.666666666666667 0 0 0.0 0 +Microbiome Building an amplicon sequence variant (ASV) table from 16S data using DADA2 https://training.galaxyproject.org//topics/microbiome/tutorials/dada-16S/tutorial.html Microbial ecology, Taxonomy, Sequence analysis DNA barcoding, Deposition, Visualisation, Analysis, Variant calling 2024-06-05 2024-06-17 9 True False False True dada2_seqCounts, tp_head_tool, dada2_mergePairs, __SORTLIST__, tp_replace_in_column, Add_a_column1, phyloseq_from_dada2, __UNZIP_COLLECTION__, interactive_tool_phyloseq, Grouping1, dada2_makeSequenceTable, cat1, collection_element_identifiers, dada2_filterAndTrim, tp_replace_in_line, dada2_removeBimeraDenovo, dada2_assignTaxonomyAddspecies, dada2_dada, dada2_plotQualityProfile, dada2_learnErrors UseGalaxy.eu, UseGalaxy.fr 0 0 0 0.0 0 +Microbiome QIIME 2 Cancer Microbiome Intervention https://training.galaxyproject.org//topics/microbiome/tutorials/qiime2-cancer-microbiome-intervention/tutorial.html Microbial ecology, Taxonomy, Sequence analysis 2024-02-12 2024-03-14 6 external False False False UseGalaxy.eu, UseGalaxy.org, UseGalaxy.org.au, UseGalaxy.fr 1 5.0 0 0 0.0 0 +Microbiome Binning of metagenomic sequencing data https://training.galaxyproject.org//topics/microbiome/tutorials/metagenomics-binning/tutorial.html Metagenomics, Sequence assembly Statistical calculation, Sequencing quality control, Validation, Sequence composition calculation, Genome assembly, Sequence assembly validation 2023-12-05 2024-03-14 4 True False False False megahit, checkm_lineage_wf GalaxyTrakr, UseGalaxy.cz, UseGalaxy.eu, UseGalaxy.fr 0 0 0 0.0 0 +Microbiome QIIME 2 Moving Pictures https://training.galaxyproject.org//topics/microbiome/tutorials/qiime2-moving-pictures/tutorial.html Microbial ecology, Taxonomy, Sequence analysis 2024-03-14 2024-03-14 2 external False False False UseGalaxy.eu, UseGalaxy.org, UseGalaxy.org.au, UseGalaxy.fr 1 5.0 0 0 0.0 0 +Microbiome 16S Microbial Analysis with mothur (short) https://training.galaxyproject.org//topics/microbiome/tutorials/mothur-miseq-sop-short/tutorial.html Microbial ecology, Taxonomy, Sequence analysis DNA barcoding, Sequence clustering, Phylogenetic tree reconstruction, Taxonomic classification, Phylogenetic tree generation, Sequence read processing, Visualisation, Sequencing quality control, Phylogenetic tree analysis, Phylogenetic analysis 2019-05-13 2024-05-31 7 True False False True mothur_make_shared, mothur_summary_single, mothur_remove_groups, mothur_classify_seqs, mothur_screen_seqs, collapse_dataset, mothur_heatmap_sim, mothur_cluster_split, mothur_sub_sample, mothur_unique_seqs, mothur_dist_shared, mothur_classify_otu, mothur_venn, newick_display, mothur_dist_seqs, XY_Plot_1, mothur_remove_seqs, mothur_tree_shared, mothur_filter_seqs, mothur_pre_cluster, mothur_rarefaction_single, mothur_remove_lineage, mothur_chimera_vsearch, mothur_summary_seqs, mothur_count_seqs, mothur_cluster, mothur_count_groups UseGalaxy.eu, UseGalaxy.no, UseGalaxy.org (Main), UseGalaxy.org.au UseGalaxy.cz, UseGalaxy.fr 9 4.666666666666667 0 0 0.0 0 +Microbiome 16S Microbial Analysis with mothur (extended) https://training.galaxyproject.org//topics/microbiome/tutorials/mothur-miseq-sop/tutorial.html Microbial ecology, Taxonomy, Sequence analysis DNA barcoding, Sequence clustering, Phylogenetic tree reconstruction, Taxonomic classification, Phylogenetic tree generation, Sequence read processing, Visualisation, Sequencing quality control, Phylogenetic tree analysis, Phylogenetic analysis 2017-02-12 2024-03-14 4 True False False True mothur_make_shared, mothur_summary_single, mothur_remove_groups, mothur_classify_seqs, mothur_screen_seqs, mothur_seq_error, mothur_heatmap_sim, mothur_align_seqs, mothur_cluster_split, mothur_sub_sample, taxonomy_krona_chart, mothur_make_biom, mothur_unique_seqs, mothur_dist_shared, mothur_classify_otu, mothur_venn, newick_display, mothur_dist_seqs, XY_Plot_1, mothur_remove_seqs, mothur_taxonomy_to_krona, mothur_tree_shared, mothur_filter_seqs, mothur_pre_cluster, mothur_rarefaction_single, mothur_remove_lineage, mothur_make_contigs, mothur_chimera_vsearch, mothur_summary_seqs, mothur_get_groups, mothur_count_seqs, mothur_cluster, mothur_count_groups UseGalaxy.eu UseGalaxy.cz, UseGalaxy.fr, UseGalaxy.no, UseGalaxy.org (Main), UseGalaxy.org.au 44 4.613636363636363 0 0 0.0 0 +Variant Analysis M. tuberculosis Variant Analysis https://training.galaxyproject.org//topics/variant-analysis/tutorials/tb-variant-analysis/tutorial.html Genomics, Microbiology, Sequence assembly, Genetic variation, Public health and epidemiology, Infectious disease Sequence analysis, Validation, Genome visualisation, Antimicrobial resistance prediction, Local alignment, Variant calling, Phylogenetic tree generation, Taxonomic classification, Statistical calculation, Sequencing quality control, Sequence alignment, Global alignment, Sequence composition calculation, Sequence alignment analysis, Phylogenetic tree visualisation, Sequence contamination filtering 2020-07-25 2024-06-12 75 True False False True tbvcfreport, multiqc, tp_sed_tool, qualimap_bamqc, tb_profiler_profile, EMBOSS:%20seqret84, snippy, fastp, fastqc, mosdepth, samtools_stats, tp_awk_tool, kraken2, jbrowse, tb_variant_filter, bcftools_consensus, upload1, EMBOSS: seqret84, __FLATTEN__, jvarkit_wgscoverageplotter 25 4.64 0 0 0.0 0 +Genome Annotation Identification of AMR genes in an assembled bacterial genome https://training.galaxyproject.org//topics/genome-annotation/tutorials/amr-gene-detection/tutorial.html Whole genome sequencing, Public health and epidemiology, Genomics, Microbiology, Sequence analysis, Infectious disease, Antimicrobial Resistance Read mapping, Genome annotation, Genome visualisation 2024-01-23 2024-03-13 9 True False False True jbrowse, upload1, bakta, bowtie2, tbl2gff3, Grep1, staramr_search UseGalaxy.cz, UseGalaxy.eu, UseGalaxy.fr Galaxy@AuBi 0 0 0 0.0 0 +Evolution Tree thinking for tuberculosis evolution and epidemiology https://training.galaxyproject.org//topics/evolution/tutorials/mtb_phylogeny/tutorial.html Genomics, Microbiology, Phylogeny, Infectious disease Sequence analysis, Phylogenetic tree analysis 2022-03-16 2023-02-15 19 True False False True upload1, interactive_tool_rstudio, raxml UseGalaxy.eu UseGalaxy.fr, UseGalaxy.org (Main) 6 4.333333333333333 0 0 0.0 0 +Evolution Identifying tuberculosis transmission links: from SNPs to transmission clusters https://training.galaxyproject.org//topics/evolution/tutorials/mtb_transmission/tutorial.html Genomics, Microbiology, Infectious disease, DNA polymorphism Antimicrobial resistance prediction, Variant calling, Phylogenetic tree generation, Phylogenetic tree visualisation 2022-03-16 2024-03-12 15 True False False True snippy, tp_cat, snp_dists, tp_grep_tool, tb_variant_filter, bcftools_consensus, upload1, tp_easyjoin_tool, snp_sites, tb_profiler_profile, addName, collapse_dataset, trimmomatic, samtools_view, Grep1, tp_replace_in_line, __MERGE_COLLECTION__, tp_sed_tool UseGalaxy.eu UseGalaxy.cz, UseGalaxy.no, UseGalaxy.org (Main) 7 4.0 0 0 0.0 0 +Proteomics metaQuantome 3: Taxonomy https://training.galaxyproject.org//topics/proteomics/tutorials/metaquantome-taxonomy/tutorial.html Proteomics, Proteogenomics, Metatranscriptomics, Microbial ecology, Metagenomics, Taxonomy Statistical inference, Differential protein expression analysis, Visualisation, Principal component visualisation, Quantification, Functional clustering, Heat map generation, Indexing, Filtering, Query and retrieval 2020-10-29 2024-03-14 36 True False False True metaquantome_filter, metaquantome_stat, metaquantome_viz, metaquantome_sample, metaquantome_expand, metaquantome_db UseGalaxy.eu UseGalaxy.cz, UseGalaxy.no, UseGalaxy.org.au 0 0 0 0.0 0 +Proteomics Metaproteomics tutorial https://training.galaxyproject.org//topics/proteomics/tutorials/metaproteomics/tutorial.html Proteomics, Proteogenomics, Biodiversity, Taxonomy Visualisation, Prediction and recognition 2017-06-28 2024-05-29 57 True False False True sqlite_to_tabular, query_tabular, peptide_shaker, unipept, search_gui UseGalaxy.eu, UseGalaxy.no, UseGalaxy.org (Main), UseGalaxy.org.au UseGalaxy.cz 1 5.0 0 0 0.0 0 +Proteomics metaQuantome 1: Data creation https://training.galaxyproject.org//topics/proteomics/tutorials/metaquantome-data-creation/tutorial.html Proteomics, Proteogenomics, Biodiversity, Taxonomy Visualisation, Prediction and recognition, Filtering, Formatting, Label-free quantification 2020-10-16 2024-03-14 90 True False False True flashlfq, Remove beginning1, Filter1, query_tabular, regex1, peptide_shaker, unipept, msconvert, Grep1, Cut1, tp_replace_in_line, tp_replace_in_column, search_gui UseGalaxy.eu UseGalaxy.cz, UseGalaxy.no, UseGalaxy.org.au 0 0 0 0.0 0 +Proteomics metaQuantome 2: Function https://training.galaxyproject.org//topics/proteomics/tutorials/metaquantome-function/tutorial.html Proteomics, Proteogenomics, Metatranscriptomics, Microbial ecology, Metagenomics Statistical inference, Differential protein expression analysis, Visualisation, Principal component visualisation, Quantification, Functional clustering, Heat map generation, Indexing, Filtering, Query and retrieval 2020-10-29 2024-03-14 38 True False False True metaquantome_filter, metaquantome_stat, metaquantome_viz, metaquantome_sample, metaquantome_expand, metaquantome_db UseGalaxy.eu UseGalaxy.cz, UseGalaxy.no, UseGalaxy.org.au 0 0 0 0.0 0 +Variant Analysis Calling variants in non-diploid systems https://training.galaxyproject.org//topics/variant-analysis/tutorials/non-dip/tutorial.html Genomics, Sequence assembly, DNA polymorphism, Microbiology, Sequence analysis, Genetic variation Generation, Data handling, Variant calling, Genome indexing, Read mapping, Statistical calculation, Sequencing quality control, Sequence alignment, Sequence composition calculation, Sequence alignment analysis, Formatting 2017-02-16 2024-03-14 39 True False False True bwa_mem, vcffilter2, bamleftalign, freebayes, bamFilter, vcf2tsv, fastqc, picard_MarkDuplicates, Cut1, picard_MergeSamFiles UseGalaxy.eu, UseGalaxy.fr, UseGalaxy.no, UseGalaxy.org (Main) UseGalaxy.cz, UseGalaxy.org.au 3 2.3333333333333335 0 0 0.0 0 +Variant Analysis M. tuberculosis Variant Analysis https://training.galaxyproject.org//topics/variant-analysis/tutorials/tb-variant-analysis/tutorial.html Genomics, Microbiology, Sequence assembly, Genetic variation, Public health and epidemiology, Infectious disease Sequence analysis, Validation, Genome visualisation, Antimicrobial resistance prediction, Local alignment, Variant calling, Phylogenetic tree generation, Taxonomic classification, Statistical calculation, Sequencing quality control, Sequence alignment, Global alignment, Sequence composition calculation, Sequence alignment analysis, Phylogenetic tree visualisation, Sequence contamination filtering 2020-07-25 2024-06-12 75 True False False True tbvcfreport, multiqc, tp_sed_tool, qualimap_bamqc, tb_profiler_profile, EMBOSS:%20seqret84, snippy, fastp, fastqc, mosdepth, samtools_stats, tp_awk_tool, kraken2, jbrowse, tb_variant_filter, bcftools_consensus, upload1, EMBOSS: seqret84, __FLATTEN__, jvarkit_wgscoverageplotter 25 4.64 0 0 0.0 0 +Variant Analysis Microbial Variant Calling https://training.galaxyproject.org//topics/variant-analysis/tutorials/microbial-variants/tutorial.html Genomics, Sequence assembly, DNA polymorphism, Microbiology, Sequence analysis, Genetic variation Genome visualisation, Variant calling, Phylogenetic tree generation, Phylogenetic tree visualisation 2018-02-26 2024-03-14 36 True False False True snippy, jbrowse UseGalaxy.eu, UseGalaxy.org (Main) GalaxyTrakr, UseGalaxy.cz, UseGalaxy.fr, UseGalaxy.no, UseGalaxy.org.au 12 4.416666666666667 0 0 0.0 0