diff --git a/topics/microbiome/metadata.yaml b/topics/microbiome/metadata.yaml index 16a4f5639f788f..32def78281eebb 100644 --- a/topics/microbiome/metadata.yaml +++ b/topics/microbiome/metadata.yaml @@ -44,3 +44,6 @@ subtopics: - id: metatranscriptomics title: "Metatranscriptomics" description: "Taxonomic and functional characterisation of mixed samples using transcriptome data." + - id: clinical-metaproteomics + title: "Metaproteomics" + description: "These tutorials are step by step analysis from database generation to the discovery of peptides to verification, quantitation, and interpretation of the results." diff --git a/topics/microbiome/tutorials/clinical-mp-1-database-generation b/topics/microbiome/tutorials/clinical-mp-1-database-generation new file mode 120000 index 00000000000000..421f5cfba07136 --- /dev/null +++ b/topics/microbiome/tutorials/clinical-mp-1-database-generation @@ -0,0 +1 @@ +../../proteomics/tutorials/clinical-mp-1-database-generation \ No newline at end of file diff --git a/topics/microbiome/tutorials/clinical-mp-2-discovery b/topics/microbiome/tutorials/clinical-mp-2-discovery new file mode 120000 index 00000000000000..830b4e684ac1fd --- /dev/null +++ b/topics/microbiome/tutorials/clinical-mp-2-discovery @@ -0,0 +1 @@ +../../proteomics/tutorials/clinical-mp-2-discovery/ \ No newline at end of file diff --git a/topics/microbiome/tutorials/clinical-mp-3-verification b/topics/microbiome/tutorials/clinical-mp-3-verification new file mode 120000 index 00000000000000..2d061df6efe0a5 --- /dev/null +++ b/topics/microbiome/tutorials/clinical-mp-3-verification @@ -0,0 +1 @@ +../../proteomics/tutorials/clinical-mp-3-verification \ No newline at end of file diff --git a/topics/microbiome/tutorials/clinical-mp-4-quantitation b/topics/microbiome/tutorials/clinical-mp-4-quantitation new file mode 120000 index 00000000000000..6c0c735ea0893f --- /dev/null +++ b/topics/microbiome/tutorials/clinical-mp-4-quantitation @@ -0,0 +1 @@ +../../proteomics/tutorials/clinical-mp-4-quantitation \ No newline at end of file diff --git a/topics/microbiome/tutorials/clinical-mp-5-data-interpretation b/topics/microbiome/tutorials/clinical-mp-5-data-interpretation new file mode 120000 index 00000000000000..ecb968f34ada7a --- /dev/null +++ b/topics/microbiome/tutorials/clinical-mp-5-data-interpretation @@ -0,0 +1 @@ +../../proteomics/tutorials/clinical-mp-5-data-interpretation \ No newline at end of file diff --git a/topics/proteomics/tutorials/clinical-mp-1-database-generation/tutorial.md b/topics/proteomics/tutorials/clinical-mp-1-database-generation/tutorial.md index 5370eb96c1e146..2fe9e717113a8b 100644 --- a/topics/proteomics/tutorials/clinical-mp-1-database-generation/tutorial.md +++ b/topics/proteomics/tutorials/clinical-mp-1-database-generation/tutorial.md @@ -52,13 +52,13 @@ Metaproteomics is the large-scale characterization of the entire complement of p To address this, we used tandem mass spectrometry (MS/MS) and bioinformatics tools on the Galaxy platform to develop a metaproteomics workflow to characterize the metaproteomes of clinical samples. This clinical metaproteomics workflow holds potential for general clinical applications such as potential secondary infections during COVID-19 infection, microbiome changes during cystic fibrosis as well as broad research questions regarding host-microbe interactions. -![Clinical Metaproteomics workflow](../../images/clinical-mp/clinical-mp-overview.JPG) +![Clinical Metaproteomics workflow]({% link topics/proteomics/images/clinical-mp/clinical-mp-overview.JPG %}) The first workflow for the clinical metaproteomics data analysis is the Database generation workflow. The Galaxy-P team has developed a workflow wherein a large database is generated by downloading protein sequences of known disease-causing microorganisms and then generating a compact database from the comprehensive database using the Metanovo tool. -![Database Generation Workflow](../../images/clinical-mp/clinical-mp-database-generation.JPG) +![Database Generation Workflow]({% link topics/proteomics/images/clinical-mp/clinical-mp-database-generation.JPG %}) diff --git a/topics/proteomics/tutorials/clinical-mp-2-discovery/tutorial.md b/topics/proteomics/tutorials/clinical-mp-2-discovery/tutorial.md index 6c12c62c79b1cc..2c9ade8468656d 100644 --- a/topics/proteomics/tutorials/clinical-mp-2-discovery/tutorial.md +++ b/topics/proteomics/tutorials/clinical-mp-2-discovery/tutorial.md @@ -58,7 +58,7 @@ This tutorial can be followed with any user-defined database but would work best The MSMS data will be searched against the compact database `Human UniProt Microbial Proteins (from MetaNovo) and cRAP` to identify peptide and protein sequences via sequence database searching. For this tutorial, two peptide identification programs will be used: SearchGUI/PeptideShaker and MaxQuant. However, you could use other software too, such as Fragpipe or Scribe. For the purpose of this tutorial, a dataset of the 4 RAW/MGF files will be used as the MS/MS input. -![Discovery Workflow](../../images/clinical-mp/clinical-mp-discovery.JPG) +![Discovery Workflow]({% link topics/proteomics/images/clinical-mp/clinical-mp-discovery.JPG %}) > diff --git a/topics/proteomics/tutorials/clinical-mp-3-verification/tutorial.md b/topics/proteomics/tutorials/clinical-mp-3-verification/tutorial.md index e8f53edee6b3f4..a75ecd0060cc01 100644 --- a/topics/proteomics/tutorials/clinical-mp-3-verification/tutorial.md +++ b/topics/proteomics/tutorials/clinical-mp-3-verification/tutorial.md @@ -56,9 +56,9 @@ The PepQuery tool is used to validate the identified microbial peptides from Sea Interestingly, the PepQuery tool does not rely on searching peptides against a reference protein sequence database as “traditional” shotgun proteomics does, which enables it to identify novel, disease-specific sequences with sensitivity and specificity in its protein validation (Figure A). Then we extract microbial protein sequences that are assigned to the PepQuery verified peptides. To this, we again add the Human UniProt Reference proteome (with Isoforms) and cRAP databases for creating a database for quantitation purposes (Figure B). -![Peptide Verification](../../images/clinical-mp/clinical-mp-verification-1.JPG) +![Peptide Verification]({% link topics/proteomics/images/clinical-mp/clinical-mp-verification-1.JPG %}) -![Database generation from verified peptides](../../images/clinical-mp/clinical-mp-verification-2.JPG) +![Database generation from verified peptides]({% link topics/proteomics/images/clinical-mp/clinical-mp-verification-2.JPG %}) > diff --git a/topics/proteomics/tutorials/clinical-mp-4-quantitation/tutorial.md b/topics/proteomics/tutorials/clinical-mp-4-quantitation/tutorial.md index f5719c65e29a73..2f9cdaab2cf8df 100644 --- a/topics/proteomics/tutorials/clinical-mp-4-quantitation/tutorial.md +++ b/topics/proteomics/tutorials/clinical-mp-4-quantitation/tutorial.md @@ -51,7 +51,7 @@ The next step of the clinical metaproteomics workflow is the quantification work In this current workflow, we perform Quantification using the MaxQuant tool and the output will be interpreted in our next module. -![Quantitation workflow](../../images/clinical-mp/clinical-mp-quantification.JPG) +![Quantitation workflow]({% link topics/proteomics/images/clinical-mp/clinical-mp-quantification.JPG %}) diff --git a/topics/proteomics/tutorials/clinical-mp-5-data-interpretation/tutorial.md b/topics/proteomics/tutorials/clinical-mp-5-data-interpretation/tutorial.md index 9f635be84ccc3e..7be1e16f311331 100644 --- a/topics/proteomics/tutorials/clinical-mp-5-data-interpretation/tutorial.md +++ b/topics/proteomics/tutorials/clinical-mp-5-data-interpretation/tutorial.md @@ -50,7 +50,8 @@ recordings: The final workflow in the array of clinical metaproteomics tutorials is the data interpretation workflow. Interpreting MaxQuant data using MSstats involves applying a rigorous statistical framework to glean meaningful insights from quantitative proteomic datasets. The MaxQuant output is explored to understand data distribution and variability. Subsequent normalization helps account for systematic variations. MSstats allows the user to define the experimental design, including sample groups and conditions, to perform statistical analysis. The output provides valuable information about differential protein expression across conditions, estimates of fold changes, and associated p-values, aiding in the identification of biologically significant proteins. Furthermore, MSstats enables quality control and data visualization, ultimately enhancing our ability to draw meaningful conclusions from complex proteomic datasets. Additional tutorial material for using MaxQuant and MSstatTMT for TMT data analysis can be found at [MaxQuant and MSstats for the analysis of TMT data](https://gxy.io/GTN:T00220). -![Data-Interpretation-workflow](../../images/clinical-mp/clinical-mp-data-interpretation.JPG) +![Data-Interpretation-workflow]({% link topics/proteomics/images/clinical-mp/clinical-mp-data-interpretation.JPG %}) + > > > In this tutorial, we will cover: @@ -131,7 +132,7 @@ Unipept serves as a vital bioinformatics platform for the analysis of mass spect > {: .hands_on} -![Data-Interpretation with Unipept](../../images/clinical-mp/clinical-mp-data-interpretation-figure2.jpg) +![Data-Interpretation with Unipept]({% link topics/proteomics/images/clinical-mp/clinical-mp-data-interpretation-figure2.jpg %}) ## Extraction of Microbial Sequences @@ -222,7 +223,7 @@ MSstats TMT(Tandem Mass Tag) is a computational tool designed for the robust sta The MSstats output typically includes essential information such as estimated fold changes, p-values, and other statistical measures that help identify differentially expressed proteins across experimental conditions or sample groups. It provides a clear picture of the variations in protein expression levels, aiding in the prioritization of biologically relevant targets. MSstats output also often includes visualizations and quality control metrics, making it a valuable resource for researchers in their quest to extract meaningful insights from complex proteomic datasets and understand the underlying biology of their experiments. Example of our data interpretation: -![Data-Interpretation results with MSstats](../../images/clinical-mp/clinical-mp-data-interpretation-figure3.jpg) +![Data-Interpretation results with MSstats]({% link topics/proteomics/images/clinical-mp/clinical-mp-data-interpretation-figure3.jpg %}) # Conclusion diff --git a/topics/single-cell/faqs/single_cell_omics.md b/topics/single-cell/faqs/single_cell_omics.md index ca249a590734b7..8adb632a0ff96c 100644 --- a/topics/single-cell/faqs/single_cell_omics.md +++ b/topics/single-cell/faqs/single_cell_omics.md @@ -10,6 +10,8 @@ Did you know we have a unique Single Cell Omics Lab with all our single cell too The Single Cell Omics Lab currently uses the main European Galaxy infrastructure and power, it's just organised better for users of particular analyses...like single cell! -Try it out! All your histories/workflows/logins from the general [European Galaxy server](https://usegalaxy.eu) will be there! +Try it out! - - {% icon subdomain %} [Single Cell Omics Lab](https://singlecell.usegalaxy.eu) + - {% icon subdomain %} [Europe | Single Cell Omics Lab](https://singlecell.usegalaxy.eu) + - {% icon subdomain %} [USA | Single Cell Omics Lab](https://singlecell.usegalaxy.org) + - {% icon subdomain %} [Australia | Single Cell Omics Lab](https://singlecell.usegalaxy.org.au) diff --git a/topics/single-cell/metadata.yaml b/topics/single-cell/metadata.yaml index 74be3ee8843e13..56d84d08957db2 100644 --- a/topics/single-cell/metadata.yaml +++ b/topics/single-cell/metadata.yaml @@ -26,7 +26,7 @@ editorial_board: subtopics: - id: scintroduction title: "Introduction" - description: "Start here if you are new to single cell analysis in Galaxy and want to learn the concepts." + description: "Start here if you are new to single cell analysis and want to learn the concepts." - id: firstsc title: "Your first analysis" description: "Start here if you are new to single cell analysis in Galaxy and want to try analysing data." @@ -51,7 +51,9 @@ subtopics: - id: datamanipulation title: "Changing data formats & preparing objects" description: "These tutorials cover a range of needs for importing data from different sources, to changing data into different formats to move from one analysis to the other." - + - id: exploratory + title: "Exploratory Analyses" + description: "What do you do with your list of genes? Come here to explore your results more!" references: diff --git a/topics/single-cell/tutorials/GO-enrichment/tutorial.md b/topics/single-cell/tutorials/GO-enrichment/tutorial.md index e13dc3ad84dd51..5c48b24872ab73 100644 --- a/topics/single-cell/tutorials/GO-enrichment/tutorial.md +++ b/topics/single-cell/tutorials/GO-enrichment/tutorial.md @@ -3,6 +3,7 @@ layout: tutorial_hands_on title: GO Enrichment Analysis on Single-Cell RNA-Seq Data zenodo_link: 'https://zenodo.org/records/13461890' +subtopic: exploratory questions: - What is Gene Ontology (GO) enrichment analysis, and why should I perform it on my marker genes? diff --git a/topics/single-cell/tutorials/scrna-case-cell-annotation/slides.html b/topics/single-cell/tutorials/scrna-case-cell-annotation/slides.html index dc127f0a5a2338..241bb2e5a83775 100644 --- a/topics/single-cell/tutorials/scrna-case-cell-annotation/slides.html +++ b/topics/single-cell/tutorials/scrna-case-cell-annotation/slides.html @@ -15,7 +15,7 @@ - "Recognise the common issues and be able to resolve them" subtopic: scintroduction -priority: 4 +priority: 6 tags: