test

episodes/Snakemake.md

@@ -1,5 +1,5 @@
 ---
-title: "Using Markdown"
+title: "Snakemake"
 teaching: 10
 exercises: 2
 ---
@@ -22,16 +22,64 @@ Snakemake is a workflow management system that simplifies the creation and execu
 
 ## Key Features:
 
-    Declarative Syntax: Snakemake uses a declarative language to define workflows, focusing on what you want to achieve rather than how to achieve it. This makes pipelines more readable and maintainable.
-    Rule-Based System: Workflows are defined as a series of rules. Each rule represents a task or process, specifying its inputs, outputs, and the command to execute.
-    Dependency Management: Snakemake automatically determines the order in which rules need to be executed based on their dependencies. This ensures that tasks are performed in the correct sequence.
-    Parallel Execution: Snakemake can efficiently distribute tasks across multiple cores or machines, accelerating the execution of large-scale pipelines.
-    Flexibility: It can handle a wide range of computational tasks, from simple data processing to complex simulations.
-    Integration with Tools: Snakemake can easily integrate with various tools and libraries used in high energy physics, such as ROOT, TensorFlow, and PyTorch.
+- Declarative Syntax: Snakemake uses a declarative language to define workflows, focusing on what you want to achieve rather than how to achieve it. This makes pipelines more readable and maintainable.
+- Rule-Based System: Workflows are defined as a series of rules. Each rule represents a task or process, specifying its inputs, outputs, and the command to execute.
+- Dependency Management: Snakemake automatically determines the order in which rules need to be executed based on their dependencies. This ensures that tasks are performed in the correct sequence.
+- Parallel Execution: Snakemake can efficiently distribute tasks across multiple cores or machines, accelerating the execution of large-scale pipelines.
+- Flexibility: It can handle a wide range of computational tasks, from simple data processing to complex simulations.
+- Integration with Tools: Snakemake can easily integrate with various tools and libraries used in high energy physics, such as ROOT, TensorFlow, and PyTorch.
 
 ## Why Snakemake for High Energy Physics?
 
-    Complex Workflows: High energy physics experiments often involve intricate pipelines with numerous steps, from data acquisition and reconstruction to analysis and simulation. Snakemake's declarative syntax and dependency management make it easy to handle such complex workflows.
-    Large Datasets: Snakemake can efficiently process and analyze large datasets generated by high energy physics experiments, thanks to its parallel execution capabilities and integration with data management tools.
-    Reproducibility: By defining workflows in a declarative language, Snakemake ensures that results are reproducible. This is crucial in scientific research where experiments need to be verifiable.
-    Scalability: Snakemake can scale to handle large-scale computational resources, allowing researchers to efficiently utilize HPC clusters for their analyses.
+- Complex Workflows: High energy physics experiments often involve intricate pipelines with numerous steps, from data acquisition and reconstruction to analysis and simulation. Snakemake's declarative syntax and dependency management make it easy to handle such complex workflows.
+- Large Datasets: Snakemake can efficiently process and analyze large datasets generated by high energy physics experiments, thanks to its parallel execution capabilities and integration with data management tools.
+- Reproducibility: By defining workflows in a declarative language, Snakemake ensures that results are reproducible. This is crucial in scientific research where experiments need to be verifiable.
+- Scalability: Snakemake can scale to handle large-scale computational resources, allowing researchers to efficiently utilize HPC clusters for their analyses.
+
+## Understanding the Basics
+
+Snakemake is a workflow management system that simplifies the process of defining complex computational pipelines. It's particularly useful for bioinformatics pipelines, but can be applied to a wide range of computational tasks.
+
+## The Core Components of a Snakemake Workflow
+
+1. Rules: These define the steps in your pipeline. Each rule has three parts:
+    * Input files: The files that the rule needs to start.
+    * Output files: The files that the rule will produce.
+    * Shell command: The command to be executed to produce the output files from the input files.
+2. Config file: This file defines parameters and variables that can be used in your rules.
+3. Snakefile: This is the main file of a Snakemake workflow. It contains the definition of all rules and config file references.
+
+## A Simple Example: A Parallel Workflow
+
+Let's create a simple workflow that simulates a data analysis pipeline. We'll have two steps:
+
+1. Data Simulation: Simulate some data.
+2. Data Analysis: Analyze the simulated data.
+
+We'll use parallel processing to speed up the analysis step.
+
+1. Create the Snakefile:
+
+```YAML
+configfile: "config.yaml"
+
+rule all:
+    input:
+        expand("results/analysis_{sample}.txt", sample=config["samples"])
+
+rule simulate_data:
+    input:
+        configfile = "config.yaml"
+    output:
+        "data/{sample}.txt"
+    shell:
+        "python simulate_data.py --sample {wildcards.sample} > {output}"
+
+rule analyze_data:
+    input:
+        data = "data/{sample}.txt"
+    output:
+        "results/analysis_{sample}.txt"
+    shell:
+        "python analyze_data.py {input} > {output}"
+```