Snakemake

Last updated on 2024-12-30 | Edit this page

Overview

Questions

  • How can I automate complex computational pipelines?
  • How can I visualize and understand the structure of my Snakemake workflow?
  • How can I create flexible and scalable Snakemake workflows to handle diverse datasets?

Objectives

  • To introduce Snakemake as a powerful tool for automating complex computational pipelines.
  • To provide a practical guide to creating and executing Snakemake workflows, including the use of rules, wildcards, and configuration files.
  • To demonstrate how to visualize and analyze Snakemake workflows using tools like --dag and --rulegraph to optimize performance and identify potential bottlenecks.

Snakemake: A Flexible Workflow Engine

Snakemake is a workflow management system that simplifies the creation and execution of complex computational pipelines. It’s particularly useful for bioinformatics pipelines, but can be applied to a wide range of computational tasks. It’s particularly well-suited for tasks involving large datasets and parallel processing, making it a popular choice in fields like high energy physics.

Let’s describe the key features of Snakemake:

  • 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.

Understanding the Basics

At its core, Snakemake organizes computational workflows into rules, each representing a specific task within the pipeline. These rules are interconnected through their input and output files, forming a dependency graph. Using a Python-like syntax, you can specify the precise commands required to generate output files from input data. Snakemake intelligently analyzes this dependency graph to determine the optimal execution order and parallelizes tasks to maximize efficiency, making it ideal for large-scale data analysis projects.

The Core Components of a Snakemake Workflow

  1. Snakefile: This is the main file of a Snakemake workflow. It contains the definition of all rules and config file references. This is where you define your pipeline.
  2. Config file: This file defines parameters and variables that can be used in your rules. (Useful, but not mandatory.)
  3. Rules: These define the steps in your pipeline. Each rule has three important 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.

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 Python Scripts:

simulate_data.py:

PYTHON 

import sys

sample = sys.argv[1]
with open(f"data/{sample}.txt", "w") as f:
    f.write(f"Simulated data for {sample}\n")

analyze_data.py:

PYTHON 

import sys

with open(sys.argv[1], "r") as f:
    data = f.read()

with open(f"results/analysis_{sys.argv[1].split('/')[-1]}", "w") as f:
    f.write(f"Analysis of {data}\n")
  1. Create the Config File (config.yaml):

YAML 

samples:
  - sample1
  - sample2
  - sample3
  - sample4
  - sample5
  1. Create the Snakefile:

YAML 

configfile: "config.yaml"

rule all:
    input:
        expand("results/analysis_{sample}.txt", sample=config["samples"])

rule simulate_data:
    output:
        "data/{sample}.txt"
    shell:
        "python simulate_data.py {wildcards.sample} > {output}"

rule analyze_data:
    input:
        "data/{sample}.txt"
    output:
        "results/analysis_{sample}.txt"
    shell:
        "python analyze_data.py {input} > {output}"

Explanation of the Workflow:

  • Rules:

    • rule simulate_data: This rule simulates data for each sample. As it has no input dependencies, it can be executed at the beginning of the pipeline.
    • rule analyze_data: This rule analyzes the simulated data for each sample. It depends on the output of the simulate_data rule, ensuring that this rule only proceeds after simulate_data is complete.
    • rule all: A rule without an output will always run, and therefore it is one of the possible ways to define the final goal of the workflow. (rule all is the common rule name of the final output) By defining the desired output, Snakemake automatically determines the necessary steps and their execution order. This rule also allows for the use of wildcards, which enable flexible and scalable workflows.

  • Parallelism: Snakemake automatically parallelizes the analyze_data rule for each sample, as they are independent of each other.

  • Wildcards: The {wildcards.sample} syntax is used to dynamically generate input and output file names based on the sample name.

Running snakemake

Load your environment or container to launch snakemake. Then to run the simple example:

BASH 

snakemake --snakefile Snakefile --configfile config.yaml --dry-run

Each part of this command serves a specific purpose:

  • --snakefile: This flag specifies the path to the Snakefile, which contains the definitions of the rules and their dependencies.
  • --configfile: This flag indicates the path to the configuration file (YAML format) where you can define parameters and variables that can be used within the Snakefile. (It is not mandatory.)
  • --dry-run: This flag tells Snakemake to simulate the workflow execution without actually running the commands. It’s useful for visualizing the execution order of rules and identifying potential issues before running the actual workflow.

OUTPUT 


Config file config.yaml is extended by additional config specified via the command line.
Building DAG of jobs...
Job stats:
job              count
-------------  -------
all                  1
analyze_data         5
simulate_data        5
total               11


[Wed Nov 13 15:19:33 2024]
rule simulate_data:
    output: data/sample2.txt
    jobid: 4
    reason: Missing output files: data/sample2.txt
    wildcards: sample=sample2
    resources: tmpdir=<TBD>


[Wed Nov 13 15:19:33 2024]
rule simulate_data:
    output: data/sample3.txt
    jobid: 6
    reason: Missing output files: data/sample3.txt
    wildcards: sample=sample3
    resources: tmpdir=<TBD>


[Wed Nov 13 15:19:33 2024]
rule simulate_data:
    output: data/sample4.txt
    jobid: 8
    reason: Missing output files: data/sample4.txt
    wildcards: sample=sample4
    resources: tmpdir=<TBD>


[Wed Nov 13 15:19:33 2024]
rule simulate_data:
    output: data/sample5.txt
    jobid: 10
    reason: Missing output files: data/sample5.txt
    wildcards: sample=sample5
    resources: tmpdir=<TBD>


[Wed Nov 13 15:19:33 2024]
rule simulate_data:
    output: data/sample1.txt
    jobid: 2
    reason: Missing output files: data/sample1.txt
    wildcards: sample=sample1
    resources: tmpdir=<TBD>


[Wed Nov 13 15:19:33 2024]
rule analyze_data:
    input: data/sample5.txt
    output: results/analysis_sample5.txt
    jobid: 9
    reason: Missing output files: results/analysis_sample5.txt; Input files updated by another job: data/sample5.txt
    wildcards: sample=sample5
    resources: tmpdir=<TBD>


[Wed Nov 13 15:19:33 2024]
rule analyze_data:
    input: data/sample1.txt
    output: results/analysis_sample1.txt
    jobid: 1
    reason: Missing output files: results/analysis_sample1.txt; Input files updated by another job: data/sample1.txt
    wildcards: sample=sample1
    resources: tmpdir=<TBD>


[Wed Nov 13 15:19:33 2024]
rule analyze_data:
    input: data/sample4.txt
    output: results/analysis_sample4.txt
    jobid: 7
    reason: Missing output files: results/analysis_sample4.txt; Input files updated by another job: data/sample4.txt
    wildcards: sample=sample4
    resources: tmpdir=<TBD>


[Wed Nov 13 15:19:33 2024]
rule analyze_data:
    input: data/sample3.txt
    output: results/analysis_sample3.txt
    jobid: 5
    reason: Missing output files: results/analysis_sample3.txt; Input files updated by another job: data/sample3.txt
    wildcards: sample=sample3
    resources: tmpdir=<TBD>


[Wed Nov 13 15:19:33 2024]
rule analyze_data:
    input: data/sample2.txt
    output: results/analysis_sample2.txt
    jobid: 3
    reason: Missing output files: results/analysis_sample2.txt; Input files updated by another job: data/sample2.txt
    wildcards: sample=sample2
    resources: tmpdir=<TBD>


[Wed Nov 13 15:19:33 2024]
rule all:
    input: results/analysis_sample1.txt, results/analysis_sample2.txt, results/analysis_sample3.txt, results/analysis_sample4.txt, results/analysis_sample5.txt
    jobid: 0
    reason: Input files updated by another job: results/analysis_sample3.txt, results/analysis_sample1.txt, results/analysis_sample4.txt, results/analysis_sample2.txt, results/analysis_sample5.txt
    resources: tmpdir=<TBD>

Job stats:
job              count
-------------  -------
all                  1
analyze_data         5
simulate_data        5
total               11

Reasons:
    (check individual jobs above for details)
    input files updated by another job:
        all, analyze_data
    output files have to be generated:
        analyze_data, simulate_data

This was a dry-run (flag -n). The order of jobs does not reflect the order of execution.

The --dry-run option is a valuable tool for testing your Snakemake workflow without actually executing the commands. It allows you to visualize the planned execution order of rules, inspect input and output files, and verify the use of wildcards and parameters.

To gain even deeper insights into the specific commands that will be executed, you can employ the --printshellcmds option. This option will print the shell commands associated with each rule, providing a detailed breakdown of the actions that Snakemake will perform.

By combining these options, you can effectively debug, optimize, and fine-tune your Snakemake workflows.

How to validate your workflow

Before diving into the details of your Snakemake workflow, it’s crucial to validate its design and ensure it functions as expected. Snakemake offers several tools for this purpose: --dry-run simulates the workflow, --dag visualizes the dependencies between rules, and --rulegraph provides a more detailed view of data flow. By utilizing these tools, you can effectively debug, optimize, and gain a comprehensive understanding of your pipeline.

--dag

The --dag option generates a Directed Acyclic Graph (DAG) of your workflow. A DAG is a diagram that illustrates the dependencies between rules. Each node in the graph represents a rule, and the edges show the dependencies between rules. By visualizing the DAG, you can:

  • Identify critical paths: Pinpoint the longest sequence of dependent rules that determine the overall workflow duration.
  • Detect potential bottlenecks: Identify rules that might limit the overall workflow performance.
  • Optimize workflow design: Rearrange rules or adjust parallelism to improve efficiency.

Can you try to visualize this simple pipeline? Try with:

BASH 

snakemake --snakefile Snakefile --configfile config.yaml --dag | dot -Tpng -o dag.png
A Directed Acyclic Graph (DAG) showing the dependencies between rules in a Snakemake workflow.
Pipeline Visualization using dag

--rulegraph

The --rulegraph option generates a more detailed graph of your workflow, including information about input and output files. This can be helpful for understanding the flow of data through your pipeline.

Can you try to visualize this simple pipeline? Try with:

BASH 

snakemake --snakefile Snakefile --configfile config.yaml --rulegraph | dot -Tpng -o rulegraph.png
A Rule Graph showing the dependencies between rules in a Snakemake workflow.
Pipeline Visualization using rulegraph

Question:

Can you think about when it can be helpful to use --dry-run, --dag, or --rulegraph

Finally, you can run the workflow by removing the --dry-run flag:

BASH 

snakemake --snakefile Snakefile --configfile config.yaml

if everything ran succesfully, at the end of the output you will have something like:

OUTPUT 


[Thu Nov 14 09:50:51 2024]
Finished job 0.
11 of 11 steps (100%) done
Complete log: .snakemake/log/2024-11-14T095051.589842.snakemake.log

You can also notice that you have two folders: data and results which are the outputs of this simple workflow.

What Happens When You Run Your Workflow Again?

A key feature of Snakemake is its ability to efficiently manage workflow execution based on file timestamps. When you run a Snakemake workflow:

  • File Checks: Snakemake first examines the input and output files specified in your rules.
  • Dependency Analysis: It then analyzes the dependency graph to determine which rules need to be executed.
  • Execution: Only rules whose output files are missing, outdated, or have outdated input files will be executed.

If you rerun the workflow without modifying the input files or deleting the output files, you’ll typically see a message like:

OUTPUT 

Nothing to be done (all requested files are present and up to date).

This behavior ensures that Snakemake avoids unnecessary computations and efficiently utilizes resources.

More about wildcards

Wildcards are powerful tools in Snakemake that enable you to create flexible and scalable workflows. They act as placeholders within rule names and file paths, allowing you to define generic rules that can handle many different input and output files.

How do wildcards work? You can define wildcards within curly braces {} in your Snakemake file. When Snakemake executes a rule, it replaces the wildcard with specific values, allowing the rule to process multiple files.

Example:

rule analyze_sample:
    input:
        "data/{sample}.txt"
    output:
        "results/{sample}_results.txt"
    shell:
        "python analyze.py {input} {output}"

In this example, {sample} is a wildcard. Snakemake will automatically iterate over different sample names and execute the rule for each one, creating specific input and output files.

One can also define wildcards in the rule all rule.

By effectively using wildcards, you can significantly simplify your Snakemake workflows and make them more adaptable to varying datasets and experimental designs.

What’s next?

This is just an overview of the capabilities of Snakemake. As a widely used program, you can find numerous resources online. For more information, you can also visit their official website.

In addition, Snakemake has a public catalog of thousands of workflow in many fields. If you want to see more sophisticated examples, please follow the link to the Snakemake workflow catalog.

Key Points

  • Snakemake automates complex computational pipelines, ensuring efficient resource utilization and avoiding unnecessary computations.
  • The Snakefile contains a list of rules describing the pipeline.
  • Snakemake manages the workflow execution based on file timestamps.
  • Snakemake contains some tools that allows you to debug and validate your workflows, like --dry-run, --dag, or --rulegraph.