Introduction to Snakemake

Johannes Köster

 

2015

Why workflow management?

Analyses usually entail the application of various tools, algorithms and scripts.

Workflow management

handles boilerplate:

  • parallelization
  • suspend/resume
  • logging
  • data provenance

Snakemake

Snakemake infers dependencies and execution order.

text based:

Python + domain specific syntax

Decompose workflow into rules.

Rules define how to obtain output files from input files.

Part 1

Basics

Define workflows

in terms of rules

rule sort:
    input:
        "path/to/dataset.txt"
    output:
        "dataset.sorted.txt"
    shell:
        "sort {input} > {output}"

rule name

refer to input and output from shell command

how to create output from input

Define workflows

in terms of rules

rule sort:
    input:
        "path/to/{dataset}.txt"
    output:
        "{dataset}.sorted.txt"
    shell:
        "sort {input} > {output}"

generalize rules with

named wildcards

Define workflows

in terms of rules

rule sort_and_annotate:
    input:
        "path/to/{dataset}.txt",
        "path/to/annotation.txt"
    output:
        "{dataset}.sorted.txt"
    shell:
        "paste <(sort {input[0]}) {input[1]} > {output}"

multiple input or output files

refer by index

Define workflows

in terms of rules

rule sort_and_annotate:
    input:
        a="path/to/{dataset}.txt",
        b="path/to/annotation.txt"
    output:
        "{dataset}.sorted.txt"
    shell:
        "paste <(sort {input.a}) {input.b} > {output}"

name input and output files

refer by name

Define workflows

in terms of rules

rule sort:
    input:
        a="path/to/{dataset}.txt"
    output:
        b="{dataset}.sorted.txt"
    run:
        with open(output.b, "w") as out:
            for l in sorted(open(input.a)):
                print(l, file=out)

use Python in rules

Define workflows

in terms of rules

rule sort:
    input:
        a="path/to/{dataset}.txt"
    output:
        b="{dataset}.sorted.txt"
    script:
        "scripts/myscript.R"

refer to Python or R scripts

(in version 3.5)

Dependencies are determined top-down

  • For a given target, a rule that can be applied to create it is determined (a job).
  • For the input files of the rule, go on recursively.
  • If no target is specified, Snakemake tries to apply the first rule in the workflow.

Dependencies are determined top-down

DATASETS = ["D1", "D2", "D3"]


rule all:
    input:
        expand("{dataset}.sorted.txt", dataset=DATASETS)


rule sort:
    input:
        "path/to/{dataset}.txt"
    output:
        "{dataset}.sorted.txt"
    shell:
        "sort {input} > {output}"

Job 1: apply rule all

(a target rule that just collects results)

Job i: apply rule sort to create i-th input of job 1

use arbitrary Python code in your workflow

Directed acyclic graph (DAG) of jobs

Job execution

A job is executed if and only if

  • output file is target and does not exist
  • output file needed by another executed job and does not exist
  • input file newer than output file
  • input file will be updated by other job
  • execution is enforced

determined via breadth-first-search on DAG of jobs

Command line interface

Assumption: workflow defined in a Snakefile in the same directory.

# execute the workflow with target D1.sorted.txt
snakemake D1.sorted.txt

# execute the workflow without target: first rule defines target
snakemake

# dry-run
snakemake -n

# dry-run, print shell commands
snakemake -n -p

# dry-run, print execution reason for each job
snakemake -n -r

# visualize the DAG of jobs using the Graphviz dot command
snakemake --dag | dot -Tsvg > dag.svg

Part 2

Advanced

Parallelization

Disjoint paths in the DAG of jobs can be executed in parallel.

# execute the workflow with 8 cores
snakemake --cores 8

execute 8 jobs in parallel?

Defining resources

rule sort:
    input:
        "path/to/{dataset}.txt"
    output:
        "{dataset}.sorted.txt"
    threads: 4
    resources: mem_mb=100
    shell:
        "sort --parallel {threads} {input} > {output}"

refer to defined thread number

define arbitrary additional resources

define used threads

Command line interface

Assumption: workflow defined in a Snakefile in the same directory.

# execute the workflow with 8 cores
snakemake --cores 8

# prioritize the creation of a certain file
snakemake --prioritize D1.sorted.txt --cores 8

# execute the workflow with 8 cores and 100MB memory
snakemake --cores 8 --resources mem_mb=100

can execute 2 sort jobs in parallel

can execute only 1 sort job in parallel

Scheduling

Available jobs are scheduled to

  • maximize parallelization
  • prefer high priority jobs
  • while satisfying resource constraints.

Scheduling

\max_{E \subseteq J} \sum_{j \in E}\, (p_j, d_j, i_j)^T
maxE⊆J∑j∈E(pj,dj,ij)T\max_{E \subseteq J} \sum_{j \in E}\, (p_j, d_j, i_j)^T
\sum_{j \in E} r_{ij} \leq R_i \text{ for } i=1,2,...,n
∑j∈Erij≤Ri for i=1,2,...,n\sum_{j \in E} r_{ij} \leq R_i \text{ for } i=1,2,...,n

s.t.

available jobs

priority

descendants

input size

resource usage

free resource (e.g. CPU cores)

Config files

configfile: "config.yaml"


rule all:
    input:
        expand("{dataset}.sorted.txt", dataset=config["datasets"])


rule sort:
    input:
        "path/to/{dataset}.txt"
    output:
        "{dataset}.sorted.txt"
    threads: 4
    resources: mem_mb=100
    shell:
        "sort --parallel {threads} {input} > {output}"

define config file

refer to config values

Input functions

Workflows are executed in three phases

  • initialization phase (parsing)
  • DAG phase (DAG is built)
  • scheduling phase (execution of DAG)

Input functions defer determination of input files to the DAG phase

(when wildcard values are known).

Input functions

configfile: "config.yaml"


rule all:
    input:
        expand("{dataset}.sorted.txt", dataset=config["datasets"])


rule sort:
    input:
        lambda wildcards: config["datasets"][wildcards.dataset]
    output:
        "{dataset}.sorted.txt"
    threads: 4
    resources: mem_mb=100
    shell:
        "sort --parallel {threads} {input} > {output}"

input functions take the determined wildcard values as only argument

Logging

configfile: "config.yaml"


rule all:
    input:
        expand("{dataset}.sorted.txt", dataset=config["datasets"])


rule sort:
    input:
        lambda wildcards: config["datasets"][wildcards.dataset]
    output:
        "{dataset}.sorted.txt"
    log:
        "logs/sort/{dataset}.log"
    threads: 4
    resources: mem_mb=100
    shell:
        "sort --parallel {threads} {input} > {output} 2> {log}"

define log file

refer to log file from shell command

Cluster execution

# execute the workflow on cluster with qsub submission command
# (and up to 100 parallel jobs)
snakemake --cluster qsub --jobs 100

# tell the cluster system about the used threads
snakemake --cluster "qsub -pe threaded {threads}" --jobs 100

# execute the workflow with synchronized qsub
snakemake --cluster-sync "qsub -sync yes" --jobs 100

# execute the workflow with DRMAA
snakemake --drmaa --jobs 100

Many additional features

  • modularization
  • handling of temporary and protected files
  • HTML5 reports
  • rule parameters
  • tracking of tool versions and code changes
  • per file data provenance information
  • a Python API for embedding Snakemake in other tools

Distribution of Snakemake workflows

Solution 1: Git repository with

├── config.yaml
├── requirements.txt
├── scripts
│   ├── script1.py
│   └── script2.R
└── Snakefile
# clone workflow into working directory
git clone https://bitbucket.org/user/myworkflow.git path/to/workdir
cd path/to/workdir

# edit config and workflow as needed
vim config.yaml

# install dependencies into isolated environment
conda create -n myworkflow --file requirements.txt

# activate environment
source activate myworkflow

# execute workflow
snakemake -n

Distribution of Snakemake workflows

Solution 2: Python/Conda package

# install workflow with all dependencies into isolated environment
conda create -n myworkflow myworkflow

# activate environment
source activate myworkflow

# copy Snakefile and config file into working directory
myworkflow init path/to/workdir
cd path/to/workdir

# edit config and workflow as needed
vim config.yaml

# execute workflow
snakemake -n

Distribution of Snakemake workflows

Solution 3: Hide workflow in package

# install workflow with all dependencies into isolated environment
conda create -n myworkflow myworkflow

# activate environment
source activate myworkflow

# copy only config file into working directory
myworkflow init path/to/workdir
cd path/to/workdir

# edit config as needed
vim config.yaml

# execute workflow with a wrapper that uses a Snakefile
# hidden in the package and delegates execution to Snakemake
myworkflow run -n

Acknowledgements

Sven Rahmann, Universität Duisburg-Essen

Christopher Schröder, Universität Duisburg-Essen

Marcel Martin, SciLifeLab Stockholm

Tobias Marschall, Max Planck Institute for Informatics

Sean Davis, NIH

David Koppstein, MIT

Ryan Dale, NIH

Chris Tomkins-Tinch, Broad Institute

Hyeshik Chang, Seoul National University

Karel Brinda, Université Paris-Est Marne-la-Vallée

Anthony Underwood, Public Health England

Elias Kuthe, TU Dortmund

Paul Moore, Atos SE

Mattias Frånberg, Karolinska Institute

Simon Ye, MIT

Willem Ligtenberg, Open Analytics

Per Unneberg, SciLifeLab Stockholm

Matthew Shirley, Johns Hopkins School of Medicine

Jeremy Leipzig, Childrens Hospital of Philadelphia

Kemal Eren

Kyle Meyer

 

all users and supporters

https://bitbucket.org/johanneskoester/snakemake

Köster, Johannes and Rahmann, Sven. "Snakemake - A scalable bioinformatics workflow engine". Bioinformatics 2012.

Köster, Johannes. "Parallelization, Scalability, and Reproducibility in Next-Generation Sequencing Analysis", PhD thesis, TU Dortmund 2014.

Resources

https://bioconda.github.io