Reproducible, transparent, and adaptable data analysis with Snakemake

Johannes Köster

 

2023

dataset

results

Data analysis

"Let me do that by hand..."

dataset

results

dataset

dataset

dataset

dataset

dataset

"Let me do that by hand..."

Data analysis

  • check computational validity
  • apply same analysis to new data
  • check methodological validity
  • understand analysis

Data analysis

Reproducibility

Transparency

  • modify analysis
  • extend analysis

Adaptability

>700k downloads since 2015

>2000 citations

>10 citations per week in 2022

  • automation
  • scalability
  • portability
  • readability
  • documentation
  • traceability

Data analysis

Reproducibility

Transparency

  • readability
  • portability
  • scalability

Adaptability

Data analysis

  • automation
  • scalability
  • portability
  • readability
  • documentation
  • traceability

Reproducibility

Transparency

  • readability
  • portability
  • scalability

Adaptability

dataset

results

dataset

dataset

dataset

dataset

dataset

Define workflows

in terms of rules

Define workflows

in terms of rules

rule mytask:
    input:
        "path/to/{dataset}.txt"
    output:
        "result/{dataset}.txt"
    script:
        "scripts/myscript.R"


rule myfiltration:
     input:
        "result/{dataset}.txt"
     output:
        "result/{dataset}.filtered.txt"
     shell:
        "mycommand {input} > {output}"


rule aggregate:
    input:
        "results/dataset1.filtered.txt",
        "results/dataset2.filtered.txt"
    output:
        "plots/myplot.pdf"
    script:
        "scripts/myplot.R"

Define workflows

in terms of rules

Define workflows

in terms of rules

rule mytask:
    input:
        "data/{sample}.txt"
    output:
        "result/{sample}.txt"
    shell:
        "some-tool {input} > {output}"

rule name

how to create output from input

define

  • input
  • output
  • log files
  • parameters
  • resources
rule mytask:
    input:
        "path/to/{dataset}.txt"
    output:
        "result/{dataset}.txt"
    script:
        "scripts/myscript.R"


rule myfiltration:
     input:
        "result/{dataset}.txt"
     output:
        "result/{dataset}.filtered.txt"
     shell:
        "mycommand {input} > {output}"


rule aggregate:
    input:
        "results/dataset1.filtered.txt",
        "results/dataset2.filtered.txt"
    output:
        "plots/myplot.pdf"
    script:
        "scripts/myplot.R"

Define workflows

in terms of rules

Boilerplate-free integration of scripts

rule mytask:
    input:
        "data/{sample}.txt"
    output:
        "result/{sample}.txt"
    script:
        "scripts/myscript.py"

reusable scripts:

  • Python
  • R
  • Julia
  • Rust
import pandas as pd

data = pd.read_table(snakemake.input[0])
data = data.sort_values("id")
data.to_csv(snakemake.output[0], sep="\t")

Python:

data <- read.table(snakemake@input[[1]])
data <- data[order(data$id),]
write.table(data, file = snakemake@output[[1]])

Boilerplate-free integration of scripts

R:

import polar as pl

pl.read_csv(&snakemake.input[0])
  .sort()
  .to_csv(&snakemake.output[0])

Rust:

Jupyter notebook integration

rule mytask:
    input:
        "data/{sample}.txt"
    output:
        "result/{sample}.txt"
    notebook:
        "notebooks/mynotebook.ipynb"
  1. Integrated interactive edit mode.
  2. Automatic generalization for reuse in other jobs.

Reusable wrappers

rule map_reads:
    input:
        "{sample}.bam"
    output:
        "{sample}.sorted.bam"
    wrapper:
        "0.22.0/bio/samtools/sort"

reuseable wrappers from central repository

Reusable wrappers

Output handling

rule mytask:
    input:
        "data/{sample}.txt"
    output:
        temp("result/{sample}.txt")
    shell:
        "some-tool {input} > {output}"

Output handling

rule mytask:
    input:
        "data/{sample}.txt"
    output:
        protected("result/{sample}.txt")
    shell:
        "some-tool {input} > {output}"

Output handling

rule mytask:
    input:
        "data/{sample}.txt"
    output:
        pipe("result/{sample}.txt")
    shell:
        "some-tool {input} > {output}"

Using and combining workflows

configfile: "config/config.yaml"

module dna_seq:
    snakefile:
        github("snakemake-workflows/dna-seq-gatk-variant-calling", path="workflow/Snakefile", tag="v1.17.0")
    config:
        config

use rule * from dna_seq
# easily extend the workflow
rule plot_vafs:
    input:
        "filtered/all.vcf.gz"
    output:
        "results/plots/vafs.svg"
    notebook:
        "notebooks/plot-vafs.py.ipynb"

Using and combining workflows

configfile: "config/config.yaml"

module dna_seq:
    snakefile:
        github("snakemake-workflows/dna-seq-gatk-variant-calling", path="workflow/Snakefile", tag="v1.17.0")
    config:
        config["dna-seq"]

use rule * from dna_seq as dna_seq_*
# easily extend the workflow
rule plot_vafs:
    input:
        "filtered/all.vcf.gz"
    output:
        "results/plots/vafs.svg"
    notebook:
        "notebooks/plot-vafs.py.ipynb"
module rna_seq:
    snakefile:
        github("snakemake-workflows/rna-seq-kallisto-sleuth", path="workflow/Snakefile", tag="v1.0.0")
    config:
        config["rna-seq"]

use rule * from rna_seq as rna_seq_*

Data analysis

  • automation
  • scalability
  • portability
  • readability
  • documentation
  • traceability

Reproducibility

Transparency

  • readability
  • portability
  • scalability

Adaptability

\max U_t \cdot 2S \cdot \sum_{j \in J} x_j \cdot p_j + 2S \cdot \sum_{j \in J} x_j \cdot (u_{t,j}) + S \cdot \sum_{f \in F} \gamma_f \cdot S_f\\ + \sum_{f \in F} \delta_f \cdot S_f
\sum_{j \in J} x_j \cdot u_{r,j} \leq U_r \quad \forall r \in R
\delta_f \leq \frac{\sum_{j \in J} x_j \cdot z_{f,j}}{\sum_{j \in J} z_{f,j}} \quad\forall f \in F
\text{subject to:}

job selection

job resource usage

free resources

job temp file consumption

temp file lifetime fraction

job priority

job thread usage

Scheduling

temp file size

temp file deletion

\gamma_f \leq \delta_f \quad\forall f \in F
\gamma_f \in \{0,1\}
\delta_f \in [0,1]
x_f \in \{0,1\}

DAG partitioning

--groups a=g1 b=g1
--groups a=g1 b=g1
--group-components g1=2
--groups a=g1 b=g1
--group-components g1=5

Scalable to any platform

workstation

compute server

cluster

grid computing

cloud computing

MPI support

rule calc_pi:
  output:
      "pi.calc",
  resources:
      tasks=10,
      mpi="mpiexec",
  shell:
      "{resources.mpi} -n {resources.tasks} calc-pi-mpi 10 > {output}"

Between workflow caching

dataset

results

dataset

dataset

dataset

dataset

dataset

shared data

Between workflow caching

Data analysis

  • automation
  • scalability
  • portability
  • readability
  • documentation
  • traceability

Reproducibility

Transparency

  • readability
  • portability
  • scalability

Adaptability

rule mytask:
    input:
        "path/to/{dataset}.txt"
    output:
        "result/{dataset}.txt"
    conda:
        "envs/some-tool.yaml"
    shell:
        "some-tool {input} > {output}"

Conda integration

channels:
 - conda-forge
dependencies:
  - some-tool =2.3.1
  - some-lib =1.1.2

Container integration

rule mytask:
    input:
        "path/to/{dataset}.txt"
    output:
        "result/{dataset}.txt"
    container:
        "docker://biocontainers/some-tool#2.3.1"
    shell:
        "some-tool {input} > {output}"

Containerization

containerized:
    "docker://username/myworkflow:1.0.0"


rule mytask:
    input:
        "path/to/{dataset}.txt"
    output:
        "result/{dataset}.txt"
    conda:
        "envs/some-tool.yaml"
    shell:
        "some-tool {input} > {output}"
snakemake --containerize > Dockerfile

Data analysis

  • automation
  • scalability
  • portability
  • readability
  • documentation
  • traceability

Reproducibility

Transparency

  • readability
  • portability
  • scalability

Adaptability

Self-contained HTML reports

Snakemake workflow catalog

Conclusion

With

  • the human readable specification language
  • reusable modularization capabilities
  • seamless execution on all platforms without adaptation of the workflow definition
  • integrated package management and containerization
  • interactive reporting

Snakemake covers all aspects of fully reproducible, transparent, and adaptable data analysis.

Sustainable data analysis with Snakemake

By Johannes Köster

Sustainable data analysis with Snakemake

Non-bio-related talk about Snakemake

  • 1,586

More from Johannes Köster