Sustainable data analysis with                       

2022

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

Data analysis

Reproducibility

Transparency

  • modify analysis
  • extend analysis

Adaptability

>428k downloads since 2015

>1400 citations

>7 citations per week in 2021

  • 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

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

Using and combining workflows

configfile: "config/config.yaml"

module dna_seq:
    snakefile:
        "https://github.com/snakemake-workflows/dna-seq-gatk-variant-calling/raw/v2.0.1/Snakefile"
    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:
        "https://github.com/snakemake-workflows/dna-seq-gatk-variant-calling/raw/v2.0.1/Snakefile"
    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:
        "https://github.com/snakemake-workflows/rna-seq-kallisto-sleuth/raw/v1.0.0/Snakefile"
    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

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

Features

  • human readable language
  • ad-hoc script integration
  • jupyter notebook integration
  • high scalability
  • caching of shared resources
  • Conda and container integration
  • modularization (wrappers, workflows)
  • data-dependent conditional execution
  • streaming/piping between jobs
  • service jobs (providing a shared memory device or a database)
  • helpers for scatter-gather
  • helpers for parameter exploration
  • integrated benchmarking
  • remote file support (S3, FTP, Zenodo, ...)

Snakemake highlights (15min)

By Johannes Köster

Snakemake highlights (15min)

  • 230