Johannes Köster

 

https://koesterlab.github.io

The road to reproducibility

-

Snakemake in 2017

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results

Data analysis

"Let me do that by hand..."

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"Let me do that by hand..."

Data analysis

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automation

From raw data to final figures:

  • document parameters, tools, versions
  • execute without manual intervention

Reproducible data analysis

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scalability

Handle parallelization:

  • execute for tens to thousands of datasets
  • efficiently use any computing platform

automation

Reproducible data analysis

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Handle deployment:

be able to easily execute analyses on a different machine

portability

scalability

automation

Reproducible data analysis

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portability

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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

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

The script directive

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

# read table
table = pd.read_table(snakemake.input[0])
# sort values
table.sort_values("somecol", inplace=True)

# write output
table.to_csv(snakemake.output.somename, sep="\t")

how to create output from input

# read table
table <- read.table(snakemake@input[[1]])

# sort values
table <- table[order(table$somecol), ]

# write table
write.table(table, file = snakemake@output[["somename"]])

how to create output from input

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

The script directive

from snakemake.remote import S3


s3 = S3.RemoteProvider()


rule mytask:
    input:
        s3.remote("data/{sample}.txt")
    output:
        "result/{sample}.txt"
    script:
        "scripts/myscript.R"

Remote files

Remote files

  • Amazon S3
  • google storage
  • (S)FTP
  • HTTP(S)
  • Dropbox
  • WebDav
  • XRootD
  • GridFTP
  • GFAL
  • NCBI

Mirror entire I/O to remote

snakemake --default-remote-provider GS --default-remote-prefix mybucket

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scalability

automation

portability

Scheduling

Paradigm:

Workflow definition shall be independent of computing platform and available resources

Rules:

define resource usage (threads, memory, ...)

Scheduler:

  • solves multidimensional knapsack problem
  • schedules independent jobs in parallel
  • passes resource requirements to any backend

Scalable to any platform

workstation

compute server

cluster

grid computing

cloud computing

Command-line interface

# execute workflow locally with 16 CPU cores
snakemake --cores 16


# execute on cluster
snakemake --cluster qsub --jobs 100


# execute in the cloud
snakemake --kubernetes --jobs 1000 --default-remote-provider GS --default-remote-prefix mybucket

Configuration profiles

snakemake --profile slurm --jobs 1000
$HOME/.config/snakemake/slurm
├── config.yaml
├── slurm-jobscript.sh
├── slurm-status.py
└── slurm-submit.py

Configuration profiles

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Full reproducibility:

install required software and all dependencies in exact versions

portability

scalability

automation

Software installation is a pain

source("https://bioconductor.org/biocLite.R")
biocLite("DESeq2")
easy_install snakemake
./configure --prefix=/usr/local
make
make install
cp lib/amd64/jli/*.so lib
cp lib/amd64/*.so lib
cp * $PREFIX
cpan -i bioperl
cmake ../../my_project \
    -DCMAKE_MODULE_PATH=~/devel/seqan/util/cmake \
    -DSEQAN_INCLUDE_PATH=~/devel/seqan/include
make
make install
apt-get install bwa
yum install python-h5py
install.packages("matrixpls")

Package management with

package:
  name: seqtk
  version: 1.2

source:
  fn: v1.2.tar.gz
  url: https://github.com/lh3/seqtk/archive/v1.2.tar.gz

requirements:
  build:
    - gcc
    - zlib
  run:
    - zlib

about:
  home: https://github.com/lh3/seqtk
  license: MIT License
  summary: Seqtk is a fast and lightweight tool for processing sequences

test:
  commands:
    - seqtk seq

Idea:

Normalization installation via recipes

#!/bin/bash

export C_INCLUDE_PATH=${PREFIX}/include
export LIBRARY_PATH=${PREFIX}/lib

make all
mkdir -p $PREFIX/bin
cp seqtk $PREFIX/bin
  • source or binary
  • recipe and build script
  • package

Easy installation and management:

no admin rights needed

conda install pandas

conda update pandas

conda remove pandas

conda env create -f myenv.yaml -n myenv

Isolated environments:

channels:
  - conda-forge
  - defaults
dependencies:
  - pandas ==0.20.3
  - statsmodels ==0.8.0
  - r-dplyr ==0.7.0
  - r-base ==3.4.1
  - python ==3.6.0

Package management with

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

Integration with Snakemake

channels:
  - conda-forge
  - defaults
dependencies:
  - mycommand ==2.3.1

Over 3000 bioinformatics related packages

Over 200 contributors

Singularity

rule mytask:
    input:
        "path/to/{dataset}.txt"
    output:
        "result/{dataset}.txt"
    singularity:
        "docker://biocontainers/mycommand"
    shell:
        "mycommand {input} > {output}"

Coming soon:

Singularity + Conda

rule mytask:
    input:
        "path/to/{dataset}.txt"
    output:
        "result/{dataset}.txt"
    singularity:
        "docker://ubuntu:16.04"
    conda:
        "envs/mycommand.yaml"
    shell:
        "mycommand {input} > {output}"

Snakemake Wrappers

rule star:
    input:
        sample=["reads/{sample}.1.fastq", "reads/{sample}.2.fastq"]
    output:
        "star/{sample}/Aligned.out.bam"
    log:
        "logs/star/{sample}.log"
    params:
        # path to STAR reference genome index
        index="index",
        # optional parameters
        extra=""
    threads: 8
    wrapper:
        "0.19.3/bio/star/align"
  • deploy software via Conda
  • obtain script from git repository

Snakemake Wrappers

import os
from snakemake.shell import shell

extra = snakemake.params.get("extra", "")
log = snakemake.log_fmt_shell(stdout=True, stderr=True)

n = len(snakemake.input.sample)
assert n == 1 or n == 2, "input->sample must have 1 (single-end) or 2 (paired-end) elements."

if snakemake.input.sample[0].endswith(".gz"):
    readcmd = "--readFilesCommand zcat"
else:
    readcmd = ""


outprefix = os.path.dirname(snakemake.output[0]) + "/"


shell(
    "STAR "
    "{snakemake.params.extra} "
    "--runThreadN {snakemake.threads} "
    "--genomeDir {snakemake.params.index} "
    "--readFilesIn {snakemake.input.sample} "
    "{readcmd} "
    "--outSAMtype BAM Unsorted "
    "--outFileNamePrefix {outprefix} "
    "--outStd Log "
    "{log}")

Snakemake Wrappers

Snakemake Workflows

Sustainable publishing

Goal:

  • persistently store reproducible data analysis
  • ensure it is always accessible

Minimize dependencies:

  • third-party resources
  • proprietary formats

Solution:

  • store everything (including packages) in an archive
  • upload to persistent storage like https://zenodo.org
  • obtain document object identifier (DOI)

Sustainable publishing

# archive workflow (including Conda packages)
snakemake --archive myworkflow.tar.gz

Author:

  1.  
  2. Upload to Zenodo and acquire DOI.
  3. Cite DOI in paper.

Reader:

  1. Download and unpack workflow archive from DOI.
  2.  
# execute workflow (Conda packages are deployed automatically)
snakemake --use-conda --cores 16

Conclusion

  • Fully reproducible data analyses have to consider three dimensions.
  • In the last months, Snakemake has seen improvements in all three dimensions of reproducibility.
  • The result is an almost complete and future-proof solution to one of the major scientific challenges of today.

portability

scalability

automation

The road to reproducibility - Snakemake in 2017

By Johannes Köster

The road to reproducibility - Snakemake in 2017

Keynote at Snakemake Day 2017 in Amsterdam (https://snakemake-days.github.io)

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