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Reproducible Bioinformatics Pipelines with Docker & Anduril

Christian Frech, PhDBioinformatician at Children‘s Cancer Research Institute, Vienna

CeMM Special SeminarSeptember 25th, 2015

Why care about reproducible pipelines in bioinformatics?

For your (future) self Quickly re-run analysis with different parameters/tools Best documentation how results have been produced

For others Allow others to easily reproduce your findings

(“reproducibility crisis”)*

Code re-use between projects and colleagues

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*) http://theconversation.com/science-is-in-a-reproducibility-crisis-how-do-we-resolve-it-16998

Obstacles to computational reproducibility

Software/script not available (even upon request) Black box: Code (or even virtual machine) available, but no

documentation how to run it Dependency hell: Software and documentation available,

but (too) difficult to get it running

Code rot: Code breaks over time due to software updates 404 Not Found: unstable URLs, e.g. links to lab homepages

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Go figure…

Computational pipelines to the rescue

In bioinformatics, data analysis typically consists of a series of heterogeneous programs stringed together via file-based inputs and outputs

Example: FASTQ -> alignment (BWA) -> variants calling (GATK) -> variant annotation (SnpEff) -> custom R script

Simple automation via (bash/R/Python/Perl) scripting has its limitations

No error checking No partial execution No parallelization

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No shortage of pipeline frameworks Script-based

GNU Make, Snakemake, Bpipe, Ruffus, Drake, Rake, Nextflow, …

GUI-based Galaxy, GenePattern, Chipster, Taverna, Pegasus, … Various commercial solutions for more standardized

workflows (e.g. RNA-seq) Geared toward biologists without programming skills

(“point-and-click”)

5See also https://www.biostars.org/p/79, https://www.biostars.org/p/91301/

Personal wish list for pipeline framework

Script-based (maximum flexibility, minimum overhead) Powerful scripting language Cluster integration (preferably via slurm) Modular (allow code re-use b/w projects and colleagues) Component library for frequent tasks (e.g. join two CSV files) Reporting (HTML, PDF) to share results Free & open-source Bundle scripts/data with execution environment

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What’s wrong with good ol’ GNU make?

Available on all Linux platforms Stood the test of time

(developed in 1970s) Rapid development

(Bash scripting + target rules) Multi-threading (-j parameter)

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No cluster support Arcane syntax, cryptic pattern

rules Half-baked multi-output rules No type checking (everything is a

generic file) Difficult to modularize

(code re-use) Rebuild not triggered by recipe

change No reporting

PRO CON

Anduril

8http://www.anduril.org

Anduril Developed since 2008 at Biomedicum Systems Biology Laboratory,

Helsinki, Finland http://research.med.helsinki.fi/gsb/hautaniemi/

Built for scientific data analysis with focus on bioinformatics Proprietary workflow scripting language “Anduril script”

Possibility to embed native code (Bash/R/Python/Perl) Version 2 will switch to Scala

Open source & free Significo (http://www.significo.fi/) is commercial spin-off offering Anduril

consulting services No widespread adoption (yet?)

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Anduril features Script-based (maximum flexibility, less overhead) Expressive scripting language Cluster integration (preferably via slurm) Modular to allow code re-use (b/w projects and colleagues) Ready-made component library for frequent analysis steps Reporting (HTML, PDF) to share results Free & open-source Bundle scripts/data with execution environment

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X

Example workflow: RNA-seq alignment with GSNAP

inputBamDir = INPUT(path="/data/bam", recursive=false)inputBamFiles = Folder2Array(folder1 = inputBamDir, filePattern = "C57C3ACXX_CV_([^_]+)_.*[.]bam$")

alignedBams = record()for bam : std.iterArray(inputBamFiles) {

gsnap = GSNAP (reads = INPUT(path=bam.file), options = "--npaths=1 --max-mismatches=1 --novelsplicing=0",@cpu = 10, @memory = 40000,@name = "gsnap_" + bam.key

)alignedBams[bam.key] = gsnap.alignment

}

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Anduril script

Execute with$ anduril run workflow.and --exec-mode slurm

Distributed execution on cluster

Embedding native R code in Anduril script

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ensembl = REvaluate(table1 = ucsc,script = StringInput(content=

'''table.out <- table1table.out$chrom <- gsub("^chr", "", table.out$chrom)'''

))

Supports also inlining of Bash, Python, Java, and Perl scripts

Convert UCSC to Ensembl chromosome names in a CSV file containing column ‘chrom’:

Anduril features Script-based (maximum flexibility, less overhead) Expressive scripting language Cluster integration (preferably via slurm) Modular to allow code re-use (b/w projects and colleagues) Ready-made component library for frequent analysis steps Reporting (HTML, PDF) to share results Free & open-source Bundle scripts/data with execution environment

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?

“Lightweight” virtualization technology for Unix-based systems Processes run in isolated namespaces (“containers”), but share same kernel Like VMs: containers portable between systems -> reproducibility! Unlike VMs: instant startup, no resource pre-allocation -> better hardware utilization

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VM Container

How to bundle workflow with execution environment?

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ContainerAnduril

Workflow

Component 1

Component 2

Component 3

Pro: Single container, easy to maintainCon: VM-like approach; huge, monolithic container, difficult to share (against Docker philosophy)

Pro: Completely modularized, easy to re-use/share workflow componentsCon: “container hell”?

Workflow

Anduril

Solution 1 Solution 2

Container AComponent 1

Container BComponent 2

Container CComponent 3

Hybrid solution

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Pro: Workflow completely containerized (= portable); only shared components in common containersCon: Still (but greatly reduced) overhead for container maintenance

WorkflowAnduril

Container AComponent 1

Component 2

Component 3

Master containerProject- and user-specific components installed in master container

Shared components installed in common container (e.g. container “RNA-seq”)

“Docker inside docker”

Dockerized GSNAP in Anduril

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inputBamDir = INPUT(path="/data/bam", recursive=false)inputBamFiles = Folder2Array(folder1 = inputBamDir, filePattern = "C57C3ACXX_CV_([^_]+)_.*[.]bam$")

alignedBams = record()for bam : std.iterArray(inputBamFiles) {

gsnap = GSNAP (reads = INPUT(path=bam.file), options = "--npaths=1 --max-mismatches=1 --novelsplicing=0",docker = "cfrech/anduril-gsnap-2015-09-21",@cpu = 10, @memory = 40000,@name = "gsnap_" + bam.key

)alignedBams[bam.key] = gsnap.alignment

}

So, Anduril is great… but Proprietary scripting language

Biggest hurdle for widespread adoption IMO Will likely improve with version 2 (which uses Scala)

Documentation opaque for beginners WANTED: Simple step-by-step guide to build your first Anduril workflow

High upfront investment to get going (because of the above) In-lining Bash/R/Perl/Python should be simpler

Currently too much clutter when using “BashEvaluate” and alike Coding in Anduril sometimes “feels heavy” compared to other frameworks

(e.g. GNU Make) Will improve with fluency in workflow scripting language

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Anduril RNA-seq case study

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RNA-seq case studyStep 1: Configure Anduril workflow

title = “My project long title“shortName = “My project short title“authors = "Christian Frech"

// analyses to run

runNetworkAnalysis = truerunMutationAnalysis = truerunGSEA = true

// constants

PROJECT_BASE="/mnt/projects/myproject“gtf = INPUT(path=PROJECT_BASE+"/data/Homo_sapiens.GRCh37.75.etv6runx1.gtf.gz")referenceGenomeFasta = INPUT(path="/data/reference/human_g1k_v37.fasta")

...

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+ description of samples, sample groups, and group comparisons in external CSV file

RNA-seq case studyStep 2: Run Anduril workflow on cluster

$ anduril run main.and --exec-mode slurm

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RNA-seq case studyStep 3: Go for lunch

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RNA-seq case studyStep 4: Study PDF report

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What follows are screenshots from this PDF report

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QC: Read counts

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QC: Gene body coverage

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QC: Distribution of expression values per sample

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QC: Sample PCA & heatmap

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Vulcano plot for each comparison

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Table report of DEGs for each comparison

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Expression values of top diff. expressed genes per comparison

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GO term enrichment for each comparison

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Interaction network of DEGs for each comparison

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Chromosomal distribution of DEGs

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GSEA heat map summarizing all comparisons

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Rows = enriched gene setsColumns = comparisonsValue = normalized enrichment score (NES)Red = enriched for up-regulated genesBlue = enriched for down-regulated genes* = significant (FDR < 0.05)** = highly significant (FDR < 0.01)

Future developments Push new Anduril components to public repository

(needs some refactoring, documentation, test cases) Help on Anduril2 manuscript Port custom Makefiles to Anduril (ongoing) Cloud deployment of dockerized workflow

Couple slurm to AWS EC2 Automatic spin-up of docker-enabled AMIs serving as

computing nodes

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In the (not so) distant future …

$ docker pull cfrech/frech2015_et_al

$ docker run cfrech/frech2015_et_al --use-cloud --max-nodes 300 --out output

$ evince output/figure1.pdf

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Further reading

Discussion thread on Docker & Andurilhttps://groups.google.com/forum/#!msg/anduril-dev/Et8-YG9O-Aw/24i4M1pDIfcJ

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Acknowledgement

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Marko Laakso (Significo) Sirku Kaarinen (Significo) Kristian Ovaska (Valuemotive) Pekka Lehti (Valuemotive) Ville Rantanen (University of

Helsinki, Hautaniemi lab) Nuno Andrade (CCRI) Andreas Heitger (CCRI)