2018-07-17_S+C

Reproducible data analysis with Nextflow & NF-core

Alexander Peltzer

Quantitative Biology Center (QBiC) Tübingen

Outlook

Challenges in computational biology
Basic principles of Nextflow
Introduction to NF-core project

Challenges: Big Data

Data in computational biology is
- big (PB scale)
- diverse (sequencing, proteomics, metabolomics ...)
- erroneous (e.g. contains sequencing errors)

We need methods and tools to analyze such data!

Challenges: Big Data - ICGC

Text

"Hyper-Moore gap"

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Credit to Swaine Chen, Genome Institute of Singapore, AWS Summit 2018

The FAIR* principle

Findable

Accessible

Interoperable

Reproducible

The FAIR Guiding Principles for scientific data management and stewardship, Wilkinson et al. 2016 qPortal: A platform for data-driven biomedical research, Mohr et al. 2018

DOI

qPortal

Challenges: Software dependencies

Workflows / Pipelines consist of

different tools
dozens of individual methods

Complex dependency trees and configuration requirements!

Steinbiss et al., "Companion: a web server for annotation and analysis of parasite genomes", NAR 2016

Challenges: Reproducibility

Large-scale projects more common today
- 1,000 Genomes Project
- 100,000 Genomes Project UK
Reproduce results with older data / integrate with newer data
Challenging: Many paper results are not reproducible at all or require a lot of effort

Challenges: Reproducibility

"We estimated the overall time to reproduce the method as 280 hours for a novice with minimal expertise in bioinformatics."

Challenges: Environmental stability

Portability and stability of code between different OS should be ensured
Are results different? Yes, they are ...

Challenges: Software dependencies

diTommaso et al., 2017, Nature Biotechnology

Nextflow

Custom DSL (domain-specific language) for
- fast prototyping
- enabling task composition
- easy parallelization
Self-contained: Containerize tasks (e.g. with Docker)
Isolation of dependencies: Keep container - rerun analysis at any point!

(credit to E Floden, CRG Barcelona)

Nextflow: Centralised Orchestration

Nextflow

Cluster

Submit jobs to cluster nodes
Store data on shared storage

Storage

Nextflow: Cloud deployment (AWS)

(credit to E Floden, CRG Barcelona)

Platform support

(credit to E Floden, CRG Barcelona)

Nextflow: Executor abstraction

Improves code portability

#Run me locally
process.executor = 'local'

#Run on AWS Batch
process.executor = 'awsbatch'

#Run on Kubernetes cluster
process.executor = 'k8s'

Community effort to collect production ready analysis pipelines
Save time in development, more testing, more updates
https://nf-co.re

Phil Ewels

Alex Peltzer

Sven Fillinger

Andreas Wilm

Maxime Garcia

+ many others!

Tiffany Delhomme

Community effort to collect production ready analysis pipelines
Save time in development, more testing, more updates
Initially supported by SciLifeLab, QBiC and A*Star Genome Institute Singapore

All pipelines adhere to requirements

Nextflow based
MIT license
Software bundled in Docker
Continuous integration testing (e.g. Travis CI)
Stable release tags
Common pipeline usage and structure

Dockerfiles

FROM nfcore/base
MAINTAINER Phil Ewels <phil.ewels@scilifelab.se>
LABEL authors="phil.ewels@scilifelab.se" \
    description="Docker image containing all requirements for the nfcore/rnaseq pipeline"

COPY environment.yml /
RUN conda env create -f /environment.yml && conda clean -a
ENV PATH /opt/conda/envs/nfcore-rnaseq-1.5dev/bin:$PATH

Dockerfiles

Bioconda package based, clean-style

name: nfcore-rnaseq-1.5dev
channels:
  - bioconda
  - conda-forge
  - defaults
dependencies:
  - conda-forge::openjdk=8.0.144
  - fastqc=0.11.7
  - trim-galore=0.4.5
  - star=2.6.0c
  - hisat2=2.1.0
  - picard=2.18.7
  - bioconductor-dupradar=1.8.0
  - conda-forge::r-data.table=1.11.4
  - conda-forge::r-gplots=3.0.1
  - bioconductor-edger=3.20.7
  - conda-forge::r-markdown=0.8
  - preseq=2.0.3
  - rseqc=2.6.4
  - samtools=1.8
  - stringtie=1.3.4
  - subread=1.6.1
  - multiqc=1.5

Docker/Singularity processes