PiGx: reproducible genomics analysis pipelines with GNU Guix

Abstract

In bioinformatics, as well as other computationally intensive research fields, there is a need for workflows that can reliably produce consistent output, from known sources, independent of the software environment or configuration settings of the machine on which they are executed. Indeed, this is essential for controlled comparison between different observations and for the wider dissemination of workflows. However, providing this type of reproducibility and traceability is often complicated by the need to accommodate the myriad dependencies included in a larger body of software, each of which generally comes in various versions. Moreover, in many fields (bioinformatics being a prime example), these versions are subject to continual change due to rapidly evolving technologies, further complicating problems related to reproducibility. Here, we propose a principled approach for building analysis pipelines and managing their dependencies with GNU Guix. As a case study to demonstrate the utility of our approach, we present a set of highly reproducible pipelines called PiGx for the analysis of RNA sequencing, chromatin immunoprecipitation sequencing, bisulfite-treated DNA sequencing, and single-cell resolution RNA sequencing. All pipelines process raw experimental data and generate reports containing publication-ready plots and figures, with interactive report elements and standard observables. Users may install these highly reproducible packages and apply them to their own datasets without any special computational expertise beyond the use of the command line. We hope such a toolkit will provide immediate benefit to laboratory workers wishing to process their own datasets or bioinformaticians seeking to automate all, or parts of, their analyses. In the long term, we hope our approach to reproducibility will serve as a blueprint for reproducible workflows in other areas. Our pipelines, along with their corresponding documentation and sample reports, are available at http://bioinformatics.mdc-berlin.de/pigx

Conservation of a core neurite transcriptome across neuronal types and species

“…Raw RNA‐seq data were downloaded from the NCBI‐GEO or EBI ArrayExpress databases (Table S1) and processed using the PiGx RNA‐seq pipeline version 0.0.10 (Wurmus et al, ) using default settings and ENSEMBLE genome assemblies for mouse (GRCm38.p6, release 96), human (GRCh38.p13, release 97), and rat (Rnor_6.0, release 98). Based on the initial analysis reports generated by the PiGx pipeline individual replicates that did not separate well between neurite and soma samples were excluded from further analysis (Table S1).…”

“…(b) Annotation of the datasets: model system and cell type; citation; the species of a dataset (human, mouse, or rat); the separation method for outgrowths and cell bodies; the method of data acquisition. RNA‐seq studies were re‐analyzed using the PiGx RNA‐seq pipeline (Wurmus et al, ). If studies used different analysis methods (*) or raw data was unavailable (**), transcript detection levels were obtained from supplementary data of the respective studies…”

The conserved histone chaperone LIN‐53 is required for normal lifespan and maintenance of muscle integrity in Caenorhabditis elegans

“…The ChIP‐seq sequencing datasets were processed using the PiGx‐ChIP‐seq (Wurmus et al, ) pipeline (version 0.0.16), in which the quality of the raw fastq reads was improved using Trim Galore (https://www.bioinformatics.babraham.ac.uk/projects/trim_galore/), processed reads were aligned to the DNA sequence assembly WBcel235 (Ensembl version 89) using Bowtie2 (Langmead & Salzberg, ), the peaks were called using MACS2 (Zhang et al, ), and the IDR (irreproducible discovery rate) peaks were called using the IDR software (Li, Qunhua Li, Brown, Huang, & Bickel, ).…”

PDGFA-associated protein 1 protects mature B lymphocytes from stress-induced cell death and promotes antibody gene diversification

“…The RNA-seq data were analyzed using the pigx-rnaseq pipeline (Wurmus et al, 2018). STAR (Dobin et al, 2013) mapped the reads to the GRCm38 assembly for mouse by Ensembl, and HTSeq (Anders et al, 2015) was used to count the transcript abundance.…”