RNAseq pipeline

Introduction

cellgeni/rnaseq is a bioinformatics analysis pipeline used for RNA sequencing data at the Cellular Genetics program at the Wellcome Sanger Institute.

The pipeline uses Nextflow, a bioinformatics workflow tool. Input may be CRAM files from IRODS storage or fastq files from an input directory. In both cases a sample file with a list of sample IDs is used to associate a single primary identifier with a sample.

In its primary mode the rnaseq pipeline performs four main tasks; read alignment, quality control, feature counts, and merging of gene counts and/or transcript counts. Three aligners can be used: STAR, hisat2, and salmon. It is possible to run any subset of these.

Count matrices for both STAR and hisat2 are created using featureCounts. For salmon merged count matrices are created directly from its outputs, and additionally a count matrix is created from gene counts produced by STAR itself.

A secondary mode is the ability to run mixcr. It can be run concurrently or independently from the primary rnaseq mode. We envision that other modes will be added to this pipeline in a similar manner.

This pipeline is primarily used with an LSF cluster and an OpenStack private cloud. However, the pipeline should be able to run on any system that Nextflow supports. See the installation docs for more information.

Documentation

The RNAseq pipeline comes with documentation about the pipeline, found in the docs/ directory.

Credits

This pipeline was forked from the amazing nf-core/rnaseq pipeline, originally developed at the National Genomics Infrastructure at SciLifeLab in Stockholm, Sweden by Phil Ewels (@ewels) and Rickard Hammarén (@Hammarn).

Our pipeline has diverged in a few ways, as described below. It still closely resembles the nf-core pipeline in the sense that processes look similar or are virtually identical. Most changes are either the removal or addition of a process, or a reworking of the pipeline plumbing. This is testament to how nicely Nextflow isolates process logic and definitions from workflow orchestration.

• Support for running on Kubernetes was added, using Docker containers. We adhere to a strict one-process one-container mapping, where possible using off-the shelf biocontainers.

• Integration of IRODS input was added. This introduced two processes: pulling the data from IRODS and converting cram files to fastq files.

• We use the sample ID as the primary process ID and tag everywhere.

• The building of indexes was split off into a separate file buildindex.nf. This file still needs a lot of work to recover from the splitting step.

• All code was pulled out of conditional statements, enabling us to run different modes at the same time. This is helpful for testing, reduces overhead, and reduces the barrier to using different modes.

• mixcr was added as additional mode.

• We've added lost cause channels, tracking which samples are lost at what stage of the pipeline, if any. A lost cause section will be present in the MultiQC report if a sample was disregarded, for example because of low mapping percentage.

Diagram

This is the flow chart of the pipeline.