nf-core/rnavar

What nf-core/rnavar is

nf-core/rnavar is a bioinformatics analysis pipeline designed for variant discovery from RNA sequencing data following the GATK4 best practices workflow. It identifies single-nucleotide polymorphisms (SNPs) and small insertions and deletions (indels) within transcribed genomic regions. The pipeline was originally developed by Praveen Raj and Maxime U Garcia at The Swedish Childhood Tumor Biobank (Barntumörbanken), Karolinska Institutet, with further development by Nicolas Vannieuwkerke at CMGG.

Maintenance is currently led by Maxime U Garcia at the National Genomics Infrastructure. Variant calling directly from RNA-seq enables researchers to examine expressed alleles and transcriptional variations without necessarily requiring matched whole-genome or whole-exome sequencing data. The pipeline automates every phase of the process, spanning read-level quality control, spliced alignment, duplicate marking, recalibration, variant discovery, filtering, and functional annotation.

What the pipeline does

The pipeline processes single-end or paired-end FASTQ reads declared in a CSV samplesheet. Technical replicates are concatenated with cat, and initial read quality is assessed with FastQC. The workflow can optionally extract unique molecular identifiers (UMIs) using UMI-tools and execute HLA typing with Seq2HLA. Spliced sequence reads are mapped to a reference genome using STAR, after which SAMtools sorts and indexes the alignments. Picard MarkDuplicates marks optical and PCR duplicates.

To prepare spliced alignments for variant calling, GATK4 SplitNCigarReads splits reads containing Ns in their CIGAR strings across exon junctions. Base quality score recalibration is performed with GATK4 BaseRecalibrator and ApplyBQSR over target intervals generated by GATK4 BedToIntervalList and IntervalListTools. GATK4 HaplotypeCaller calls SNPs and indels across genomic intervals, and the resulting VCFs are merged using GATK4 MergeVCFs and indexed with Tabix. Variant calls are subsequently filtered using GATK4 VariantFiltration, annotated using BCFtools Annotate, snpEff, or Ensembl VEP, and summarised in an aggregate quality report produced by MultiQC and R.

Key facts

What you can use it for

RNA-seq SNP and indel calling

Calling expressed small genomic variants from single-end or paired-end RNA sequencing datasets adhering to standard GATK4 best practice guidelines.

HLA typing from RNA reads

Inferring human leukocyte antigen alleles directly from input FASTQ sequencing reads using the optional Seq2HLA module.

UMI-tagged RNA variant identification

Extracting unique molecular identifiers from RNA-seq libraries using UMI-tools prior to mapping and variant evaluation.

Variant effect annotation

Annotating filtered RNA sequence variants with functional predictions and genomic context via Ensembl VEP or snpEff.

Run it here

The research agent runs nf-core/rnavar with Nextflow on its own machine — up to 32 cores, 256 GiB of memory and 24 hours per run, which covers the test profile and real single-sample runs. It builds the samplesheet, checks it against the pipeline's schema and reads the results back. When a run is too big for one machine, it gives you the exact command for your own cluster instead.

Strengths and limits

Questions

How to cite it

10.5281/zenodo.6669636