nf-core pipeline ·
nf-core/alleleexpression
A Nextflow pipeline for allele-specific expression analysis using STAR-WASP, UMI deduplication, and phASER.
What nf-core/alleleexpression is
Alleleexpression is an nf-core Nextflow pipeline designed for allele-specific expression (ASE) analysis. It quantifies transcript abundance differences between maternal and paternal alleles in RNA sequencing data, integrating read alignment, deduplication, phasing, and statistical testing.
The pipeline mitigates reference mapping bias and amplification artefacts by combining STAR alignment in WASP mode with UMI-tools deduplication. Downstream haplotype phasing and allele-level quantification are handled through Beagle and phASER to provide both haplotype-level and gene-level expression estimates.
What the pipeline does
The workflow begins with input validation, VCF preparation, and raw read quality inspection using FastQC. Reads are aligned to the reference genome with STAR in WASP mode to reduce reference-allele bias, followed by filtering to retain WASP-passing reads. BAM files are deduplicated using UMI-tools, sorted, and indexed.
In parallel, target chromosomes are extracted from input VCF files, and Beagle performs phasing using a reference panel and genetic map. The phased variants and deduplicated alignments are supplied to phASER to reconstruct haplotype-level expression and phASER Gene AE for gene-level counts. Finally, the pipeline evaluates gene-level ASE using either a binomial or beta-binomial test with Benjamini-Hochberg false discovery rate correction, and aggregates run statistics into a MultiQC report.
Key facts
- Pipeline framework
- Nextflow (>=25.04.0)
- Alignment tool
- STAR (WASP mode)
- Deduplication tool
- UMI-tools
- Phasing tools
- Beagle and phASER
- Variant manipulation tools
- samtools and bcftools
- Quality control
- FastQC and MultiQC
- Required inputs
- RNA-seq reads, reference genome, annotation, VCF, Beagle reference panel, and genetic map
- Primary outputs
- FastQC reports, STAR alignments, WASP-filtered BAMs, UMI-deduplicated BAMs, phased VCFs, phASER counts, and ASE tables
What you can use it for
- Haplotype- and Gene-Level ASE Quantification
- Identifies allele-specific expression differences across genes and phased haplotypes from RNA-seq data.
- Bias-Reduced RNA-Seq Alignment
- Maps sequencing reads using STAR-WASP to eliminate reference allele mapping bias at heterozygous variant sites.
- UMI-Deduplicated Allelic Counting
- Removes PCR duplicates using unique molecular identifiers prior to allelic read counting to improve quantification accuracy.
Run it here
The research agent runs nf-core/alleleexpression 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
Good at
- Minimises reference mapping bias using STAR's integrated WASP mapping protocol.
- Accounts for PCR amplification duplicates by integrating UMI-tools deduplication.
- Combines population-level reference phasing via Beagle with read-backed phasing in phASER.
- Supports configurable statistical evaluations via binomial or beta-binomial tests with Benjamini-Hochberg FDR adjustments.
Watch out for
- Requires an external Beagle reference panel and genetic map for variant phasing.
- Running with Docker requires building the phASER container locally prior to execution.
- Support for sex-chromosome analysis remains under active development.
Questions
Can I run nf-core/alleleexpression without my own cluster?
The research agent runs nf-core/alleleexpression 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.
What licence is nf-core/alleleexpression under?
MIT, like every nf-core pipeline.
What inputs are required to run the pipeline?
The pipeline requires RNA-seq reads, a reference genome, genomic annotation files, a VCF containing variant calls, and a Beagle reference panel accompanied by a genetic map.
How does the pipeline prevent reference mapping bias?
It aligns reads using STAR in WASP mode and subsequently filters the output to retain only WASP-passing reads.
Which statistical tests are supported for ASE calling?
Users can select between a binomial test or a beta-binomial test, with Benjamini-Hochberg false discovery rate control and optional effect-size filtering.
Are container images retrieved automatically?
When using Singularity, the phASER container is fetched automatically from Zenodo; when using Docker, the phASER image must be built locally before running the pipeline.
Written from nf-core/alleleexpression's own README. Statements citing a figure that does not appear there are removed before publication. Licence and running details come from our registry.