A practical guide to nf-core/rnaseq: what the pipeline does, how the samplesheet and reference work, which aligner route to choose, and how to run it on Bridge Horizon so every result stays reproducible.
nf-core/rnaseq takes raw sequencing reads and returns counts you can analyse. Along the way it checks read quality, trims adapters, aligns or pseudo-aligns against your reference, quantifies genes and transcripts, and produces a single MultiQC report summarising how the run went.
It is written in Nextflow and every step runs inside a pinned container, which is what makes the pipeline portable: the same release and parameters give the same output on a cluster or in the cloud. The hard parts in practice are not the science, they are the samplesheet, the reference build, the compute configuration, and remembering exactly which version produced last year's figures.
On Bridge Horizon, a bioinformatician sets the pipeline release, reference, and defaults once. After that anyone on the team launches it on their own data, and the inputs, parameters, containers, and outputs are recorded together as one tracked run.
How Bridge Horizon runs it
Upload reads directly or connect a sequencing run. No copying files between a laptop, a share drive, and a cluster.
A guided sample sheet captures read pairs, strandedness, and groups, so the most common cause of failed nf-core runs is caught before you launch.
Choose a pinned nf-core/rnaseq release and a reference genome and annotation. Both are recorded with the run.
STAR with Salmon, STAR with RSEM, HISAT2, or a pseudo-alignment route. Your bioinformatician can set a validated default for the lab.
Nextflow handles parallelism and compute. When it finishes, review the MultiQC report and the count matrices in the same workspace.
Send counts straight into differential expression, share the run with your PI or collaborators in seconds, and re-run the identical configuration later.
Reproducible bioinformatics workflows
Reproducibility shouldn't depend on whoever ran the analysis still being on the team. Bridge Horizon captures everything that went into a result, the pipeline version, parameters, reference, container image, and inputs, so any teammate can re-run it identically months or years later.
Pipelines are pinned to a specific version on every run, so an old analysis doesn't silently change when the pipeline does.
Every parameter, input file, and reference is recorded automatically. No lab notebook entries to maintain by hand.
When a reviewer, client, or auditor asks how a figure was made, you can show them the full chain in one click.
FAQ
Built on production genomics experience since 2020, Horizon makes every dataset, pipeline run, and parameter shared and traceable across your entire team.