nf-core/smrnaseq

What nf-core/smrnaseq is

nf-core/smrnaseq is a bioinformatics analysis pipeline designed for small RNA sequencing (small RNA-Seq) workflows. It provides an automated workflow to process raw sequencing reads through quality checks, adapter trimming, contaminant removal, and quantification against mature microRNA (miRNA) and hairpin references.

Originally written by P. Ewels, C. Wang, R. Hammarén, L. Pantano, and A. Peltzer, the pipeline was updated to Nextflow DSL2 by Lorena Pantano from MIT with community assistance. It standardises small RNA analyses across diverse compute environments by executing each process in its own software container.

The workflow addresses common challenges in small RNA profiling, including library-specific adapter trimming, unique molecular identifier (UMI) handling, filtering of non-target non-coding RNA contaminants, isomiR characterisation, and the identification of novel miRNAs.

What the pipeline does

The pipeline begins with initial quality control and adapter processing. FastQC assesses raw reads, followed by optional UMI extraction and miRNA adapter handling using UMI-tools. 3' adapters are trimmed and reads are filtered by quality and length using fastp, verified by post-trimming FastQC. Optional UMI deduplication can also be performed using UMICollapse and UMI-tools. Sample quality and species-specific miRNA features are then evaluated using miRTrace. Users can optionally remove non-target contamination—such as rRNA, tRNA, cDNA, ncRNA, and piRNA—by aligning reads against filter references with Bowtie2.

Filtered reads undergo miRNA quantification and discovery through two main tracks. In the primary track, Bowtie1 aligns reads against miRBase mature miRNAs and hairpins, SAMtools processes alignments, and edgeR performs TMM normalisation, multidimensional scaling (MDS) clustering, and sample similarity heatmap generation for miRBase or MirGeneDB hairpins. Concurrently, seqcluster collapses reads and mirtop annotates miRNAs and isomiRs. Optional steps include host genome alignment with Bowtie1 and novel miRNA discovery using MiRDeep2. MultiQC aggregates metrics across all stages into a final summary report.

Key facts

What you can use it for

miRNA expression profiling

Quantifying known mature miRNAs and hairpin precursors against miRBase or MirGeneDB, yielding TMM-normalised counts, top expressed hairpin tables, MDS plots, and sample clustering heatmaps with edgeR.

isomiR annotation and quantification

Collapsing reads with seqcluster and annotating microRNA sequence variants (isomiRs) using mirtop.

Novel microRNA discovery

Mapping reads to a host reference genome and predicting novel and known miRNAs using MiRDeep2.

Contaminant sequence filtration

Removing unwanted reads mapping to rRNA, tRNA, cDNA, ncRNA, and piRNA using Bowtie2 prior to target quantification.

Run it here

The research agent runs nf-core/smrnaseq 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

Philip Ewels, Alexander Peltzer, Sven Fillinger, Harshil Patel, Johannes Alneberg, Andreas Wilm, Maxime Ulysse Garcia, Paolo Di Tommaso & Sven Nahnsen. The nf-core framework for community-curated bioinformatics pipelines. Nat Biotechnol. 2020 Feb 13. doi: 10.1038/s41587-020-0439-x.