nf-core/nanoseq

What nf-core/nanoseq is

nf-core/nanoseq is a bioinformatics workflow engineered for Oxford Nanopore Technologies (ONT) sequencing datasets. It handles both DNA and direct or amplified RNA sequencing reads, providing a standardised route from raw signals or basecalled reads through demultiplexing, quality control, genome alignment, and specialised downstream analyses.

Originally authored by Chelsea Sawyer and Harshil Patel at The Francis Crick Institute, with key contributions from researchers at the Genome Institute of Singapore, the Institute of Medical Genetics and Applied Genomics in Tübingen, and SciLifeLab, the pipeline addresses the analytical complexity of long-read sequencing. By combining standard long-read aligners with modular variant calling and transcriptomic profiling packages, it simplifies diverse Nanopore data processing tasks.

The pipeline operates using Nextflow DSL2 with isolated software containers, ensuring reproducibility across high-performance computing clusters and cloud services. Automated continuous integration runs against full-scale datasets from the Singapore Nanopore Expression Consortium on AWS to validate resource management and software consistency across releases.

What the pipeline does

The analysis begins with optional demultiplexing via qcat and read cleaning with NanoLyse, followed by quality assessment using NanoPlot and FastQC. Reads are aligned to reference genomes using either minimap2 or GraphMap2, both of which accommodate spliced and unspliced reads according to user settings and input types. If multiplexed samples map to distinct genomes, individual references can be assigned per sample. Alignments are processed with samtools to generate coordinate-sorted BAM files, and coverage tracks are exported as bigWig and bigBed files using BEDTools, bedGraphToBigWig, and bedToBigBed.

Downstream execution branches by protocol. For DNA, the pipeline performs small-variant calling via Medaka, DeepVariant, or PEPPER-Margin-DeepVariant, alongside structural variant calling using Sniffles or cuteSV. For RNA, transcript reconstruction and quantification are performed with either bambu or StringTie2 (paired with featureCounts). Transcriptomic runs can also perform differential expression with DESeq2 and DEXSeq, RNA modification detection using m6anet and xpore, and RNA fusion gene identification with JAFFAL. Final summary metrics are aggregated into a MultiQC report.

Key facts

What you can use it for

Nanopore DNA variant discovery

Align raw or basecalled DNA reads and identify small variants with DeepVariant, Medaka, or PEPPER-Margin-DeepVariant, as well as structural variants using Sniffles or cuteSV.

Direct RNA and cDNA transcriptomics

Reconstruct and quantify transcripts using bambu or StringTie2 with featureCounts, followed by gene and transcript differential expression testing with DESeq2 and DEXSeq.

Epitranscriptomic modification detection

Profile RNA base modifications across long reads using dedicated algorithms including m6anet and xpore.

RNA fusion identification

Detect gene fusions in long-read transcriptomic sequencing runs through JAFFAL integration.

Run it here

The research agent runs nf-core/nanoseq 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.