nf-core pipeline · consensus, umi, umis
nf-core/fastquorum
A Nextflow pipeline to generate consensus reads from sequencing data using unique molecular identifiers.
What nf-core/fastquorum is
nf-core/fastquorum is a bioinformatics pipeline that implements the fgbio Best Practices FASTQ to Consensus workflow. It processes sequencing reads tagged with unique molecular identifiers (UMIs) to build consensus reads, reducing sequencing errors and artefacts. The workflow accommodates single-UMI, multi-UMI, and Duplex Sequencing protocols.
The pipeline was originally developed and is maintained by Nils Homer and Zach Norgaard from Fulcrum Genomics, with contributions from Simon Pearce. It provides researchers with a standardised method to group raw sequencing reads by their molecular barcodes, align templates, and call high-accuracy consensus sequences.
What the pipeline does
The workflow begins by running FastQC on raw sequencing reads, followed by fgbio FastqToBam to extract UMI sequences based on defined read structures. Reads are subsequently mapped using bwa mem, converted and paired with unmapped reads using fgbio ZipperBam, and template-coordinate sorted using samtools sort. Reads sharing common genomic positions and barcode tags are then grouped with fgbio GroupReadsByUmi.
For Duplex Sequencing libraries, consensus sequences are generated with fgbio CallDuplexConsensusReads alongside duplex-specific QC from fgbio CollectDuplexSeqMetrics. For standard UMI libraries, fgbio CallMolecularConsensusReads generates single-strand molecular consensus reads. Consensus reads are then re-aligned to the reference genome with bwa mem, filtered with fgbio FilterConsensusReads, and compiled into a summary report using MultiQC. The pipeline can be executed in either Research and Development mode (rd) for testing parameters or High Throughput mode (ht) for optimised production environments.
Key facts
- Pipeline framework
- Nextflow (DSL2)
- Input formats
- FASTQ (single-end or paired-end) with samplesheet CSV
- Core tools
- fgbio, bwa, samtools, FastQC, MultiQC
- Execution modes
- Research and Development (rd), High Throughput (ht)
- Version
- 2.0.0
What you can use it for
- Duplex Sequencing analysis
- Processing paired-strand UMI sequencing datasets to call duplex consensus reads and generate duplex quality metrics.
- Single-strand molecular consensus calling
- Consolidating reads containing single or multiple degenerate or non-random UMIs into consensus reads using fgbio.
- Parameter testing in exploratory research
- Operating in research and development mode (mode rd) to evaluate alternate consensus calling thresholds and filtering parameters.
- Production-scale high-throughput processing
- Deploying the pipeline in high-throughput mode (mode ht) to maximise execution speed and throughput across large sample cohorts.
Run it here
The research agent runs nf-core/fastquorum 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
- Standardises the fgbio Best Practices FASTQ to Consensus Pipeline in Nextflow DSL2.
- Supports both standard single-strand UMI and double-stranded Duplex Sequencing data architectures.
- Provides two dedicated operational modes tailored either for exploratory parameter adjustment or production efficiency.
- Integrates complete quality control reporting across UMI grouping, alignment, and consensus filtering via MultiQC.
Watch out for
- Requires explicit definition of read structures in the samplesheet to map logical read and UMI layouts.
- Kits and assays from vendors such as Agilent, Illumina, IDT, NEB, TwinStrand, and Twist are unverified until public test data are integrated.
Questions
Can I run nf-core/fastquorum without my own cluster?
The research agent runs nf-core/fastquorum 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/fastquorum under?
MIT, like every nf-core pipeline.
What inputs are required to run the pipeline?
A samplesheet CSV file is required, containing sample identifiers, paths to FASTQ files, and a read structure string describing how logical reads and molecular barcodes are arranged, alongside reference genome files.
How does the pipeline handle Duplex Sequencing data compared to standard UMI data?
For Duplex Sequencing, the pipeline runs fgbio CallDuplexConsensusReads and collects duplex-specific metrics with fgbio CollectDuplexSeqMetrics. Standard non-duplex datasets are processed via fgbio CallMolecularConsensusReads.
What is the difference between R&D mode and High Throughput mode?
Research and Development mode (--mode rd) permits branching to test varying consensus calling and filtering parameters. High Throughput mode (--mode ht) disables branching to maximise throughput in production environments.
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.
Written from nf-core/fastquorum's own README. Statements citing a figure that does not appear there are removed before publication. Licence and running details come from our registry.