nf-core pipeline ·
nf-core/readsimulator
Nextflow pipeline for simulating amplicon, target capture, metagenome, and whole-genome sequencing reads.
What nf-core/readsimulator is
nf-core/readsimulator is a bioinformatics pipeline designed to generate synthetic sequencing reads across multiple experimental paradigms. It supports simulated datasets for amplicon sequencing, target capture, metagenomes, and whole-genome sequencing.
The pipeline was created by Adam Bennett for the Minderoo Foundation's OceanOmics project, with development support from Carson J Miller, Lauren Huet, and Philipp Bayer. It allows computational researchers and biologists to generate reproducible in silico datasets for testing, method benchmarking, and protocol evaluation.
What the pipeline does
The pipeline takes an input samplesheet listing sample names and numerical seeds used for pseudo-random read generation. Depending on the simulation mode requested, the workflow routes execution through dedicated external tools. For amplicon data, reference databases are constructed with CRABS and simulated with art_illumina. Target capture workflows align probes to a genome with Bowtie2, index the alignment with SAMtools, and simulate Illumina or PacBio reads using Japsa capsim.
Metagenomic datasets are generated using InsilicoSeq Generate, whereas whole-genome reads are simulated using wgsim. Upon producing the simulated FASTQ files, the pipeline constructs a formatted samplesheet linking each sample identifier to its generated FASTQ paths. Read quality evaluation is performed using FastQC, and metrics are compiled into an aggregate MultiQC report.
Key facts
- Pipeline framework
- Nextflow
- Latest release
- 1.0.1 (2024-04-26)
- Supported simulation types
- Amplicon, target capture, metagenome, whole genome
- Input format
- CSV samplesheet (sample, seed)
- Output formats
- FASTQ, CSV samplesheet, HTML reports
- Target capture engines
- Bowtie2, SAMtools, Japsa capsim
- Metagenome engine
- InsilicoSeq Generate
- Amplicon engines
- CRABS, art_illumina
- Whole-genome engine
- wgsim
- Quality control tools
- FastQC, MultiQC
What you can use it for
- Metagenomic pipeline benchmarking
- Simulate paired Illumina metagenomic sequencing reads using InsilicoSeq Generate to assess taxonomic profiling and assembly pipelines.
- Target capture probe evaluation
- Model Illumina or PacBio target capture experiments by aligning probes to reference genomes with Bowtie2 and simulating captured reads with Japsa capsim.
- Amplicon assay simulation
- Create reference databases via CRABS and simulate Illumina amplicon reads using art_illumina to test amplicon bioinformatic pipelines.
- Whole-genome test dataset generation
- Synthesise whole-genome paired reads via wgsim using reproducible random seeds to benchmark variant detection algorithms.
Run it here
The research agent runs nf-core/readsimulator 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
- Supports four distinct sequencing experimental designs: amplicon, target capture, metagenomic, and whole-genome sequencing.
- Automates the creation of a standardised output samplesheet linking sample identifiers to generated FASTQ files.
- Provides random seed controls per sample to facilitate reproducible read generation.
- Offers both Illumina and PacBio read simulation capabilities for target capture assays via Japsa capsim.
- Includes built-in post-simulation quality control reporting using FastQC and MultiQC across all simulation modes.
Watch out for
- Amplicon read simulation is limited to the Illumina platform via art_illumina.
- Metagenome read simulation is restricted to Illumina profiles via InsilicoSeq Generate.
- Pipeline parameters cannot be supplied through custom configuration files using the Nextflow -c flag and must be passed via the command-line interface or parameter files.
Questions
Can I run nf-core/readsimulator without my own cluster?
The research agent runs nf-core/readsimulator 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/readsimulator under?
MIT, like every nf-core pipeline.
What inputs does nf-core/readsimulator require?
It requires a CSV samplesheet specifying sample identifiers and numerical seeds for random generation, alongside mode-specific execution arguments.
Which sequencing technologies can be simulated?
The pipeline simulates Illumina reads for amplicon, metagenome, whole-genome, and target capture data, as well as PacBio reads for target capture assays.
What files are output by the workflow?
The workflow generates simulated FASTQ files, an output CSV samplesheet with paths to the FASTQ files, FastQC metrics, and an integrated MultiQC report.
How are simulation seeds managed?
Seeds are defined on a per-sample basis directly in the input CSV samplesheet under the seed column.
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. Pipeline DOI: 10.5281/zenodo.10622410.
Written from nf-core/readsimulator's own README. Statements citing a figure that does not appear there are removed before publication. Licence and running details come from our registry.