nf-core pipeline · mnase-seq, nucleosome, nucleosome-maps
nf-core/mnaseseq
Pipeline for processing micrococcal nuclease sequencing data and mapping genome-wide nucleosome positions.
What nf-core/mnaseseq is
nf-core/mnaseseq is a bioinformatics analysis pipeline designed for high-throughput DNA sequencing data generated via micrococcal nuclease (MNase) digestion. MNase digestion digests unprotected DNA while leaving nucleosome-bound DNA intact, allowing researchers to map nucleosome positioning and examine chromatin organisation at genomic scale.
The pipeline was initially created by Harshil Patel and the Bioinformatics and Biostatistics Group at The Francis Crick Institute in London. Built within the Nextflow framework, it enables reproducible execution across high-performance computing clusters and cloud environments through container technologies including Docker and Singularity.
What the pipeline does
The analysis begins with raw sequencing read quality inspection using FastQC, followed by adapter and quality trimming using Trim Galore!. Cleaned reads are mapped to a reference genome with BWA. Picard is employed to mark duplicate reads, and alignments from multiple sequencing libraries belonging to the same biological sample are merged. A comprehensive filtering procedure removes unmapped reads, duplicate reads, non-primary alignments, multimapping reads, reads overlapping blacklisted genomic regions, reads with more than four mismatches, and soft-clipped reads using SAMtools, BEDTools, and BAMTools. For paired-end datasets, Pysam and BAMTools enforce proper FR pair orientation, insert size boundaries, and concordant chromosome mapping.
Following alignment filtering, library complexity and alignment metrics are measured using Preseq and Picard, while deepTools performs genome-wide fingerprint coverage checks. Normalised bigWig coverage files scaled to one million mapped reads are generated using BEDTools and bedGraphToBigWig. DANPOS2 is then used to call nucleosome positions and compute smoothed, normalised coverage tracks, from which deepTools plots gene-body meta-profiles. The pipeline repeats alignment merging, deduplication, bigWig track creation, and nucleosome calling across biological replicates, compiles an Integrative Genomics Viewer (IGV) session file, and aggregates all QC statistics into a final MultiQC report.
Key facts
- Pipeline framework
- Nextflow
- Initial developer
- Harshil Patel (The Francis Crick Institute)
- Latest release
- 1.0.0 (2022-05-25)
- Read aligner
- BWA
- Nucleosome caller
- DANPOS2
- Execution environments
- Docker, Singularity, Conda
- Reporting tool
- MultiQC
- Primary inputs
- Sample design CSV file, raw FASTQ reads, reference genome
- Primary outputs
- Filtered BAM alignments, bigWig coverage files, DANPOS2 nucleosome calls, IGV session, MultiQC report
What you can use it for
- Genome-wide nucleosome positioning
- Identify precise nucleosome boundaries and occupancy peaks across the genome using DANPOS2.
- Gene-body chromatin profiling
- Generate aggregate occupancy meta-profile plots across gene bodies using smoothed bigWig tracks and deepTools.
- Multi-library and replicate analysis
- Merge technical sequencing libraries and biological replicates while removing optical and PCR duplicate reads.
Run it here
The research agent runs nf-core/mnaseseq 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
- Applies stringent quality filters to alignments, removing soft-clipped reads, non-primary mappings, and reads with high mismatch counts.
- Automatically outputs an IGV session file alongside normalised bigWig tracks for visual inspection.
- Carries out nucleosome calling and signal profiling at both individual sample and merged replicate levels.
Watch out for
- Paired-end filtering strictly requires reads in FR orientation and within a defined insert size, discarding pairs that fail these criteria.
- Requires reference genome annotations and blacklist files to execute the complete filtering workflow.
Questions
Can I run nf-core/mnaseseq without my own cluster?
The research agent runs nf-core/mnaseseq 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/mnaseseq under?
MIT, like every nf-core pipeline.
What inputs does the pipeline require?
It requires a sample sheet design file in CSV format, sequencing reads in FASTQ format, and a reference genome.
Which software is used for peak and nucleosome calling?
The pipeline uses DANPOS2 to identify nucleosome positions and create smoothed, normalised signal coverage tracks.
Does the pipeline handle paired-end sequencing reads?
Yes, both single-end and paired-end datasets are supported, with paired-end reads undergoing specific checks for insert size, FR orientation, and chromosome consistency.
How are duplicate reads treated?
Picard marks duplicates after initial alignment and library merging, and SAMtools removes duplicates during downstream filtering before nucleosome calling.
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/mnaseseq's own README. Statements citing a figure that does not appear there are removed before publication. Licence and running details come from our registry.