nf-core pipeline · k-mer, kmer, kmer-counting
nf-core/kmermaid
A bioinformatics pipeline for comparative analysis of omic sequences using k-mer based similarity methods.
What nf-core/kmermaid is
nf-core/kmermaid is a bioinformatics pipeline designed to carry out comparative analyses of omic datasets through k-mer based similarity approaches. A k-mer refers to an identical sequence substring of length k extracted from genetic data. The pipeline was originally developed by Olga Botvinnik, with the Nextflow DSL2 conversion implemented by Igor Trujnara.
The workflow enables researchers to calculate distances between sequencing datasets, assemblies, and single-cell profiles without requiring exhaustive alignment. It accepts an array of biological inputs—such as raw sequencing reads, FASTA references, BAM alignments, and public SRA accessions—and pairs preprocessing utilities with k-mer sketching and distance reporting.
What the pipeline does
The workflow is structured into optional, input-dependent preprocessing stages followed by core k-mer estimation. For alignment data such as 10X Genomics runs, it can unpack BAM archives, extract FASTQ reads using samtools, split reads per individual cell, and tally unique molecular identifiers (UMIs) per cell with pbtk. The pipeline can also directly download SRA accessions.
For sequencing reads, optional preprocessing includes adapter and quality trimming using fastp, quality control via FastQC, ribosomal RNA removal using SortMeRNA, and translation into amino acid sequences with orpheum. In the core analysis phase, the pipeline generates sketches of the processed sequences, calculates distance metrics between samples, and compiles statistics alongside a MultiQC summary report.
Key facts
- Supported inputs
- CSV sample sheets, paired-end FASTQ reads, FASTA files, BAM files, SRA accessions
- Outputs
- Calculated distance metrics, statistics files, MultiQC report
- Read processing tools
- fastp, FastQC, SortMeRNA, orpheum
- BAM processing tools
- samtools, pbtk
- Original developer
- Olga Botvinnik
- DSL2 developer
- Igor Trujnara
- Latest release
- 0.1.0-alpha
What you can use it for
- Pairwise sequencing read comparison
- Sketch and compute distance metrics across paired-end FASTQ datasets to evaluate sample similarity.
- Single-cell BAM preprocessing and analysis
- Extract reads, demultiplex single cells, and count per-cell UMIs from 10X BAM files prior to sketch distance calculations.
- Protein-space k-mer similarity
- Translate nucleotide reads into protein sequences with orpheum to perform amino acid k-mer comparisons.
- Comparative analysis from public repositories
- Retrieve public datasets directly using SRA experiment identifiers for automated preprocessing and k-mer analysis.
- Reference FASTA comparison
- Directly compare FASTA sequence files using k-mer sketching and distance estimation.
Run it here
The research agent runs nf-core/kmermaid 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
- Accommodates diverse sequence formats, including FASTA, paired FASTQ, BAM alignments, and direct SRA downloads.
- Integrates quality control, read trimming, ribosomal RNA filtering, and translation into an automated execution path.
- Provides specific preprocessing utilities for single-cell 10X BAM data, including cell-level splitting and UMI counting.
- Aggregates pipeline execution statistics and quality summaries into an integrated MultiQC report.
Watch out for
- The software remains in an early alpha development stage (version 0.1.0-alpha).
- Many preprocessing steps are input-dependent and optional, requiring careful parameter configuration for specific data types.
Questions
Can I run nf-core/kmermaid without my own cluster?
The research agent runs nf-core/kmermaid 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/kmermaid under?
MIT, like every nf-core pipeline.
What input formats can be supplied to nf-core/kmermaid?
The pipeline accepts sample manifests in CSV format, paired FASTQ files, FASTA sequence files, BAM alignment files, and SRA accession identifiers.
Does the pipeline handle ribosomal RNA removal?
Yes, SortMeRNA can be executed during the read preprocessing stage to eliminate ribosomal RNA sequences.
Can k-mer analysis be performed on amino acid sequences?
Yes, the pipeline can translate nucleotide reads into protein sequences using orpheum before sketch generation and distance calculation.
What outputs does the pipeline produce?
nf-core/kmermaid outputs distance estimates, statistics files, and an aggregate MultiQC report.
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/kmermaid's own README. Statements citing a figure that does not appear there are removed before publication. Licence and running details come from our registry.