8 releases

0.2.4 Apr 6, 2023
0.2.3 Feb 20, 2023
0.1.2 Feb 10, 2023

#55 in Biology

Download history 82/week @ 2023-02-09 83/week @ 2023-02-16 17/week @ 2023-02-23 1/week @ 2023-03-02 2/week @ 2023-03-16 9/week @ 2023-03-23 4/week @ 2023-03-30 34/week @ 2023-04-06 13/week @ 2023-04-13 16/week @ 2023-04-20 1/week @ 2023-04-27 17/week @ 2023-05-04 12/week @ 2023-05-11 11/week @ 2023-05-18 15/week @ 2023-05-25

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2.5K SLoC

Split K-mer Analysis (version 2)

Cargo Build & Test docs.rs Clippy check codecov Crates.io GitHub release (latest SemVer)


Choose from:

  1. Download a binary from the releases.
  2. Use cargo install ska or cargo add ska.
  3. Use conda install -c bioconda ska2 (note the two!).
  4. Build from source

For 2) or 4) you must have the rust toolchain installed.

OS X users

If you have an M1/M2 (arm64) Mac, we aren't currently automatically building binaries, so would recommend either option 2) or 4) for best performance.

If you get a message saying the binary isn't signed by Apple and can't be run, use the following command to bypass this:

xattr -d "com.apple.quarantine" ./ska

Build from source

  1. Clone the repository with git clone.
  2. Run cargo install --path . or RUSTFLAGS="-C target-cpu=native" cargo install --path . to optimise for your machine.


Can be found at https://docs.rs/ska.


This is a reimplementation of Simon Harris' SKA package in the rust language, by Johanna von Wachsmann, Simon Harris and John Lees.

SKA (Split Kmer Analysis) is a toolkit for prokaryotic (and any other small, haploid) DNA sequence analysis using split kmers. A split kmer is a pair of kmers in a DNA sequence that are separated by a single base. Split kmers allow rapid comparison and alignment of small genomes, and is particulalry suited for surveillance or outbreak investigation. SKA can produce split kmer files from fasta format assemblies or directly from fastq format read sequences, cluster them, align them with or without a reference sequence and provide various comparison and summary statistics. Currently all testing has been carried out on high-quality Illumina read data, so results for other platforms may vary.

Optimisations include:

  • Integer DNA encoding, optimised parsing from FASTA/FASTQ.
  • Faster dictionaries.
  • Full parallelisation of build phase.
  • Smaller, standardised input/output files.
  • Reduced memory footprint with read filtering.

And other improvements:

  • IUPAC uncertainty codes for multiple copy k-mers.
  • Fully dynamic files (merge, delete samples).
  • Native VCF output for map.
  • Support for known strand sequence (e.g. RNA viruses).
  • Stream to STDOUT, or file with -o.
  • Simpler command line combining ska fasta, ska fastq, ska alleles and ska merge into the new ska build.
  • Option for single commands to run ska align or ska map.
  • Logging.
  • CI testing.

All of which make ska.rust run faster and with smaller file size and memory footprint than the original.

Planned features

  • Add support for amiguity in VCF output

Things you can no longer do

  • Use k > 63 (shouldn't be necessary? Let us know if you need this and why).
  • ska annotate (use bedtools).
  • ska compare, ska humanise, ska info or ska summary (use ska nk --full-info).
  • ska distance and ska unique (use pp-sketchlib).
  • ska type (use PopPUNK instead of MLST 🙂)
  • Ns are always skipped, and will not be found in any split k-mers.
  • .skf files are not backwards compatible with version 1.


~338K SLoC