Skip to content
No description, website, or topics provided.
C C++ Makefile Other
Branch: master
Clone or download
Latest commit eb7ab75 Feb 7, 2020
Type Name Latest commit message Commit time
Failed to load latest commit information.
scripts Code refactoring Feb 7, 2020
src Code refactoring Feb 7, 2020
.gitignore Code refactoring Feb 7, 2020
Makefile Code refactoring Feb 7, 2020 Update Feb 7, 2020


Winnowmap is a long-read mapping algorithm, and a result of our exploration into superior minimizer sampling techniques. Minimizer sampling was originally introduced by Roberts et al. This technique yields reduced representation of reference genome, enabling fast mapping analyses. A key property of the minimizer technique is that if two sequences share a substring of a specified length, then they can be guaranteed to have a matching minimizer. However, because the k-mer distribution in eukaryotic genomes is highly uneven, minimizer-based long read mappers (e.g., minimap2, mashmap) opt to discard the most frequently occurring minimizers from the genome in order to avoid excessive false positive seed hits. By doing so, the underlying guarantee is lost and accuracy is reduced in repetitive genomic regions (e.g., long tandem repeats).

To address the above problem, Winnowmap implements a novel weighted minimizer sampling algorithm. A unique feature of Winnowmap is that it performs minimizer sampling while taking into account a weight for each k-mer; i.e, the higher the weight of a k-mer, the more likely it is to be selected. Rather than masking k-mers, Winnowmap down-weights frequently occurring k-mers, thus reducing their chance of getting selected as minimizers. Winnowmap implements the new minimizer sampling and indexing algorithm, and borrows minimap2’s highly efficient anchor chaining and gapped alignment routines. The user-interface of Winnowmap is maintained similar to minimap2.


Winnowmap requires c++11 to build, which is available in GCC >= 4.8. To compile Winnowmap, run make. Expect two executables computeHighFreqKmers and winnowmap.


  • Step 1: compute a set of highly frequent k-mers:

    computeHighFreqKmers 19 1 1024 ref.fa bad_Hk19_mers.txt (OR)
    computeHighFreqKmers 15 0 1024 ref.fa bad_k15_mers.txt

    The above executable computeHighFreqKmers expects five arguments in the following order: k-mer length, binary number 0/1 indicating whether to enable homopolymer compression, minimum k-mer frequency, reference genome and output file.

  • Step 2: Map long reads to reference:

    winnowmap -W bad_Hk19_mers.txt -cx map-pb ref.fa pacbio.fq.gz > output.paf (OR)
    winnowmap -W bad_k15_mers.txt -cx map-ont ref.fa ont.fq.gz > output.paf

    Except the -W parameter above needed by Winnowmap, the remaining options are consistent with minimap2 usage.

    Users should keep k-mer length and homopolymer compression parameters consistent in Steps 1 and 2. For example, map-pb preset in Step 2 uses k-mer length 19 with homopolymer compression enabled. The other popular preset map-ont uses k-mer length 15 without homopolymer compression. In near future, these two steps will be merged into one for user convenience.


Comparing Winnowmap to minimap2, we observed a reduction in the mapping error-rate from 0.14% to 0.06% in the recently finished human X chromosome, and from 3.6% to 0% within the highly repetitive X centromere (3.1 Mbp). Winnowmap improves mapping accuracy within repeats and achieves these results with sparser sampling, leading to better index compression and competitive runtimes. By avoiding masking, we show that Winnowmap maintains uniform minimizer density.

Minimizer sampling density using a human X chromosome as the reference, with the centromere positioned between 58 Mbp and 61 Mbp. ‘Standard’ method refers to the classic minimizer sampling algorithm from Roberts et al., without any masking or modification.

You can’t perform that action at this time.