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krepp is a k-mer-based maximum likelihood tool for estimating distances of reads to genomes and phylogenetic placement.

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Overview

krepp comprises two main stages: indexing and querying. You can index a set of reference genomes using the krepp index subcommand, and then use an index to perform queries: distance calculation from reads to genomes (krepp dist) or phylogenetic/taxonomic placement (krepp place).

You can start by installing the tool or compiling from the source as described in Installation. A toy dataset is available in here. You can make yourself familiar with the commands and test your installation by following this little tutorial.

If you would like to use a prebuilt microbial index, see Quickstart for a brief overview of commands. We offer a wide range of indices for various datasets, which are listed in this catalogue. You can pick one that fits your needs and install it. If you would like to index from scratch using your own dataset or phylogeny, refer to Indexing reference genomes.

Detailed descriptions of options for the distance estimation (krepp dist) and phylogenetic placement (krepp place) are available in Estimating distances and Placing on a tree. krepp also offers a simpler functionality to quickly analyze query sequences against a single reference using krepp sketch and krepp seek, described here.

We plan to post tutorials for specific tasks and various use cases available in Tutorials page.

Citation

See the manuscript in Genome Biology. You can use the following BibTeX entry to cite krepp:

@article{sapci_krepp_2026,
	title = {krepp: a k-mer-based maximum pseudo-likelihood method for estimating read distances and genome-wide phylogenetic placement},
	volume = {27},
	issn = {1474-760X},
	shorttitle = {krepp},
	url = {https://doi.org/10.1186/s13059-026-03999-y},
	doi = {10.1186/s13059-026-03999-y},
	number = {1},
	urldate = {2026-03-28},
	journal = {Genome Biology},
	author = {Şapcı, Ali Osman Berk and Mirarab, Siavash},
	month = feb,
	year = {2026},
	keywords = {Average nucleotide identity, k-mer-based sequence comparison, Metagenomics, Phylogenetic placement},
	pages = {108},
}