Machine learning enables alignment-free distance calculation and phylogenetic placement using k-mer frequencies

Machine learning enables alignment-free distance calculation and phylogenetic placement using k-mer frequencies | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL Molecular Ecology Resources This is a preprint and has not been peer reviewed. Data may be preliminary. 23 April 2025 V1 Latest version Share on Machine learning enables alignment-free distance calculation and phylogenetic placement using k-mer frequencies Authors : Eleonora Rachtman 0000-0002-6104-5750 [email protected] , Yueyu Jiang , and Siavash Mirarab Authors Info & Affiliations https://doi.org/10.22541/au.174539883.30231226/v1 Published Molecular Ecology Resources Version of record Peer review timeline 588 views 387 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract A key application of phylogenetics in ecological studies is identifying unknown sequences with respect to known ones. This goal can be formalized as assigning taxonomic labels or inserting sequences into a reference phylogenetic tree (phylogenetic placement). Much attention has been paid to the phylogenetic placement of short fragments used in amplicon sequencing or metagenomics. However, placing longer pieces of DNA, such as assembled genomes, contigs, or long reads, is less studied. Placing long sequences should be easier than short reads due to their increased signal. However, handling larger inputs poses its own challenges, including finding homologs and the computational burden. Here, we explore a phylogenetic placement method that uses k-mer frequencies to measure distances between long query sequences and reference genomes. Our proposed method, kf2vec, requires no alignment and can work on any region of the genome (needs no marker genes), thus simplifying analysis pipelines. A rich literature exists on using short k-mers frequencies to measure distances that correlate with phylogeny. Existing methods, however, have had moderate practical success despite enjoying strong theory. Instead of using predefined metrics, we train a deep neural network to estimate a distance from k-mer frequency vectors such that those distances match the path lengths on the reference phylogeny. The trained model is then used to characterize new samples. We demonstrate that kf2vec outperforms existing kmer-based approaches in distance calculation and allows accurate phylogenetic placement and taxonomic identification of new samples from various types of long sequences. Supplementary Material File (kmer_deeplearning_main.pdf) Download 1.46 MB Information & Authors Information Version history V1 Version 1 23 April 2025 Peer review timeline Published Molecular Ecology Resources Version of Record 12 Oct 2025 Published Copyright This work is licensed under a Non Exclusive No Reuse License. Collection Molecular Ecology Resources Keywords assembly-free and alignment-free distance calculation deep learning genomic distance k-mer-based distance calculation metagenomics phylogenetic placement Authors Affiliations Eleonora Rachtman 0000-0002-6104-5750 [email protected] UCSD View all articles by this author Yueyu Jiang UCSD View all articles by this author Siavash Mirarab UCSD View all articles by this author Metrics & Citations Metrics Article Usage 588 views 387 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Eleonora Rachtman, Yueyu Jiang, Siavash Mirarab. Machine learning enables alignment-free distance calculation and phylogenetic placement using k-mer frequencies. Authorea . 23 April 2025. DOI: https://doi.org/10.22541/au.174539883.30231226/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . Format Please select one from the list RIS (ProCite, Reference Manager) EndNote BibTex Medlars RefWorks Direct import Tips for downloading citations document.getElementById('citMgrHelpLink').addEventListener('click', function() { popupHelp(this.href); return false; }); $(".js__slcInclude").on("change", function(e){ if ($(this).val() == 'refworks') $('#direct').prop("checked", false); $('#direct').prop("disabled", ($(this).val() == 'refworks')); }); Cited by Atika Islam, Minhaj Morshed Chowdhury, Sukla Topoti Saha Prome, Rubyeat Islam, Nafisa Tabassum, Integrating Machine Learning and Scaling Strategies for Accurate K-mer Based Alignment-Free Phylogenetics, Proceedings of the 2025 9th International Conference on Computational Biology and Bioinformatics, (37-42), (2026). https://doi.org/10.1145/3789938.3789947 Crossref Loading... View Options View options PDF View PDF Figures Tables Media Share Share Share article link Copy Link Copied! Copying failed. 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