Evidence map›Paper›PMID 42177542›Full record

ArticleGenome biology2026

ReSkmer: modeling repeats allows k-mer-based alignment-free methods to calculate population genomic distances.

Eduardo Charvel, Isaac Thomas, Homère J Alves Monteiro, Shahab Sarmashghi, Glenn Dunshea, Vineet Bafna, Siavash Mirarab

Abstract read
In one paragraph

Article in Genome biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. DipSkmer: Reference-free population genomics with diploid genome skims.bioRxiv : the preprint server for biology · 2026
    Article
4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

7 authors.

Eduardo Charvel *Bioinformatics & Systems Biology Graduate Program, University of California, San Diego, La Jolla, USA.
Isaac Thomas *Department of Computer Science & Engineering, University of California, San Diego, La Jolla, USA.
Homère J Alves MonteiroCenter for Evolutionary Hologenomics, GLOBE Institute, University of Copenhagen, Copenhagen, Denmark.
Shahab SarmashghiBroad Institute of MIT and Harvard, Boston, Massachusetts, USA.
Glenn DunsheaDepartment of Natural History, NTNU University Museum, Norwegian University of Science and Technology, Trondheim, 7491, Norway.
Vineet BafnaDepartment of Computer Science & Engineering, University of California, San Diego, La Jolla, USA.
Siavash MirarabDepartment of Electrical & Computer Engineering, University of California, San Diego, La Jolla, USA. smirarab@ucsd.edu.

Funding

Computational methods for detecting patterns of complex genomic variationR01GM114362 · NIGMS · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI Vineet Bafna · 2016 to 2026
$3.1M
European Union under the Horizon Europe Programme 101082004NIGMS NIH HHS R01 GM114362NIH HHS 468 R01GM114362
6 · The paper itself

Abstract

Understanding ecological change requires robust, inexpensive methods for monitoring populations. One approach is using low-coverage genome sequencing (genome skimming) to compute genetic diversity via assembly-free, alignment-free, k-mer-based methods to compute genomic distances from shotgun sequences using the intersection of k-mer sets. Skmer extended this approach to genome skims by modeling coverage and error. However, these methods ignore genome repetitiveness, hampering population genetic distance calculation. Here, we mathematically derive the expected intersection size between k-mers sampled from two repetitive genomes, accounting for repeats, coverage, and errors, leading to the method ReSkmer. Our experiments show highly accurate distances despite sampling highly repetitive genomes.

Indexed as

Genetics, PopulationGenomicsModels, GeneticRepetitive Sequences, Nucleic AcidAlgorithmsGenetic Variation

Identifiers

PMID42177542
PMCPMC13386924

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.