ParaMask: a new method to identify multicopy genomic regions, corrects major biases in whole-genome sequencing data

Multicopy genomic regions are repeated sequences that can bias genomic analyses. Here, we present a method, ParaMask, to identify and filter multicopy regions in population-level genomic data of any species. The broad applicability of this method stems from a flexible Expectation-Maximization framework to detect excess heterozygosity while simultaneously fitting inbreeding levels. By combining this signature with read-ratio deviations, excess sequencing depth, and a clustering technique, our method attains high recall. We show that multicopy regions create biases that confound evolutionary genomic analyses and that by identifying these regions with our method and filtering them, we can correct these biases.

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Publication Details

Journal
Genome biology
Published
2025-10-24
DOI
https://doi.org/10.1186/s13059-025-03836-8
Primary Topic
Genomics and Phylogenetic Studies
Type
article
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article

ParaMask: a new method to identify multicopy genomic regions, corrects major biases in whole-genome sequencing data

Andrea Fulgione, Male Arimond, Helene Bråten Grindeland, Bastiaan Tjeng et al.
Genome biology
Genomics and Phylogenetic Studies
article

ParaMask: a new method to identify multicopy genomic regions, corrects major biases in whole-genome sequencing data

Andrea Fulgione, Male Arimond, Helene Bråten Grindeland, Bastiaan Tjeng, Andrea Dalla Libera
article en

Abstract

Multicopy genomic regions are repeated sequences that can bias genomic analyses. Here, we present a method, ParaMask, to identify and filter multicopy regions in population-level genomic data of any species. The broad applicability of this method stems from a flexible Expectation-Maximization framework to detect excess heterozygosity while simultaneously fitting inbreeding levels. By combining this signature with read-ratio deviations, excess sequencing depth, and a clustering technique, our method attains high recall. We show that multicopy regions create biases that confound evolutionary genomic analyses and that by identifying these regions with our method and filtering them, we can correct these biases.

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