ArticleComputational and structural biotechnology journal2023
JLOH: Inferring loss of heterozygosity blocks from sequencing data.
Article in Computational and structural biotechnology journal, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 7 citations in OpenAlex.
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- Insights into the genomic and phenotypic diversity of Monosporozyma unispora strains isolated from anthropic environments.FEMS yeast research · 2025Article
- Insights into the origin, hybridisation and adaptation of Candida metapsilosis hybrid pathogens.PLoS pathogens · 2025Article
- Hybrids unleashed: exploring the emergence and genomic insights of pathogenic yeast hybrids.Current opinion in microbiology · 2024Review
- Origin of fungal hybrids with pathogenic potential from warm seawater environments.Nature communications · 2023Article
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Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Heterozygosity is a genetic condition in which two or more alleles are found at a genomic locus. Individuals that are the offspring of genetically divergent yet still interfertile parents (e.g. hybrids) are highly heterozygous. One of the most studied aspects in the genomes of these individuals is the loss of their original heterozygosity (LOH) when multi-allelic sites lose one of their two alleles by converting it to the other, or by remaining hemizygous at that site. The region undergoing LOH may involve a single nucleotide polymorphism (SNP) or a longer stretch of DNA. LOH is deeply interconnected with adaptation but the in silico techniques to infer evolutionary relevant LOH blocks are hardly standardised, and a general tool to infer and analyse them across genomic contexts and species is missing. Here, we present JLOH, a computational toolkit for the inference and exploration of LOH blocks in genomes with at least 1% heterozygosity. JLOH only requires commonly available genomic sequencing data as input. Starting from mapped reads, called variants and a reference genome sequence, JLOH infers candidate LOH blocks based on SNP density (SNPs/kbp) and read coverage per position. Considering that most organisms that undergo extensive LOH are hybrids, JLOH has been designed to capture any subgenomic LOH pattern, assigning each LOH block to its subgenome of origin.
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