ArticleNAR genomics and bioinformatics2025
Sequence-based chromatin activity modeling and regulatory impact prediction of genetic variants in farmed animals using deep learning.
Article in NAR genomics and bioinformatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- Non-Coding Negative Regulatory Features in Livestock Genomes: Functional Annotation and Causal Validation.Animals : an open access journal from MDPI · 2026Review
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5 authors.
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Abstract
Noncoding genomic variations are crucial for the genetic regulation of traits; however, their functional impact in farmed animals remains underexplored due to limited genomic resources and the absence of tailored computational tools. Here, we present a deep learning-based framework that utilizes functional genomic data to generate genome-wide predictions of the regulatory impact of noncoding variants in cattle, chicken, pig, and Atlantic salmon. By leveraging chromatin profiles such as assay for transposase-accessible chromatin, DNase I hypersensitive site, and chromatin immunoprecipitation sequencing data, we train and optimize separate deep networks for each species, achieving robust sequence modeling accuracy specific to each. Motif analysis confirms that the models capture regulatory grammar, while
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