ArticleCell reports methods2025
Joint processing of long- and short-read sequencing data with deep learning improves variant calling.
Article in Cell reports methods, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Transcriptional architecture of African swine fever virus.PLoS pathogens · 2026Article
- Benchmarking of sequencing technologies defines optimal strategies for genetic variants detection in a human genome.Genome biology · 2026Article
- Blended Length Genome Sequencing (blend-seq): Combining Short Reads with Low-Coverage Long Reads to Maximize Variant Discovery.bioRxiv : the preprint server for biology · 2025Article
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Authors and funding
1 author.
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Abstract
Despite the complementary strengths of short- and long-read sequencing approaches, variant-calling methods still rely on a single data type. In this study, we collected and harmonized Nanopore datasets of the seven healthy individuals in the GIAB project across three independent consortia. By leveraging these harmonized Nanopore data, we explore the benefits of using a hybrid DeepVariant model to jointly process Illumina and Nanopore data for germline variant detection. We show that a shallow hybrid long-short sequencing approach can match or surpass the germline variant detection accuracy of state-of-the-art single-technology methods, potentially reducing overall sequencing costs and enabling the detection of large germline structural variations. These findings hold great promise for molecular diagnostics in clinical settings, particularly for rare genetic disease screenings.
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