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Benchmark of computational methods to detect digenism in sequencing data.

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Bibliographic Details
Title: Benchmark of computational methods to detect digenism in sequencing data.
Authors: Ogloblinsky MC; Univ Brest, Inserm, EFS, UMR 1078, GGB, Brest, France. marie-sophie.ogloblinsky@inserm.fr., Conrad DF; Division of Genetics, Oregon National Primate Research Center, Oregon Health & Science University, Portland, OR, USA., Baudot A; Aix Marseille Univ, INSERM, Marseille Medical Genetics (MMG), Marseille, France., Tournier-Lasserve E; Université Paris Cité, Inserm, NeuroDiderot, Unité Mixte de Recherche 1141, F-75019, Paris, France.; Assistance publique-Hôpitaux de Paris, Service de Génétique Moléculaire Neurovasculaire, Hôpital Saint-Louis, F-75010, Paris, France., Génin E; Univ Brest, Inserm, EFS, UMR 1078, GGB, Brest, France.; Assistance publique-Hôpitaux de Paris, Service de Génétique Moléculaire Neurovasculaire, Hôpital Saint-Louis, F-75010, Paris, France., Marenne G; Univ Brest, Inserm, EFS, UMR 1078, GGB, Brest, France. gaelle.marenne@inserm.fr.
Corporate Authors: FrEx Consortium
Source: European journal of human genetics : EJHG [Eur J Hum Genet] 2025 Jul; Vol. 33 (7), pp. 831-841. Date of Electronic Publication: 2025 Apr 09.
Publication Type: Journal Article; Review
Journal Info: Publisher: Nature Publishing Group Country of Publication: England NLM ID: 9302235 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1476-5438 (Electronic) Linking ISSN: 10184813 NLM ISO Abbreviation: Eur J Hum Genet Subsets: MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:1476-5438
DOI:10.1038/s41431-025-01834-9