DeepTrio: Variant Calling in Families Using Deep Learning

DeepTrio: Variant Calling in Families Using Deep Learning | bioRxiv Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results DeepTrio: Variant Calling in Families Using Deep Learning Lucas Brambrink , Alexey Kolesnikov , Sidharth Goel , Maria Nattestad , View ORCID Profile Taedong Yun , Gunjan Baid , Howard Yang , View ORCID Profile Cory Y McLean , View ORCID Profile Pi-Chuan Chang , View ORCID Profile Kishwar Shafin , View ORCID Profile Andrew Carroll doi: https://doi.org/10.1101/2021.04.05.438434 Lucas Brambrink 1 Google Inc, 1600 Amphitheatre Pkwy , Mountain View, California, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Alexey Kolesnikov 1 Google Inc, 1600 Amphitheatre Pkwy , Mountain View, California, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Sidharth Goel 1 Google Inc, 1600 Amphitheatre Pkwy , Mountain View, California, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Maria Nattestad 1 Google Inc, 1600 Amphitheatre Pkwy , Mountain View, California, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Taedong Yun 1 Google Inc, 1600 Amphitheatre Pkwy , Mountain View, California, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Taedong Yun Gunjan Baid 1 Google Inc, 1600 Amphitheatre Pkwy , Mountain View, California, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Howard Yang 1 Google Inc, 1600 Amphitheatre Pkwy , Mountain View, California, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Cory Y McLean 1 Google Inc, 1600 Amphitheatre Pkwy , Mountain View, California, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Cory Y McLean Pi-Chuan Chang 1 Google Inc, 1600 Amphitheatre Pkwy , Mountain View, California, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Pi-Chuan Chang Kishwar Shafin 1 Google Inc, 1600 Amphitheatre Pkwy , Mountain View, California, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Kishwar Shafin Andrew Carroll 1 Google Inc, 1600 Amphitheatre Pkwy , Mountain View, California, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Andrew Carroll For correspondence: awcarroll{at}google.com Abstract Full Text Info/History Metrics Preview PDF Abstract Every human inherits one copy of the genome from their mother and another from their father. Parental inheritance helps us understand the transmission of traits and genetic diseases, which often involve de novo variants and rare recessive alleles. Here we present DeepTrio, which learns to analyze child-mother-father trios from the joint sequence information, without explicit encoding of inheritance priors. DeepTrio learns how to weigh sequencing error, mapping error, and de novo rates and genome context directly from the sequence data. DeepTrio has higher accuracy on both Illumina and PacBio HiFi data when compared to DeepVariant. Improvements are especially pronounced at lower coverages (with 20x DeepTrio roughly equivalent to 30x DeepVariant). DeepTrio includes pre-trained models for Illumina WGS, Illumina exome, and PacBio HiFi. Competing Interest Statement All authors are employees of Google LLC and own Alphabet stock as part of the standard compensation package. This study was funded by Google LLC. Footnotes Improved methodology for de novo variant calling (added to GitHub code), updated evaluations to new versions of DeepTrio and other methods, included additional sequencing technologies. Updated author list of contributors. Copyright The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license . Back to top Previous Next Posted April 02, 2026. Download PDF Email Thank you for your interest in spreading the word about bioRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. 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Share DeepTrio: Variant Calling in Families Using Deep Learning Lucas Brambrink , Alexey Kolesnikov , Sidharth Goel , Maria Nattestad , Taedong Yun , Gunjan Baid , Howard Yang , Cory Y McLean , Pi-Chuan Chang , Kishwar Shafin , Andrew Carroll bioRxiv 2021.04.05.438434; doi: https://doi.org/10.1101/2021.04.05.438434 Share This Article: Copy Citation Tools DeepTrio: Variant Calling in Families Using Deep Learning Lucas Brambrink , Alexey Kolesnikov , Sidharth Goel , Maria Nattestad , Taedong Yun , Gunjan Baid , Howard Yang , Cory Y McLean , Pi-Chuan Chang , Kishwar Shafin , Andrew Carroll bioRxiv 2021.04.05.438434; doi: https://doi.org/10.1101/2021.04.05.438434 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Bioinformatics Subject Areas All Articles Animal Behavior and Cognition (7984) Biochemistry (18689) Bioengineering (14808) Bioinformatics (44286) Biophysics (22532) Cancer Biology (19654) Cell Biology (26817) Clinical Trials (138) Developmental Biology (13916) Ecology (20946) Epidemiology (2067) Evolutionary Biology (25400) Genetics (16132) Genomics (23443) Immunology (18643) Microbiology (42363) Molecular Biology (18003) Neuroscience (93167) Paleontology (696) Pathology (2974) Pharmacology and Toxicology (5076) Physiology (8088) Plant Biology (15953) Scientific Communication and Education (2094) Synthetic Biology (4546) Systems Biology (10205) Zoology (2381)

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