Human MutationGenetic & Genomic MedicinePeer-reviewed journal article
seqr: a web-based analysis and collaboration tool for rare disease genomics
Lynn S. PaisiD, Hana Snow, Ben WeisburdiD, Shifa ZhangiD, Samantha M. BaxteriD, Stephanie P. DiTroiaiD, Emily O’HeiriD, Eleina M. England, Katherine R. ChaoiD, Gabrielle LemireiD, Ikeoluwa Osei‐OwusuiD, Grace E. VanNoyiD, Michael W. WilsoniD, Kevin K. NguyeniD, Harindra Arachchi, William PhuiD, Matthew SolomonsoniD, Stacy Mano, Melanie O’LearyiD, Alysia Kern LovgreniD, Lawrence J. BabbiD, Christina Anne Austin-TseiD, Heidi L. RehmiD, Daniel G. MacArthuriD, Anne O’Donnell‐LuriaiD
Abstract
Exome and genome sequencing have become the tools of choice for rare disease diagnosis, leading to large amounts of data available for analyses. To identify causal variants in these datasets, powerful filtering and decision support tools that can be efficiently used by clinicians and researchers are required. To address this need, we developed seqr - an open source, web-based tool for family-based monogenic disease analysis that allows researchers to work collaboratively to search and annotate genomic callsets. To date, seqr is being used in several research pipelines and one clinical diagnostic lab. In our own experience through the Broad Institute Center for Mendelian Genomics, seqr has enabled analyses of over 10,000 families, supporting the diagnosis of more than 3,800 individuals with rare disease and discovery of over 300 novel disease genes. Here we describe a framework for genomic analysis in rare disease that leverages seqrs capabilities for variant filtration, annotation, and causal variant identification, as well as support for research collaboration and data sharing. The seqr platform is available as open source software, allowing low-cost participation in rare disease research, and a community effort to support diagnosis and gene discovery in rare disease.
- organism: human
- assay: whole-exome sequencing
- assay: whole-genome sequencing
- research focus: variant calling
- research focus: variant effect prediction
- research focus: database / resource
- research focus: software / tool
- approach: statistical model
- approach: experimental assay
- Citations
- 106
- FWCI
- 13.26
- Field percentile
- top 1%
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