<i>seqr</i> : A web‐based analysis and collaboration tool for rare disease genomics

Lynn Pais(Broad Institute), Hana Snow(Broad Institute), Ben Weisburd(Broad Institute), Shifa Zhang(Broad Institute), Samantha Baxter(Broad Institute), Stephanie DiTroia(Broad Institute), Emily O’Heir(Broad Institute), Eleina England(Broad Institute), Katherine R. Chao(Broad Institute), Gabrielle Lemire(Broad Institute), Ikeoluwa Osei‐Owusu(Broad Institute), Grace E. VanNoy(Broad Institute), Michael W. Wilson(Broad Institute), Kevin Nguyen(Broad Institute), Harindra Arachchi(Broad Institute), William Phu(Broad Institute), Matthew Solomonson(Broad Institute), Stacy Mano(Broad Institute), Melanie O’Leary(Broad Institute), Alysia Kern Lovgren(Broad Institute), Lawrence Babb(Broad Institute), Christina Austin‐Tse(Broad Institute), Heidi L. Rehm(Broad Institute), Daniel G. MacArthur(Broad Institute), Anne O’Donnell‐Luria(Broad Institute)
Human Mutation
March 10, 2022
Cited by 109Open Access
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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 seqr's 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.


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