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      A web application and service for imputing and visualizing missense variant effect maps

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          Abstract

          Summary

          The promise of personalized genomic medicine depends on our ability to assess the functional impact of rare sequence variation. Multiplexed assays can experimentally measure the functional impact of missense variants on a massive scale. However, even after such assays, many missense variants remain poorly measured. Here we describe a software pipeline and application to impute missing information in experimentally determined variant effect maps.

          Availability and implementation

          http://impute.varianteffect.org source code: https://github.com/joewuca/imputation.

          Supplementary information

          Supplementary data are available at Bioinformatics online.

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          Most cited references9

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          Amino acid substitution matrices from protein blocks.

          S Henikoff, J. Henikoff (1992)
          Methods for alignment of protein sequences typically measure similarity by using a substitution matrix with scores for all possible exchanges of one amino acid with another. The most widely used matrices are based on the Dayhoff model of evolutionary rates. Using a different approach, we have derived substitution matrices from about 2000 blocks of aligned sequence segments characterizing more than 500 groups of related proteins. This led to marked improvements in alignments and in searches using queries from each of the groups.
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            Deep mutational scanning: a new style of protein science.

            Douglas Fowler, Stanley Fields (2014)
            Mutagenesis provides insight into proteins, but only recently have assays that couple genotype to phenotype been used to assess the activities of as many as 1 million mutant versions of a protein in a single experiment. This approach-'deep mutational scanning'-yields large-scale data sets that can reveal intrinsic protein properties, protein behavior within cells and the consequences of human genetic variation. Deep mutational scanning is transforming the study of proteins, but many challenges must be tackled for it to fulfill its promise.
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              Massively Parallel Functional Analysis of BRCA1 RING Domain Variants.

              Lea M. Starita, David Young, Muhtadi Islam … (2015)
              Interpreting variants of uncertain significance (VUS) is a central challenge in medical genetics. One approach is to experimentally measure the functional consequences of VUS, but to date this approach has been post hoc and low throughput. Here we use massively parallel assays to measure the effects of nearly 2000 missense substitutions in the RING domain of BRCA1 on its E3 ubiquitin ligase activity and its binding to the BARD1 RING domain. From the resulting scores, we generate a model to predict the capacities of full-length BRCA1 variants to support homology-directed DNA repair, the essential role of BRCA1 in tumor suppression, and show that it outperforms widely used biological-effect prediction algorithms. We envision that massively parallel functional assays may facilitate the prospective interpretation of variants observed in clinical sequencing.
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                Author and article information

                Contributors
                Russell Schwartz: Role: Associate Editor
                Journal
                Journal ID (nlm-ta): Bioinformatics
                Journal ID (iso-abbrev): Bioinformatics
                Journal ID (publisher-id): bioinformatics
                Title: Bioinformatics
                Publisher: Oxford University Press
                ISSN (Print): 1367-4803
                ISSN (Electronic): 1367-4811
                Publication date (Print): 01 September 2019
                Publication date (Electronic): 14 January 2019
                Publication date PMC-release: 14 January 2019
                Volume: 35
                Issue: 17
                Pages: 3191-3193
                Affiliations
                [1 ]Donnelly Centre, University of Toronto , Toronto, ON, Canada
                [2 ]Department of Molecular Genetics, University of Toronto , Toronto, ON, Canada
                [3 ]Department of Computer Science, University of Toronto , Toronto, ON, Canada
                [4 ] Lunenfeld-Tanenbaum Research Institute, Sinai Health System , Toronto, ON, Canada
                [5 ] Center for Cancer Systems Biology, Dana Farber Cancer Institute , Boston, MA, USA
                [6 ] Canadian Institute for Advanced Research , Toronto, ON, Canada
                Author notes
                To whom correspondence should be addressed. E-mail: fritz.roth@ 123456utoronto.ca
                Article
                Publisher ID: btz012
                DOI: 10.1093/bioinformatics/btz012
                PMC ID: 6735881
                PubMed ID: 30649215
                SO-VID: 969ba2dc-61cd-4fad-a41f-c33a240b611a
                Copyright © © The Author(s) 2019. Published by Oxford University Press.
                License:

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                Date received : 31 July 2018
                Date revision received : 4 December 2018
                Date accepted : 7 January 2019
                Page count
                Pages: 3
                Funding
                Funded by: National Human Genome Research Institute 10.13039/100000051
                Funded by: National Institutes of Health Center of Excellence in Genomic Science
                Award ID: HG004233
                Funded by: Canada Excellence Research Chairs
                Funded by: Canadian Institutes of Health Foundation
                Funded by: One Brave Idea Foundation
                Categories
                Subject: Applications Notes
                Subject: Genetics and Population Analysis

                ScienceOpen disciplines: Bioinformatics & Computational biology
                Data availability:
                ScienceOpen disciplines: Bioinformatics & Computational biology

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