Danny Antaki1,2,3,4, William M Brandler1,2,3, Jonathan Sebat1,2,3. 1. Beyster Center for Genomics of Psychiatric Diseases. 2. Department of Psychiatry. 3. Department of Cellular and Molecular Medicine and Pediatrics. 4. Biomedical Sciences Graduate Program, University of California San Diego, La Jolla, CA 92093, USA.
Abstract
Motivation: Structural variation (SV) detection from short-read whole genome sequencing is error prone, presenting significant challenges for population or family-based studies of disease. Results: Here, we describe SV2, a machine-learning algorithm for genotyping deletions and duplications from paired-end sequencing data. SV2 can rapidly integrate variant calls from multiple structural variant discovery algorithms into a unified call set with high genotyping accuracy and capability to detect de novo mutations. Availability and implementation: SV2 is freely available on GitHub (https://github.com/dantaki/SV2). Contact: jsebat@ucsd.edu. Supplementary information: Supplementary data are available at Bioinformatics online.
Motivation: Structural variation (SV) detection from short-read whole genome sequencing is error prone, presenting significant challenges for population or family-based studies of disease. Results: Here, we describe SV2, a machine-learning algorithm for genotyping deletions and duplications from paired-end sequencing data. SV2 can rapidly integrate variant calls from multiple structural variant discovery algorithms into a unified call set with high genotyping accuracy and capability to detect de novo mutations. Availability and implementation: SV2 is freely available on GitHub (https://github.com/dantaki/SV2). Contact: jsebat@ucsd.edu. Supplementary information: Supplementary data are available at Bioinformatics online.
Authors: William M Brandler; Danny Antaki; Madhusudan Gujral; Amina Noor; Gabriel Rosanio; Timothy R Chapman; Daniel J Barrera; Guan Ning Lin; Dheeraj Malhotra; Amanda C Watts; Lawrence C Wong; Jasper A Estabillo; Therese E Gadomski; Oanh Hong; Karin V Fuentes Fajardo; Abhishek Bhandari; Renius Owen; Michael Baughn; Jeffrey Yuan; Terry Solomon; Alexandra G Moyzis; Michelle S Maile; Stephan J Sanders; Gail E Reiner; Keith K Vaux; Charles M Strom; Kang Zhang; Alysson R Muotri; Natacha Akshoomoff; Suzanne M Leal; Karen Pierce; Eric Courchesne; Lilia M Iakoucheva; Christina Corsello; Jonathan Sebat Journal: Am J Hum Genet Date: 2016-03-24 Impact factor: 11.025
Authors: Donald F Conrad; Dalila Pinto; Richard Redon; Lars Feuk; Omer Gokcumen; Yujun Zhang; Jan Aerts; T Daniel Andrews; Chris Barnes; Peter Campbell; Tomas Fitzgerald; Min Hu; Chun Hwa Ihm; Kati Kristiansson; Daniel G Macarthur; Jeffrey R Macdonald; Ifejinelo Onyiah; Andy Wing Chun Pang; Sam Robson; Kathy Stirrups; Armand Valsesia; Klaudia Walter; John Wei; Chris Tyler-Smith; Nigel P Carter; Charles Lee; Stephen W Scherer; Matthew E Hurles Journal: Nature Date: 2009-10-07 Impact factor: 49.962
Authors: Xiaoyu Chen; Ole Schulz-Trieglaff; Richard Shaw; Bret Barnes; Felix Schlesinger; Morten Källberg; Anthony J Cox; Semyon Kruglyak; Christopher T Saunders Journal: Bioinformatics Date: 2015-12-08 Impact factor: 6.937
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Authors: Colby Chiang; Ryan M Layer; Gregory G Faust; Michael R Lindberg; David B Rose; Erik P Garrison; Gabor T Marth; Aaron R Quinlan; Ira M Hall Journal: Nat Methods Date: 2015-08-10 Impact factor: 28.547
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Authors: Sai Chen; Peter Krusche; Egor Dolzhenko; Rachel M Sherman; Roman Petrovski; Felix Schlesinger; Melanie Kirsche; David R Bentley; Michael C Schatz; Fritz J Sedlazeck; Michael A Eberle Journal: Genome Biol Date: 2019-12-19 Impact factor: 13.583
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