作者
Peter Ebert, Peter A Audano, Qihui Zhu, Bernardo Rodriguez-Martin, David Porubsky, Marc Jan Bonder, Arvis Sulovari, Jana Ebler, Weichen Zhou, Rebecca Serra Mari, Feyza Yilmaz, Xuefang Zhao, PingHsun Hsieh, Joyce Lee, Sushant Kumar, Jiadong Lin, Tobias Rausch, Yu Chen, Jingwen Ren, Martin Santamarina, Wolfram Höps, Hufsah Ashraf, Nelson T Chuang, Xiaofei Yang, Katherine M Munson, Alexandra P Lewis, Susan Fairley, Luke J Tallon, Wayne E Clarke, Anna O Basile, Marta Byrska-Bishop, André Corvelo, Uday S Evani, Tsung-Yu Lu, Mark JP Chaisson, Junjie Chen, Chong Li, Harrison Brand, Aaron M Wenger, Maryam Ghareghani, William T Harvey, Benjamin Raeder, Patrick Hasenfeld, Allison A Regier, Haley J Abel, Ira M Hall, Paul Flicek, Oliver Stegle, Mark B Gerstein, Jose MC Tubio, Zepeng Mu, Yang I Li, Xinghua Shi, Alex R Hastie, Kai Ye, Zechen Chong, Ashley D Sanders, Michael C Zody, Michael E Talkowski, Ryan E Mills, Scott E Devine, Charles Lee, Jan O Korbel, Tobias Marschall, Evan E Eichler
发表日期
2021/4/2
期刊
Science
卷号
372
期号
6537
页码范围
eabf7117
出版商
American Association for the Advancement of Science
简介
INTRODUCTION
The characterization of the full spectrum of genetic variation is critical to understanding human health and disease. Recent technological advances have made it possible to survey genetic variants on the level of fully reconstructed haplotypes, leading to substantially improved sensitivity in detecting and characterizing large structural variants (SVs), including complex classes.
RATIONALE
We focused on comprehensive genetic variant discovery from a human diversity panel representing 25 human populations. We leveraged a recently developed computational pipeline that combines long-read technology and single-cell template strand sequencing (Strand-seq) to generate fully phased diploid genome assemblies without guidance of a reference genome or use of parent-child trio information. Variant discovery from high-quality haplotype assemblies increases sensitivity and yields variants that …
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P Ebert, PA Audano, Q Zhu, B Rodriguez-Martin… - Science, 2021