Literature DB >> 31860070

A haplotype-aware de novo assembly of related individuals using pedigree sequence graph.

Shilpa Garg1,2, John Aach1, Heng Li3, Isaac Sebenius4, Richard Durbin5, George Church1,2.   

Abstract

MOTIVATION: Reconstructing high-quality haplotype-resolved assemblies for related individuals has important applications in Mendelian diseases and population genomics. Through major genomics sequencing efforts such as the Personal Genome Project, the Vertebrate Genome Project (VGP) and the Genome in a Bottle project (GIAB), a variety of sequencing datasets from trios of diploid genomes are becoming available. Current trio assembly approaches are not designed to incorporate long- and short-read data from mother-father-child trios, and therefore require relatively high coverages of costly long-read data to produce high-quality assemblies. Thus, building a trio-aware assembler capable of producing accurate and chromosomal-scale diploid genomes of all individuals in a pedigree, while being cost-effective in terms of sequencing costs, is a pressing need of the genomics community.
RESULTS: We present a novel pedigree sequence graph based approach to diploid assembly using accurate Illumina data and long-read Pacific Biosciences (PacBio) data from all related individuals, thereby generalizing our previous work on single individuals. We demonstrate the effectiveness of our pedigree approach on a simulated trio of pseudo-diploid yeast genomes with different heterozygosity rates, and real data from human chromosome. We show that we require as little as 30× coverage Illumina data and 15× PacBio data from each individual in a trio to generate chromosomal-scale phased assemblies. Additionally, we show that we can detect and phase variants from generated phased assemblies.
AVAILABILITY AND IMPLEMENTATION: https://github.com/shilpagarg/WHdenovo.
© The Author(s) 2019. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Entities:  

Mesh:

Year:  2020        PMID: 31860070     DOI: 10.1093/bioinformatics/btz942

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


  6 in total

1.  A complete pedigree-based graph workflow for rare candidate variant analysis.

Authors:  Charles Markello; Charles Huang; Alex Rodriguez; Andrew Carroll; Pi-Chuan Chang; Jordan Eizenga; Thomas Markello; David Haussler; Benedict Paten
Journal:  Genome Res       Date:  2022-04-28       Impact factor: 9.438

2.  E-Pedigrees: a large-scale automatic family pedigree prediction application.

Authors:  Xiayuan Huang; Nicholas Tatonetti; Katie LaRow; Brooke Delgoffee; John Mayer; David Page; Scott J Hebbring
Journal:  Bioinformatics       Date:  2021-06-04       Impact factor: 6.931

Review 3.  Computational methods for chromosome-scale haplotype reconstruction.

Authors:  Shilpa Garg
Journal:  Genome Biol       Date:  2021-04-12       Impact factor: 13.583

Review 4.  Advances and opportunities in malaria population genomics.

Authors:  Daniel E Neafsey; Aimee R Taylor; Bronwyn L MacInnis
Journal:  Nat Rev Genet       Date:  2021-04-08       Impact factor: 59.581

5.  Ratatosk: hybrid error correction of long reads enables accurate variant calling and assembly.

Authors:  Guillaume Holley; Doruk Beyter; Helga Ingimundardottir; Peter L Møller; Snædis Kristmundsdottir; Hannes P Eggertsson; Bjarni V Halldorsson
Journal:  Genome Biol       Date:  2021-01-08       Impact factor: 13.583

6.  gcaPDA: a haplotype-resolved diploid assembler.

Authors:  Min Xie; Linfeng Yang; Chenglin Jiang; Shenshen Wu; Cheng Luo; Xin Yang; Lijuan He; Shixuan Chen; Tianquan Deng; Mingzhi Ye; Jianbing Yan; Ning Yang
Journal:  BMC Bioinformatics       Date:  2022-02-14       Impact factor: 3.169

  6 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.