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LRSim: A Linked-Reads Simulator Generating Insights for Better Genome Partitioning.
Luo, Ruibang; Sedlazeck, Fritz J; Darby, Charlotte A; Kelly, Stephen M; Schatz, Michael C.
Afiliação
  • Luo R; Department of Computer Science, Johns Hopkins University, United States.
  • Sedlazeck FJ; Center for Computational Biology, McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, United States.
  • Darby CA; Department of Computer Science, Johns Hopkins University, United States.
  • Kelly SM; Department of Computer Science, Johns Hopkins University, United States.
  • Schatz MC; Center for Health Informatics and Bioinformatics, New York University School of Medicine, United States.
Comput Struct Biotechnol J ; 15: 478-484, 2017.
Article em En | MEDLINE | ID: mdl-29213995
ABSTRACT
Linked-read sequencing, using highly-multiplexed genome partitioning and barcoding, can span hundreds of kilobases to improve de novo assembly, haplotype phasing, and other applications. Based on our analysis of 14 datasets, we introduce LRSim that simulates linked-reads by emulating the library preparation and sequencing process with fine control over variants, linked-read characteristics, and the short-read profile. We conclude from the phasing and assembly of multiple datasets, recommendations on coverage, fragment length, and partitioning when sequencing genomes of different sizes and complexities. These optimizations improve results by orders of magnitude, and enable the development of novel methods. LRSim is available at https//github.com/aquaskyline/LRSIM.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: Comput Struct Biotechnol J Ano de publicação: 2017 Tipo de documento: Article País de afiliação: Estados Unidos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: Comput Struct Biotechnol J Ano de publicação: 2017 Tipo de documento: Article País de afiliação: Estados Unidos