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LRSim: A Linked-Reads Simulator Generating Insights for Better Genome Partitioning

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 pre...

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Detalles Bibliográficos
Autores principales: Luo, Ruibang, Sedlazeck, Fritz J., Darby, Charlotte A., Kelly, Stephen M., Schatz, Michael C.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Research Network of Computational and Structural Biotechnology 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5711661/
https://www.ncbi.nlm.nih.gov/pubmed/29213995
http://dx.doi.org/10.1016/j.csbj.2017.10.002
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author Luo, Ruibang
Sedlazeck, Fritz J.
Darby, Charlotte A.
Kelly, Stephen M.
Schatz, Michael C.
author_facet Luo, Ruibang
Sedlazeck, Fritz J.
Darby, Charlotte A.
Kelly, Stephen M.
Schatz, Michael C.
author_sort Luo, Ruibang
collection PubMed
description 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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spelling pubmed-57116612017-12-06 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. Comput Struct Biotechnol J Research Article 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. Research Network of Computational and Structural Biotechnology 2017-11-09 /pmc/articles/PMC5711661/ /pubmed/29213995 http://dx.doi.org/10.1016/j.csbj.2017.10.002 Text en © 2017 The Authors http://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Research Article
Luo, Ruibang
Sedlazeck, Fritz J.
Darby, Charlotte A.
Kelly, Stephen M.
Schatz, Michael C.
LRSim: A Linked-Reads Simulator Generating Insights for Better Genome Partitioning
title LRSim: A Linked-Reads Simulator Generating Insights for Better Genome Partitioning
title_full LRSim: A Linked-Reads Simulator Generating Insights for Better Genome Partitioning
title_fullStr LRSim: A Linked-Reads Simulator Generating Insights for Better Genome Partitioning
title_full_unstemmed LRSim: A Linked-Reads Simulator Generating Insights for Better Genome Partitioning
title_short LRSim: A Linked-Reads Simulator Generating Insights for Better Genome Partitioning
title_sort lrsim: a linked-reads simulator generating insights for better genome partitioning
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5711661/
https://www.ncbi.nlm.nih.gov/pubmed/29213995
http://dx.doi.org/10.1016/j.csbj.2017.10.002
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