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Rapid fine mapping of causative mutations from sets of unordered, contig-sized fragments of genome sequence

BACKGROUND: Traditional Map based Cloning approaches, used for the identification of desirable alleles, are extremely labour intensive and years can elapse between the mutagenesis and the detection of the polymorphism. High throughput sequencing based Mapping-by-sequencing approach requires an order...

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Autores principales: Rallapalli, Ghanasyam, Corredor-Moreno, Pilar, Chalstrey, Edward, Page, Martin, MacLean, Daniel
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6323790/
https://www.ncbi.nlm.nih.gov/pubmed/30616525
http://dx.doi.org/10.1186/s12859-018-2515-5
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author Rallapalli, Ghanasyam
Corredor-Moreno, Pilar
Chalstrey, Edward
Page, Martin
MacLean, Daniel
author_facet Rallapalli, Ghanasyam
Corredor-Moreno, Pilar
Chalstrey, Edward
Page, Martin
MacLean, Daniel
author_sort Rallapalli, Ghanasyam
collection PubMed
description BACKGROUND: Traditional Map based Cloning approaches, used for the identification of desirable alleles, are extremely labour intensive and years can elapse between the mutagenesis and the detection of the polymorphism. High throughput sequencing based Mapping-by-sequencing approach requires an ordered genome assembly and cannot be used with fragmented, un-scaffolded draft genomes, limiting its application to model species and precluding many important organisms. RESULTS: We addressed this gap in resource and presented a computational method and software implementations called CHERIPIC (Computing Homozygosity Enriched Regions In genomes to Prioritise Identification of Candidate variants). We have successfully validated implementation of CHERIPIC using three different types of bulk segregant sequence data from Arabidopsis, maize and barley, respectively. CONCLUSIONS: CHERIPIC allows users to rapidly analyse bulk segregant sequence data and we have made it available as a pre-packaged binary with all dependencies for Linux and MacOS and as Galaxy tool. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12859-018-2515-5) contains supplementary material, which is available to authorized users.
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spelling pubmed-63237902019-01-11 Rapid fine mapping of causative mutations from sets of unordered, contig-sized fragments of genome sequence Rallapalli, Ghanasyam Corredor-Moreno, Pilar Chalstrey, Edward Page, Martin MacLean, Daniel BMC Bioinformatics Software BACKGROUND: Traditional Map based Cloning approaches, used for the identification of desirable alleles, are extremely labour intensive and years can elapse between the mutagenesis and the detection of the polymorphism. High throughput sequencing based Mapping-by-sequencing approach requires an ordered genome assembly and cannot be used with fragmented, un-scaffolded draft genomes, limiting its application to model species and precluding many important organisms. RESULTS: We addressed this gap in resource and presented a computational method and software implementations called CHERIPIC (Computing Homozygosity Enriched Regions In genomes to Prioritise Identification of Candidate variants). We have successfully validated implementation of CHERIPIC using three different types of bulk segregant sequence data from Arabidopsis, maize and barley, respectively. CONCLUSIONS: CHERIPIC allows users to rapidly analyse bulk segregant sequence data and we have made it available as a pre-packaged binary with all dependencies for Linux and MacOS and as Galaxy tool. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12859-018-2515-5) contains supplementary material, which is available to authorized users. BioMed Central 2019-01-07 /pmc/articles/PMC6323790/ /pubmed/30616525 http://dx.doi.org/10.1186/s12859-018-2515-5 Text en © The Author(s) 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Software
Rallapalli, Ghanasyam
Corredor-Moreno, Pilar
Chalstrey, Edward
Page, Martin
MacLean, Daniel
Rapid fine mapping of causative mutations from sets of unordered, contig-sized fragments of genome sequence
title Rapid fine mapping of causative mutations from sets of unordered, contig-sized fragments of genome sequence
title_full Rapid fine mapping of causative mutations from sets of unordered, contig-sized fragments of genome sequence
title_fullStr Rapid fine mapping of causative mutations from sets of unordered, contig-sized fragments of genome sequence
title_full_unstemmed Rapid fine mapping of causative mutations from sets of unordered, contig-sized fragments of genome sequence
title_short Rapid fine mapping of causative mutations from sets of unordered, contig-sized fragments of genome sequence
title_sort rapid fine mapping of causative mutations from sets of unordered, contig-sized fragments of genome sequence
topic Software
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6323790/
https://www.ncbi.nlm.nih.gov/pubmed/30616525
http://dx.doi.org/10.1186/s12859-018-2515-5
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