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

Ghanasyam Rallapalli; Pilar Corredor-Moreno; Edward Chalstrey; Martin Page; Daniel Maclean

doi:10.1186/s12859-018-2515-5

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

Ghanasyam Rallapalli, Pilar Corredor-Moreno, Edward Chalstrey, Martin Page, Daniel Maclean

Research output: Contribution to journal › Article › peer-review

2 Citations (Scopus)

30 Downloads (Pure)

Abstract

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.

Original language	English
Article number	9
Journal	BMC Bioinformatics
Volume	20
DOIs	https://doi.org/10.1186/s12859-018-2515-5
Publication status	Published - 7 Jan 2019

Keywords

Bulk segregant analysis
Mapping by sequencing
Next generation mapping

Access to Document

10.1186/s12859-018-2515-5Licence: CC BY

Published_ManuscriptFinal published version, 1.17 MBLicence: CC BY

Cite this

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abstract = "BackgroundTraditional 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.ResultsWe 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.ConclusionsCHERIPIC 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.",

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T1 - Rapid fine mapping of causative mutations from sets of unordered, contig-sized fragments of genome sequence

AU - Rallapalli, Ghanasyam

AU - Corredor-Moreno, Pilar

AU - Chalstrey, Edward

AU - Page, Martin

AU - Maclean, Daniel

PY - 2019/1/7

Y1 - 2019/1/7

N2 - BackgroundTraditional 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.ResultsWe 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.ConclusionsCHERIPIC 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.

AB - BackgroundTraditional 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.ResultsWe 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.ConclusionsCHERIPIC 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.

KW - Bulk segregant analysis

KW - Mapping by sequencing

KW - Next generation mapping

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

Abstract

Keywords

Access to Document

Other files and links

Daniel Maclean

A tool for identifying causative mutations from sequencing data without a reference genome