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npInv: accurate detection and genotyping of inversions using long read sub-alignment

BACKGROUND: Detection of genomic inversions remains challenging. Many existing methods primarily target inzversions with a non repetitive breakpoint, leaving inverted repeat (IR) mediated non-allelic homologous recombination (NAHR) inversions largely unexplored. RESULT: We present npInv, a novel too...

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Autores principales: Shao, Haojing, Ganesamoorthy, Devika, Duarte, Tania, Cao, Minh Duc, Hoggart, Clive J., Coin, Lachlan J. M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6044046/
https://www.ncbi.nlm.nih.gov/pubmed/30001702
http://dx.doi.org/10.1186/s12859-018-2252-9
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author Shao, Haojing
Ganesamoorthy, Devika
Duarte, Tania
Cao, Minh Duc
Hoggart, Clive J.
Coin, Lachlan J. M.
author_facet Shao, Haojing
Ganesamoorthy, Devika
Duarte, Tania
Cao, Minh Duc
Hoggart, Clive J.
Coin, Lachlan J. M.
author_sort Shao, Haojing
collection PubMed
description BACKGROUND: Detection of genomic inversions remains challenging. Many existing methods primarily target inzversions with a non repetitive breakpoint, leaving inverted repeat (IR) mediated non-allelic homologous recombination (NAHR) inversions largely unexplored. RESULT: We present npInv, a novel tool specifically for detecting and genotyping NAHR inversion using long read sub-alignment of long read sequencing data. We benchmark npInv with other tools in both simulation and real data. We use npInv to generate a whole-genome inversion map for NA12878 consisting of 30 NAHR inversions (of which 15 are novel), including all previously known NAHR mediated inversions in NA12878 with flanking IR less than 7kb. Our genotyping accuracy on this dataset was 94%. We used PCR to confirm the presence of two of these novel inversions. We show that there is a near linear relationship between the length of flanking IR and the minimum inversion size, without inverted repeats. CONCLUSION: The application of npInv shows high accuracy in both simulation and real data. The results give deeper insight into understanding inversion. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12859-018-2252-9) contains supplementary material, which is available to authorized users.
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spelling pubmed-60440462018-07-13 npInv: accurate detection and genotyping of inversions using long read sub-alignment Shao, Haojing Ganesamoorthy, Devika Duarte, Tania Cao, Minh Duc Hoggart, Clive J. Coin, Lachlan J. M. BMC Bioinformatics Methodology Article BACKGROUND: Detection of genomic inversions remains challenging. Many existing methods primarily target inzversions with a non repetitive breakpoint, leaving inverted repeat (IR) mediated non-allelic homologous recombination (NAHR) inversions largely unexplored. RESULT: We present npInv, a novel tool specifically for detecting and genotyping NAHR inversion using long read sub-alignment of long read sequencing data. We benchmark npInv with other tools in both simulation and real data. We use npInv to generate a whole-genome inversion map for NA12878 consisting of 30 NAHR inversions (of which 15 are novel), including all previously known NAHR mediated inversions in NA12878 with flanking IR less than 7kb. Our genotyping accuracy on this dataset was 94%. We used PCR to confirm the presence of two of these novel inversions. We show that there is a near linear relationship between the length of flanking IR and the minimum inversion size, without inverted repeats. CONCLUSION: The application of npInv shows high accuracy in both simulation and real data. The results give deeper insight into understanding inversion. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12859-018-2252-9) contains supplementary material, which is available to authorized users. BioMed Central 2018-07-13 /pmc/articles/PMC6044046/ /pubmed/30001702 http://dx.doi.org/10.1186/s12859-018-2252-9 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 Methodology Article
Shao, Haojing
Ganesamoorthy, Devika
Duarte, Tania
Cao, Minh Duc
Hoggart, Clive J.
Coin, Lachlan J. M.
npInv: accurate detection and genotyping of inversions using long read sub-alignment
title npInv: accurate detection and genotyping of inversions using long read sub-alignment
title_full npInv: accurate detection and genotyping of inversions using long read sub-alignment
title_fullStr npInv: accurate detection and genotyping of inversions using long read sub-alignment
title_full_unstemmed npInv: accurate detection and genotyping of inversions using long read sub-alignment
title_short npInv: accurate detection and genotyping of inversions using long read sub-alignment
title_sort npinv: accurate detection and genotyping of inversions using long read sub-alignment
topic Methodology Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6044046/
https://www.ncbi.nlm.nih.gov/pubmed/30001702
http://dx.doi.org/10.1186/s12859-018-2252-9
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