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ParMap, an algorithm for the identification of small genomic insertions and deletions in nextgen sequencing data

BACKGROUND: Next-generation sequencing produces high-throughput data, albeit with greater error and shorter reads than traditional Sanger sequencing methods. This complicates the detection of genomic variations, especially, small insertions and deletions. FINDINGS: Here we describe ParMap, a statist...

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Autores principales: Khiabanian, Hossein, Van Vlierberghe, Pieter, Palomero, Teresa, Ferrando, Adolfo A, Rabadan, Raul
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2010
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2897800/
https://www.ncbi.nlm.nih.gov/pubmed/20507604
http://dx.doi.org/10.1186/1756-0500-3-147
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author Khiabanian, Hossein
Van Vlierberghe, Pieter
Palomero, Teresa
Ferrando, Adolfo A
Rabadan, Raul
author_facet Khiabanian, Hossein
Van Vlierberghe, Pieter
Palomero, Teresa
Ferrando, Adolfo A
Rabadan, Raul
author_sort Khiabanian, Hossein
collection PubMed
description BACKGROUND: Next-generation sequencing produces high-throughput data, albeit with greater error and shorter reads than traditional Sanger sequencing methods. This complicates the detection of genomic variations, especially, small insertions and deletions. FINDINGS: Here we describe ParMap, a statistical algorithm for the identification of complex genetic variants, such as small insertion and deletions, using partially mapped reads in nextgen sequencing data. CONCLUSIONS: We report ParMap's successful application to the mutation analysis of chromosome X exome-captured leukemia DNA samples.
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spelling pubmed-28978002010-07-07 ParMap, an algorithm for the identification of small genomic insertions and deletions in nextgen sequencing data Khiabanian, Hossein Van Vlierberghe, Pieter Palomero, Teresa Ferrando, Adolfo A Rabadan, Raul BMC Res Notes Technical Note BACKGROUND: Next-generation sequencing produces high-throughput data, albeit with greater error and shorter reads than traditional Sanger sequencing methods. This complicates the detection of genomic variations, especially, small insertions and deletions. FINDINGS: Here we describe ParMap, a statistical algorithm for the identification of complex genetic variants, such as small insertion and deletions, using partially mapped reads in nextgen sequencing data. CONCLUSIONS: We report ParMap's successful application to the mutation analysis of chromosome X exome-captured leukemia DNA samples. BioMed Central 2010-05-27 /pmc/articles/PMC2897800/ /pubmed/20507604 http://dx.doi.org/10.1186/1756-0500-3-147 Text en Copyright ©2010 Khiabanian et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Technical Note
Khiabanian, Hossein
Van Vlierberghe, Pieter
Palomero, Teresa
Ferrando, Adolfo A
Rabadan, Raul
ParMap, an algorithm for the identification of small genomic insertions and deletions in nextgen sequencing data
title ParMap, an algorithm for the identification of small genomic insertions and deletions in nextgen sequencing data
title_full ParMap, an algorithm for the identification of small genomic insertions and deletions in nextgen sequencing data
title_fullStr ParMap, an algorithm for the identification of small genomic insertions and deletions in nextgen sequencing data
title_full_unstemmed ParMap, an algorithm for the identification of small genomic insertions and deletions in nextgen sequencing data
title_short ParMap, an algorithm for the identification of small genomic insertions and deletions in nextgen sequencing data
title_sort parmap, an algorithm for the identification of small genomic insertions and deletions in nextgen sequencing data
topic Technical Note
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2897800/
https://www.ncbi.nlm.nih.gov/pubmed/20507604
http://dx.doi.org/10.1186/1756-0500-3-147
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