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Whole-genome CNV analysis: advances in computational approaches

Accumulating evidence indicates that DNA copy number variation (CNV) is likely to make a significant contribution to human diversity and also play an important role in disease susceptibility. Recent advances in genome sequencing technologies have enabled the characterization of a variety of genomic...

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Autores principales: Pirooznia, Mehdi, Goes, Fernando S., Zandi, Peter P.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4394692/
https://www.ncbi.nlm.nih.gov/pubmed/25918519
http://dx.doi.org/10.3389/fgene.2015.00138
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author Pirooznia, Mehdi
Goes, Fernando S.
Zandi, Peter P.
author_facet Pirooznia, Mehdi
Goes, Fernando S.
Zandi, Peter P.
author_sort Pirooznia, Mehdi
collection PubMed
description Accumulating evidence indicates that DNA copy number variation (CNV) is likely to make a significant contribution to human diversity and also play an important role in disease susceptibility. Recent advances in genome sequencing technologies have enabled the characterization of a variety of genomic features, including CNVs. This has led to the development of several bioinformatics approaches to detect CNVs from next-generation sequencing data. Here, we review recent advances in CNV detection from whole genome sequencing. We discuss the informatics approaches and current computational tools that have been developed as well as their strengths and limitations. This review will assist researchers and analysts in choosing the most suitable tools for CNV analysis as well as provide suggestions for new directions in future development.
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spelling pubmed-43946922015-04-27 Whole-genome CNV analysis: advances in computational approaches Pirooznia, Mehdi Goes, Fernando S. Zandi, Peter P. Front Genet Genetics Accumulating evidence indicates that DNA copy number variation (CNV) is likely to make a significant contribution to human diversity and also play an important role in disease susceptibility. Recent advances in genome sequencing technologies have enabled the characterization of a variety of genomic features, including CNVs. This has led to the development of several bioinformatics approaches to detect CNVs from next-generation sequencing data. Here, we review recent advances in CNV detection from whole genome sequencing. We discuss the informatics approaches and current computational tools that have been developed as well as their strengths and limitations. This review will assist researchers and analysts in choosing the most suitable tools for CNV analysis as well as provide suggestions for new directions in future development. Frontiers Media S.A. 2015-04-13 /pmc/articles/PMC4394692/ /pubmed/25918519 http://dx.doi.org/10.3389/fgene.2015.00138 Text en Copyright © 2015 Pirooznia, Goes and Zandi. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Genetics
Pirooznia, Mehdi
Goes, Fernando S.
Zandi, Peter P.
Whole-genome CNV analysis: advances in computational approaches
title Whole-genome CNV analysis: advances in computational approaches
title_full Whole-genome CNV analysis: advances in computational approaches
title_fullStr Whole-genome CNV analysis: advances in computational approaches
title_full_unstemmed Whole-genome CNV analysis: advances in computational approaches
title_short Whole-genome CNV analysis: advances in computational approaches
title_sort whole-genome cnv analysis: advances in computational approaches
topic Genetics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4394692/
https://www.ncbi.nlm.nih.gov/pubmed/25918519
http://dx.doi.org/10.3389/fgene.2015.00138
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