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Statistical Issues in the Analysis of ChIP-Seq and RNA-Seq Data

The recent arrival of ultra-high throughput, next generation sequencing (NGS) technologies has revolutionized the genetics and genomics fields by allowing rapid and inexpensive sequencing of billions of bases. The rapid deployment of NGS in a variety of sequencing-based experiments has resulted in f...

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Detalles Bibliográficos
Autores principales: Ghosh, Debashis, Qin, Zhaohui S.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2010
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3954086/
https://www.ncbi.nlm.nih.gov/pubmed/24710049
http://dx.doi.org/10.3390/genes1020317
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author Ghosh, Debashis
Qin, Zhaohui S.
author_facet Ghosh, Debashis
Qin, Zhaohui S.
author_sort Ghosh, Debashis
collection PubMed
description The recent arrival of ultra-high throughput, next generation sequencing (NGS) technologies has revolutionized the genetics and genomics fields by allowing rapid and inexpensive sequencing of billions of bases. The rapid deployment of NGS in a variety of sequencing-based experiments has resulted in fast accumulation of massive amounts of sequencing data. To process this new type of data, a torrent of increasingly sophisticated algorithms and software tools are emerging to help the analysis stage of the NGS applications. In this article, we strive to comprehensively identify the critical challenges that arise from all stages of NGS data analysis and provide an objective overview of what has been achieved in existing works. At the same time, we highlight selected areas that need much further research to improve our current capabilities to delineate the most information possible from NGS data. The article focuses on applications dealing with ChIP-Seq and RNA-Seq.
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spelling pubmed-39540862014-03-26 Statistical Issues in the Analysis of ChIP-Seq and RNA-Seq Data Ghosh, Debashis Qin, Zhaohui S. Genes (Basel) Review The recent arrival of ultra-high throughput, next generation sequencing (NGS) technologies has revolutionized the genetics and genomics fields by allowing rapid and inexpensive sequencing of billions of bases. The rapid deployment of NGS in a variety of sequencing-based experiments has resulted in fast accumulation of massive amounts of sequencing data. To process this new type of data, a torrent of increasingly sophisticated algorithms and software tools are emerging to help the analysis stage of the NGS applications. In this article, we strive to comprehensively identify the critical challenges that arise from all stages of NGS data analysis and provide an objective overview of what has been achieved in existing works. At the same time, we highlight selected areas that need much further research to improve our current capabilities to delineate the most information possible from NGS data. The article focuses on applications dealing with ChIP-Seq and RNA-Seq. MDPI 2010-09-27 /pmc/articles/PMC3954086/ /pubmed/24710049 http://dx.doi.org/10.3390/genes1020317 Text en © 2010 by the authors; licensee MDPI, Basel, Switzerland http://creativecommons.org/licenses/by/3.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Review
Ghosh, Debashis
Qin, Zhaohui S.
Statistical Issues in the Analysis of ChIP-Seq and RNA-Seq Data
title Statistical Issues in the Analysis of ChIP-Seq and RNA-Seq Data
title_full Statistical Issues in the Analysis of ChIP-Seq and RNA-Seq Data
title_fullStr Statistical Issues in the Analysis of ChIP-Seq and RNA-Seq Data
title_full_unstemmed Statistical Issues in the Analysis of ChIP-Seq and RNA-Seq Data
title_short Statistical Issues in the Analysis of ChIP-Seq and RNA-Seq Data
title_sort statistical issues in the analysis of chip-seq and rna-seq data
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3954086/
https://www.ncbi.nlm.nih.gov/pubmed/24710049
http://dx.doi.org/10.3390/genes1020317
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