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Bioinformatics for Next Generation Sequencing Data
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Genes 2010, 1(2), 317-334;

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

Department of Statistics and Public Health Sciences, Penn State University, 514A Wartik Building, University Park, PA 16802, USA
Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Center for Comprehensive Informatics, Emory University, 1518 Clifton Rd., N.E., Atlanta, GA 30322, USA
Author to whom correspondence should be addressed.
Received: 17 August 2010 / Accepted: 20 September 2010 / Published: 27 September 2010
(This article belongs to the Special Issue Next Generation DNA Sequencing)
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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. View Full-Text
Keywords: next generation sequencing; statistical analysis next generation sequencing; statistical analysis

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This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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Ghosh, D.; Qin, Z.S. Statistical Issues in the Analysis of ChIP-Seq and RNA-Seq Data. Genes 2010, 1, 317-334.

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