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ISSN: 0974-276X

Journal of Proteomics & Bioinformatics
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Research Article

Statistical Analyses of Next Generation Sequence Data: A Partial overview

Susmita Datta1*, Somnath Datta1, Seongho Kim1, Sutirtha Chakraborty1 and Ryan S. Gill2

1Department of Bioinformatics and Biostatistics, University of Louisville, Louisville, KY 40202, USA

2Department of Mathematics, University of Louisville, Louisville, KY 40202, USA

*Corresponding Author:
Dr. Susmita Datta
Department of Bioinformatics and Biostatistics
University of Louisville, Louisville, KY 40202, USA
Tel: 1-5028520081
E-mail: [email protected]

Received Date: April 23, 2010; Accepted Date: June 03, 2010; Published Date: June 03, 2010

Citation: Datta S, Datta S, Kim S, Chakraborty S, Ryan SJ (2010) Statistical Analyses of Next Generation Sequence Data: A Partial Overview. J Proteomics Bioinform 3: 183-190. doi: 10.4172/jpb.1000138

Copyright: © 2010 Datta S, et al. 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 author and source are credited.

Abstract

Next generation sequencing has revolutionized the status of biological research. For a long time, the gold standard of DNA sequencing was considered to be the Sanger method. However, in 2005, commercial launching of next generation sequencing has made it possible to generate massively parallel and high resolution DNA sequence data. Its usefulness in various genomic applications such as genome-wide detection of SNPs, DNA methylation profi ling, mRNA expression profi ling, whole-genome re-sequencing and so on are now well recognized. There are several platforms for generating next generation sequencing (NGS) data which we briefl y discuss in this mini overview. With new technologies come new challenges for the data analysts. This mini review attempts to present a collection of selected topics in the current development of statistical methods dealing with these novel data types. We believe that knowing the advances and bottlenecks of this technology will help the researchers to benchmark the analytical tools dealing with these data and will pave the path for its proper application into clinical diagnostics.

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