Table of Contents
ISRN Bioinformatics
Volume 2013, Article ID 252183, 13 pages
http://dx.doi.org/10.1155/2013/252183
Review Article

Modern Computational Techniques for the HMMER Sequence Analysis

1Genomics Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
2Department of Energy, Joint Genome Institute, Walnut Creek, CA 94598, USA
3Department of Electrical and Computer Engineering, Gonzaga University, Spokane, WA 99258, USA

Received 17 June 2013; Accepted 30 July 2013

Academic Editors: A. Pulvirenti and K. Yura

Copyright © 2013 Xiandong Meng and Yanqing Ji. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

This paper focuses on the latest research and critical reviews on modern computing architectures, software and hardware accelerated algorithms for bioinformatics data analysis with an emphasis on one of the most important sequence analysis applications—hidden Markov models (HMM). We show the detailed performance comparison of sequence analysis tools on various computing platforms recently developed in the bioinformatics society. The characteristics of the sequence analysis, such as data and compute-intensive natures, make it very attractive to optimize and parallelize by using both traditional software approach and innovated hardware acceleration technologies.