Modern Computational Techniques for the HMMER Sequence Analysis. (3rd September 2013)

• Record Type: Journal Article
• Authors: Meng, Xiandong; Ji, Yanqing
• Other Names: Pulvirenti A. Academic Editor.; Yura K. Academic Editor.
• Is Part Of: ISRN bioinformatics. Volume 2013(2013)
• Publication Date: 2013-09-03
• Subjects: Bioinformatics -- Periodicals; Computational biology -- Periodicals; Medical informatics -- Periodicals; Computational Biology; Medical Informatics; Bioinformatics; Computational biology; Medical informatics; Periodicals; Periodicals; 570.285
• DOI: 10.1155/2013/252183
• Languages: English
• ISSNs: 2090-7338

Abstract

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.