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Modern Computational Techniques for the HMMER Sequence Analysis
Author(s) -
Xiandong Meng,
Yanqing Ji
Publication year - 2013
Publication title -
isrn bioinformatics
Language(s) - English
Resource type - Journals
eISSN - 2090-7346
pISSN - 2090-7338
DOI - 10.1155/2013/252183
Subject(s) - computer science , sequence (biology) , hidden markov model , software , markov chain , acceleration , data science , software engineering , artificial intelligence , machine learning , programming language , genetics , physics , classical mechanics , biology
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.

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