Cargando…
Modern Computational Techniques for the HMMER Sequence Analysis
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 detaile...
Autores principales: | , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi Publishing Corporation
2013
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4393056/ https://www.ncbi.nlm.nih.gov/pubmed/25937944 http://dx.doi.org/10.1155/2013/252183 |
_version_ | 1782366091161370624 |
---|---|
author | Meng, Xiandong Ji, Yanqing |
author_facet | Meng, Xiandong Ji, Yanqing |
author_sort | Meng, Xiandong |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-4393056 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2013 |
publisher | Hindawi Publishing Corporation |
record_format | MEDLINE/PubMed |
spelling | pubmed-43930562015-05-03 Modern Computational Techniques for the HMMER Sequence Analysis Meng, Xiandong Ji, Yanqing ISRN Bioinform Review Article 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. Hindawi Publishing Corporation 2013-09-03 /pmc/articles/PMC4393056/ /pubmed/25937944 http://dx.doi.org/10.1155/2013/252183 Text en Copyright © 2013 X. Meng and Y. Ji. https://creativecommons.org/licenses/by/3.0/ 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. |
spellingShingle | Review Article Meng, Xiandong Ji, Yanqing Modern Computational Techniques for the HMMER Sequence Analysis |
title | Modern Computational Techniques for the HMMER Sequence Analysis |
title_full | Modern Computational Techniques for the HMMER Sequence Analysis |
title_fullStr | Modern Computational Techniques for the HMMER Sequence Analysis |
title_full_unstemmed | Modern Computational Techniques for the HMMER Sequence Analysis |
title_short | Modern Computational Techniques for the HMMER Sequence Analysis |
title_sort | modern computational techniques for the hmmer sequence analysis |
topic | Review Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4393056/ https://www.ncbi.nlm.nih.gov/pubmed/25937944 http://dx.doi.org/10.1155/2013/252183 |
work_keys_str_mv | AT mengxiandong moderncomputationaltechniquesforthehmmersequenceanalysis AT jiyanqing moderncomputationaltechniquesforthehmmersequenceanalysis |