High Speed Biological Sequence Analysis With Hidden Markov Models on Reconfigurable Platforms

Publication type:
Journal article
Metadata:
Autoren
Autoren-URL
https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=fis-test-1&SrcAuth=WosAPI&KeyUT=WOS:000269518900008&DestLinkType=FullRecord&DestApp=WOS_CPL
DOI
10.1109/TITB.2007.904632
eISSN
1558-0032
Externe Identifier
  • Clarivate Analytics Document Solution ID: 490RD
  • PubMed Identifier: 19273034
ISSN
1089-7771
Ausgabe der Veröffentlichung
5
Zeitschrift
IEEE TRANSACTIONS ON INFORMATION TECHNOLOGY IN BIOMEDICINE
Schlüsselwörter
  • Biomedical engineering
  • computer architectures
  • database
  • dynamic programming (DP)
  • Markov models
  • parallel processing
  • search
  • sequences
Paginierung
740 - 746
Datum der Veröffentlichung
2009
Status
Published
Titel
High Speed Biological Sequence Analysis With Hidden Markov Models on Reconfigurable Platforms
Sub types
  • Article
Ausgabe der Zeitschrift
13

Data source: Web of Science (Lite)

Other metadata sources:
Autoren
DOI
10.1109/titb.2007.904632
ISSN
1089-7771
Ausgabe der Veröffentlichung
5
Zeitschrift
IEEE Transactions on Information Technology in Biomedicine
Paginierung
740 - 746
Datum der Veröffentlichung
2009
Status
Published
Herausgeber
Institute of Electrical and Electronics Engineers (IEEE)
Herausgeber URL
http://dx.doi.org/10.1109/titb.2007.904632
Datum der Datenerfassung
2021
Titel
High Speed Biological Sequence Analysis With Hidden Markov Models on Reconfigurable Platforms
Ausgabe der Zeitschrift
13

Data source: Crossref

Abstract
Molecular biologists use hidden Markov models (HMMs) as a popular tool to statistically describe biological sequence families. This statistical description can then be used for sensitive and selective database scanning, e.g., new protein sequences are compared with a set of HMMs to detect functional similarities. Efficient dynamic-programming algorithms exist for solving this problem; however, current solutions still require significant scan times. These scan time requirements are likely to become even more severe due to the rapid growth in the size of these databases. This paper shows how reconfigurable architectures can be used to derive an efficient fine-grained parallelization of the dynamic programming calculation. We describe how this technique leads to significant runtime savings for HMM database scanning on a standard off-the-shelf field-programmable gate array (FPGA).
Addresses
  • School of Computer Engineering, Nanyang Technological University, Singapore 639798, Singapore. tim.oliver@pmail.ntu.edu.sg
Autoren
DOI
10.1109/titb.2007.904632
eISSN
1558-0032
Externe Identifier
  • PubMed Identifier: 19273034
Open access
false
ISSN
1089-7771
Ausgabe der Veröffentlichung
5
Zeitschrift
IEEE transactions on information technology in biomedicine : a publication of the IEEE Engineering in Medicine and Biology Society
Schlüsselwörter
  • Markov Chains
  • Sequence Alignment
  • Sequence Analysis, Protein
  • Computational Biology
  • Algorithms
  • Pattern Recognition, Automated
Sprache
eng
Medium
Print-Electronic
Online publication date
2008
Paginierung
740 - 746
Datum der Veröffentlichung
2009
Status
Published
Datum der Datenerfassung
2009
Titel
High speed biological sequence analysis with hidden Markov models on reconfigurable platforms.
Sub types
  • Journal Article
Ausgabe der Zeitschrift
13

Data source: Europe PubMed Central

Abstract
Molecular biologists use hidden Markov models (HMMs) as a popular tool to statistically describe biological sequence families. This statistical description can then be used for sensitive and selective database scanning, e.g., new protein sequences are compared with a set of HMMs to detect functional similarities. Efficient dynamic-programming algorithms exist for solving this problem; however, current solutions still require significant scan times. These scan time requirements are likely to become even more severe due to the rapid growth in the size of these databases. This paper shows how reconfigurable architectures can be used to derive an efficient fine-grained parallelization of the dynamic programming calculation. We describe how this technique leads to significant runtime savings for HMM database scanning on a standard off-the-shelf field-programmable gate array (FPGA).
Autoren
Autoren-URL
https://www.ncbi.nlm.nih.gov/pubmed/19273034
DOI
10.1109/TITB.2007.904632
eISSN
1558-0032
Ausgabe der Veröffentlichung
5
Zeitschrift
IEEE Trans Inf Technol Biomed
Schlüsselwörter
  • Algorithms
  • Computational Biology
  • Markov Chains
  • Pattern Recognition, Automated
  • Sequence Alignment
  • Sequence Analysis, Protein
Sprache
eng
Country
United States
Paginierung
740 - 746
Datum der Veröffentlichung
2009
Status
Published
Datum, an dem der Datensatz öffentlich gemacht wurde
2009
Titel
High speed biological sequence analysis with hidden Markov models on reconfigurable platforms.
Sub types
  • Journal Article
Ausgabe der Zeitschrift
13

Data source: PubMed

Autoren
Zeitschrift
IEEE Trans. Inf. Technol. Biomed.
Artikelnummer
5
Paginierung
740 - 746
Datum der Veröffentlichung
2009
Titel
High Speed Biological Sequence Analysis With Hidden Markov Models on Reconfigurable Platforms.
Ausgabe der Zeitschrift
13

Data source: DBLP

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