Handbook of Hidden Markov Models in Bioinformatics

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A01=Martin Gollery
acid
amino
Author_Martin Gollery
automated protein function annotation
Biological Sequence Analysis
Blast Hit
Blast Similarity Search
Cat Walking
Category=PS
computational genomics methods
database
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eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
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EST Database
EST Sequence
family
FPGA Chip
General Model
Hidden Markov Models
HMM
hmmer
KEGG Pathway
Maximum Entropy Model
molecular bioinformatics tools
MRC Laboratory
NCBI Website
package
Pairwise Alignment
pfam
Pfam Database
Pipeline Pilot
probabilistic sequence analysis
PROSITE Database
protein
protein domain prediction
PSSM
Ram Disk
regular
Regular Expression
SAM Format
SAM Model
sequence alignment algorithms
sequence motif discovery
sequences
Smith Waterman Algorithm
String Database

Product details

  • ISBN 9781584886846
  • Weight: 412g
  • Dimensions: 156 x 234mm
  • Publication Date: 12 Jun 2008
  • Publisher: Taylor & Francis Inc
  • Publication City/Country: US
  • Product Form: Hardback
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Demonstrating that many useful resources, such as databases, can benefit most bioinformatics projects, the Handbook of Hidden Markov Models in Bioinformatics focuses on how to choose and use various methods and programs available for hidden Markov models (HMMs).

The book begins with discussions on key HMM and related profile methods, including the HMMER package, the sequence analysis method (SAM), and the PSI-BLAST algorithm. It then provides detailed information about various types of publicly available HMM databases, such as Pfam, PANTHER, COG, and metaSHARK. After outlining ways to develop and use an automated bioinformatics workflow, the author describes how to make custom HMM databases using HMMER, SAM, and PSI-BLAST. He also helps you select the right program to speed up searches. The final chapter explores several applications of HMM methods, including predictions of subcellular localization, posttranslational modification, and binding site.

By learning how to effectively use the databases and methods presented in this handbook, you will be able to efficiently identify features of biological interest in your data.

Gollery, Martin

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