Gollery Handbook of Hidden Markov Models in Bioinformatics

Erscheinungsjahr 2010
ISBN: 978-1-4200-1180-7
Verlag: Taylor & Francis
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 176 Seiten

Reihe: Chapman & Hall/CRC Mathematical & Computational Biology

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.

Zielgruppe

Advanced undergraduate and graduate students, researchers, and practitioners in bioinformatics and computational biology; biologists and biochemists who use HMM databases for sequence analysis.

Autoren/Hrsg.

Gollery, Martin

Fachgebiete

• Naturwissenschaften Biowissenschaften Biowissenschaften Genetik und Genomik (nichtmedizinisch)

Inhaltsverzeichnis

Introduction to HMMs and Related Profile Methods
Introduction to Sequence Analysis
Pairwise Algorithms: Smith–Waterman, FASTA, and BLAST
Pairwise Limitations
The Advantages of Profile Methods
The Rise of Profile HMMs
Regular Expressions
But What Exactly Is an HMM?
Curated vs. Noncurated Databases
Disadvantages and Limitations of Profile HMMs for Bioinformatics
Profile HMMs
A General Model HMMs
Plan 7 from Janelia Farms
Local Scoring
Global Alignments
The Maximum Entropy Model
Statistics
Other Uses for HMMs in Biology
HMM Methods
The HMMER Suite of Programs
Creating Multiple Alignments with HMMs
SAM
PSI-BLAST, PSI-TBLASTN, and RPS-BLAST
Regular Expression Methods
MEME and Meta-MEME
Wise2
Commercial and Alternative HMM Implementations
HMMER Options
HMM Databases
The Many Flavors of Pfam
SMART
TIGRfam
SUPERFAMILY
PANTHER
PRED-GPCR
CDD
COG
The TLfam Database
KINfam
PRIAM and metaSHARK
NODE
FPfam
KinasePhos
Building an Analytical Pipeline
What Is an Analytical Pipeline?
How Do I Create a Pipeline and What Do I Put Into It?
Is There An Easier Way to Manage My Workflow?
Are There Any Pipelines That I Can Simply Download and Install?
Building Custom Databases
Building HMMER Databases
Building Databases with the SAM Package
Building PSSM Databases for RPS-BLAST
Building Regular Expression Databases
Speeding Your Searches
Pick Your Targets Carefully
Format Selection
Optimized Solutions
Accelerated Computing
GeneWise
Other Uses of HMMs in Bioinformatics
Methods Comparing HMMs to Other HMMs
Subcellular Localization Prediction
Posttranslational Modification Prediction
Binding Site Predictions
Gene Finding Programs
MEME, MAST, and Meta-MEME
Index
An Introduction, a Summary, and Questions appear in each chapter.