Unsupervised segmentation of continuous genomic data

The advent of high-density, high-volume genomic data has created the need for tools to summarize large datasets at multiple scales. HMMSeg is a command-line utility for the scale-specific segmentation of continuous genomic data using hidden Markov models (HMMs). Scale specificity is achieved by an o...

Full description

Saved in:
Bibliographic Details
Published inBioinformatics Vol. 23; no. 11; pp. 1424 - 1426
Main Authors Day, Nathan, Hemmaplardh, Andrew, Thurman, Robert E., Stamatoyannopoulos, John A., Noble, William S.
Format Journal Article
LanguageEnglish
Published Oxford Oxford University Press 01.06.2007
Oxford Publishing Limited (England)
Subjects
Algorithms
Artificial Intelligence
Biological and medical sciences
Chromosome Mapping - methods
Computer Simulation
Databases, Genetic
Fundamental and applied biological sciences. Psychology
General aspects
Information Storage and Retrieval - methods
Markov Chains
Mathematics in biology. Statistical analysis. Models. Metrology. Data processing in biology (general aspects)
Models, Genetic
Models, Statistical
Pattern Recognition, Automated - methods
Sequence Analysis, DNA - methods
Segmentation
Genomics
DNA chip
Data
Scale
Phylogeny
Gene expression
Scope note
Continuous process
Hidden Markov model
Web site
Bioinformatics
EM algorithm
Online AccessGet full text

Cover

More Information
Summary:The advent of high-density, high-volume genomic data has created the need for tools to summarize large datasets at multiple scales. HMMSeg is a command-line utility for the scale-specific segmentation of continuous genomic data using hidden Markov models (HMMs). Scale specificity is achieved by an optional wavelet-based smoothing operation. HMMSeg is capable of handling multiple datasets simultaneously, rendering it ideal for integrative analysis of expression, phylogenetic and functional genomic data. Availability: http://noble.gs.washington.edu/proj/hmmseg Contact: rthurman@u.washington.edu
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ObjectType-Article-2
ObjectType-Feature-1
ISSN:1367-4803
1367-4811
1460-2059
DOI:10.1093/bioinformatics/btm096