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 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.


BACKGROUND
http://noble.gs.washington.edu/proj/hmmseg
Day
Nathan
N
Department of Computer Science and Engineering, University of Washington, Seattle, WA, USA.
Hemmaplardh
Andrew
A
Thurman
Robert E
RE
Stamatoyannopoulos
John A
JA
Noble
William S
WS
D000465:Algorithms; D001185:Artificial Intelligence; D002874:Chromosome Mapping / Q000379:methods*; D003198:Computer Simulation; D030541:Databases, Genetic; D016247:Information Storage and Retrieval / Q000379:methods; D008390:Markov Chains; D008957:Models, Genetic; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods*; D017422:Sequence Analysis, DNA / Q000379:methods*