title: Unsupervised segmentation of continuous genomic data.
abstract: 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
authors: Department of Computer Science and Engineering, University of Washington, Seattle, WA, USA.
authors: Nathan
authors: N
authors: Day
authors: Andrew
authors: A
authors: Hemmaplardh
authors: Robert E
authors: RE
authors: Thurman
authors: John A
authors: JA
authors: Stamatoyannopoulos
authors: William S
authors: WS
authors: Noble
mesh_terms: 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*