Unsupervised segmentation of continuous genomic data

Bioinformatics. 2007 Jun 1;23(11):1424-6. doi: 10.1093/bioinformatics/btm096. Epub 2007 Mar 23.


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

Publication types

  • Research Support, N.I.H., Extramural

MeSH terms

  • Algorithms*
  • Artificial Intelligence*
  • Chromosome Mapping / methods*
  • Computer Simulation
  • Databases, Genetic*
  • Information Storage and Retrieval / methods
  • Markov Chains
  • Models, Genetic
  • Models, Statistical
  • Pattern Recognition, Automated / methods*
  • Sequence Analysis, DNA / methods*