Unsupervised segmentation of continuous genomic data
Nathan Day, Andrew Hemmaplardh, Robert E. Thurman, John A. Stamatoyannopoulos and William Stafford Noble
Bioinformatics. 23(11):1424-1426, 2007
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.