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Unsupervised segmentation of continuous genomic data
Author(s) -
Nathan Day,
Andrew Hemmaplardh,
Robert E. Thurman,
J Stamatoyannopoulos,
William Stafford Noble
Publication year - 2007
Publication title -
bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/btm096
Subject(s) - smoothing , computer science , segmentation , rendering (computer graphics) , hidden markov model , wavelet , artificial intelligence , data mining , pattern recognition (psychology) , computational biology , biology , computer vision
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.

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