Work

Unsupervised segmentation of continuous genomic data

Year: 2007

Type: article

Abstract: UNLABELLED: 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

Source: Bioinformatics

DOI: 10.1093/bioinformatics/btm096

Authors: Nathan Day, Andrew Hemmaplardh, Robert E. Thurman, John A. Stamatoyannopoulos, William S. Noble

Institution: University of Washington

Language: English

Topic: Genomics and Chromatin Dynamics

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: hybrid

Estimated APC paid: $3,989

Funders: National Institutes of Health, National Human Genome Research Institute, National Institute of General Medical Sciences