Unsupervised segmentation of continuous genomic data

被引:75
作者
Day, Nathan
Hemmaplardh, Andrew
Thurman, Robert E. [1 ]
Stamatoyannopoulos, John A.
Noble, William S.
机构
[1] Univ Washington, Dept Comp Sci & Engn, Seattle, WA 98195 USA
[2] Univ Washington, Div Med Genet, Seattle, WA 98195 USA
[3] Univ Washington, Dept Genome Sci, Seattle, WA 98195 USA
关键词
D O I
10.1093/bioinformatics/btm096
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
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.
引用
收藏
页码:1424 / 1426
页数:3
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