4.7 Article

Unsupervised segmentation of continuous genomic data

期刊

BIOINFORMATICS
卷 23, 期 11, 页码 1424-1426

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OXFORD UNIV PRESS
DOI: 10.1093/bioinformatics/btm096

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资金

  1. NHGRI NIH HHS [U01 HG003161, U54 HG004592] Funding Source: Medline
  2. NIGMS NIH HHS [R01 GM71852, R01 GM071923] Funding Source: Medline

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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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