4.7 Article

Unsupervised segmentation of continuous genomic data

Journal

BIOINFORMATICS
Volume 23, Issue 11, Pages 1424-1426

Publisher

OXFORD UNIV PRESS
DOI: 10.1093/bioinformatics/btm096

Keywords

-

Funding

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

Ask authors/readers for more resources

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.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.7
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

No Data Available
No Data Available