Unsupervised pattern discovery in human chromatin structure through genomic segmentation

Nature Methods
Michael M HoffmanWilliam Stafford Noble

Abstract

We trained Segway, a dynamic Bayesian network method, simultaneously on chromatin data from multiple experiments, including positions of histone modifications, transcription-factor binding and open chromatin, all derived from a human chronic myeloid leukemia cell line. In an unsupervised fashion, we identified patterns associated with transcription start sites, gene ends, enhancers, transcriptional regulator CTCF-binding regions and repressed regions. Software and genome browser tracks are at http://noble.gs.washington.edu/proj/segway/.

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Related Concepts

Bayesian Prediction
Chromatin
Histone H7
Early Promoters, Genetic
Regulatory Sequences, Nucleic Acid
Transcription Factor
Genome, Human
K562 Cells
Transcription Initiation Site
Chromatin

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