Unsupervised pattern discovery in human chromatin structure through genomic segmentation

UMMS Affiliation

Department of Biochemistry and Molecular Pharmacology; Program in Bioinformatics and Integrative Biology

Publication Date


Document Type



Bayes Theorem; Chromatin; *Genome, Human; Histones; Humans; K562 Cells; Molecular Sequence Data; Promoter Regions, Genetic; Regulatory Sequences, Nucleic Acid; Transcription Factors; *Transcription Initiation Site


Bioinformatics | Computational Biology | Genetics and Genomics | Systems Biology


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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Citation: Nat Methods. 2012 Mar 18;9(5):473-6. doi: 10.1038/nmeth.1937. Link to article on publisher's site

Journal/Book/Conference Title

Nature methods

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Link to Article in PubMed

PubMed ID