Unsupervised pattern discovery in human chromatin structure through genomic segmentation

Abstract

Segway, a method using dynamic Bayesian network techniques, segments a genome and produces functional labels defined by histone modifications, transcription-factor binding, locations of open chromatin and other genome-wide functional data. 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/ .