{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,24]],"date-time":"2026-06-24T18:07:39Z","timestamp":1782324459005,"version":"3.54.5"},"reference-count":30,"publisher":"Oxford University Press (OUP)","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2013,1,1]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Motivation: Many studies have shown that epigenetic changes, such as altered DNA methylation and histone modifications, are linked to estrogen receptor \u03b1 (ER\u03b1)-positive tumors and disease prognoses. Several recent studies have applied high-throughput technologies such as ChIP-seq and MBD-seq to interrogate the altered architectures of ER\u03b1 regulation in tamoxifen (Tam)-resistant breast cancer cells. However, the details of combinatorial epigenetic regulation of ER\u03b1 target genes in breast cancers with acquired Tam resistance have not yet been fully examined.<\/jats:p><jats:p>Results: We developed a computational approach to identify and analyze epigenetic patterns associated with Tam resistance in the MCF7-T cell line as opposed to the Tam-sensitive MCF7 cell line, with the goal of understanding the underlying mechanisms of epigenetic regulatory influence on resistance to Tam treatment in breast cancer. In this study, we used ChIP-seq of ER\u03b1, RNA polymerase II, three histone modifications and MBD-seq data of DNA methylation in MCF7 and MCF7-T cells to train hidden Markov models (HMMs). We applied the Bayesian information criterion to determine that a 20-state HMM was best, which was reduced to a 14-state HMM with a Bayesian information criterion score of 1.21291 \u00d7 107. We further identified four classes of biologically meaningful states in this breast cancer cell model system, and a set of ER\u03b1 combinatorial epigenetic regulated target genes. The correlated gene expression level and gene ontology analyses showed that different gene ontology terms were enriched with Tam-resistant versus sensitive breast cancer cells. Our study illustrates the applicability of HMM-based analysis of genome-wide high-throughput genomic data to study epigenetic influences on E2\/ER\u03b1 regulation in breast cancer.<\/jats:p><jats:p>Contact: \u00a0victor.jin@osumc.edu<\/jats:p><jats:p>Supplementary information: \u00a0Supplementary data are available at Bioinformatics online.<\/jats:p>","DOI":"10.1093\/bioinformatics\/bts639","type":"journal-article","created":{"date-parts":[[2012,10,27]],"date-time":"2012-10-27T00:18:50Z","timestamp":1351297130000},"page":"22-28","source":"Crossref","is-referenced-by-count":12,"title":["A hidden Markov model to identify combinatorial epigenetic regulation patterns for estrogen receptor \u03b1 target genes"],"prefix":"10.1093","volume":"29","author":[{"given":"Russell","family":"Bonneville","sequence":"first","affiliation":[{"name":"Department of Biomedical Informatics, The Ohio State University, Columbus, OH 43210, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Victor X.","family":"Jin","sequence":"additional","affiliation":[{"name":"Department of Biomedical Informatics, The Ohio State University, Columbus, OH 43210, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2012,10,26]]},"reference":[{"key":"2023020303304548900_bts639-B1","doi-asserted-by":"crossref","first-page":"164","DOI":"10.1214\/aoms\/1177697196","article-title":"A maximization technique occurring in the statistical analysis of probabilistic functions of Markov chains","volume":"41","author":"Baum","year":"1970","journal-title":"Ann. 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