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A Poisson mixture model to identify changes in RNA polymerase II binding quantity using high-throughput sequencing technology
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| Zeitschriftentitel: | BMC Genomics |
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| Personen und Körperschaften: | , , , , , |
| In: | BMC Genomics, 9, 2008, S2 |
| Format: | E-Article |
| Sprache: | Englisch |
| veröffentlicht: |
Springer Science and Business Media LLC
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| Schlagwörter: |
| Zusammenfassung: | <jats:title>Abstract</jats:title> <jats:p>We present a mixture model-based analysis for identifying differences in the distribution of RNA polymerase II (Pol II) in transcribed regions, measured using ChIP-seq (chromatin immunoprecipitation following massively parallel sequencing technology). The statistical model assumes that the number of Pol II-targeted sequences contained within each genomic region follows a <jats:italic>Poisson</jats:italic> distribution. A <jats:italic>Poisson</jats:italic> mixture model was then developed to distinguish Pol II binding changes in transcribed region using an empirical approach and an expectation-maximization (EM) algorithm developed for estimation and inference. In order to achieve a global maximum in the M-step, a particle swarm optimization (PSO) was implemented. We applied this model to Pol II binding data generated from hormone-dependent MCF7 breast cancer cells and antiestrogen-resistant MCF7 breast cancer cells before and after treatment with 17<jats:italic>β</jats:italic>-estradiol (E2). We determined that in the hormone-dependent cells, ~9.9% (2527) genes showed significant changes in Pol II binding after E2 treatment. However, only ~0.7% (172) genes displayed significant Pol II binding changes in E2-treated antiestrogen-resistant cells. These results show that a <jats:italic>Poisson</jats:italic> mixture model can be used to analyze ChIP-seq data.</jats:p> |
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| ISSN: |
1471-2164
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| DOI: | 10.1186/1471-2164-9-s2-s23 |