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Using a latent variable approach to integrate next generation sequencing data from epigenomics and transcriptomics studies

Friday, November 08, 2013 — Poster Session IV

2:00 p.m. – 4:00 p.m.

FAES Academic Center (Upper-Level Terrace)




  • K. Shen
  • M. Leerkes
  • D. Hurt


Rapidly developing next generation sequencing technologies make it possible to provide novel insights into patients’ genomic landscapes by simultaneously profiling DNA methylation (the epigenome) and gene expression (the transcriptome). However, these high-dimensional and fundamentally disparate datasets pose special challenges for interpretation, and new integrative analysis methods are greatly needed. We developed a robust pipeline to integrate bisulfite-seq DNA methylation data and RNA-seq gene expression data based on a latent variable approach. We report on the application of this new integrative analysis method to breast cancer cell line data sets. Common patterns between alteration of DNA methylation and gene expression were identified among the cell lines using this method. When these patterns are coupled with the drug response data for the cell lines, light may be shed on how drug resistance develops in cancers. This method can also be applied to other “omic” data sets for integrative analysis.

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