Project description:The datasets presented here are transcription factors, transcriptional co-factors or histone modifications associated with active enhancers in a variety of mouse and human cell types. ChIP-seq of enhancer-associated transcription factors and histone modifications
Project description:The datasets presented here are transcription factors, transcriptional co-factors or histone modifications associated with active enhancers in a variety of mouse and human cell types.
Project description:Prostate cancer is the most common cancer in men and androgen receptor (AR) downstream signalings promote prostate cancer progression. Although androgen deprivation therapy is effective for treating prostate cancer, most of tumors relapsed as castration-resistant prostate cancer (CRPC). We performed ChIP-seq analysis to investigate the role of AR-associated factors and histone modifications using CRPC model cells, 22Rv1.
Project description:Prostate cancer is the most common cancer in men and androgen receptor (AR) downstream signalings promote prostate cancer progression. Although androgen deprivation therapy is effective for treating prostate cancer, most tumors relapsed as castration-resistant prostate cancer (CRPC). We performed ChIP-seq analysis to investigate the role of important transcription factors and histone modifications using AR positive prostate cancer cells, LNCaP, CRPC model cells, 22Rv1 and DU145 cells.
Project description:Transcriptional enhancers play critical roles in regulation of gene expression, but their identification has remained a challenge. Recently, it was shown that enhancers in the mammalian genome are associated with characteristic histone modification patterns, which have been increasingly exploited for enhancer identification. However, only a limited number of histone modifications have previously been investigated for this purpose, leaving the questions answered whether there exist an optimal set of histone modifications that could improve the enhancer prediction. Here, we address this issue by exploring a rich dataset produced by the human Epigenome Roadmap Project. Specifically, we examined genome-wide profiles of 24 histone modifications in human embryonic stem cells and fibroblasts, and developed a Random-Forest based algorithm to integrate histone modification profiles for identification of enhancers.As a training set, we used histone modification profiles at genome-wide binding sites of p300 in the two cell types identified using ChIP-seq. We show that this algorithm not only leads to more accurate and precise prediction of enhancers than previous methods, but also helps identify an optimal set of three chromatin marks for enhancer prediction. ChIP-Seq Analysis of p300 in hESC H1 and IMR90 cells. Sequencing was done on the Illumina Genome Analyzer II platform for the H1 data and Illumina HiSeq for IMR90.Data was mapped to hg18 using Bowtie.
Project description:Transdifferentiation of BLaER1 B cell into macrophages is an appropriate model to understand how chromatin behaves along a dynamic process. With this purpose, we have performed chromatin immunoprecipitation experiments of two histone modifications associated to active enhancer activity along 4 time points of BLaER1 transdifferentiation.