Dataset Information

Network analysis of ChIP-Seq data reveals key genes in prostate cancer.

ABSTRACT: BACKGROUND:Prostate cancer (PC) is the second most common cancer among men in the United States, and it imposes a considerable threat to human health. A deep understanding of its underlying molecular mechanisms is the premise for developing effective targeted therapies. Recently, deep transcriptional sequencing has been used as an effective genomic assay to obtain insights into diseases and may be helpful in the study of PC. METHODS:In present study, ChIP-Seq data for PC and normal samples were compared, and differential peaks identified, based upon fold changes (with P-values calculated with t-tests). Annotations of these peaks were performed. Protein-protein interaction (PPI) network analysis was performed with BioGRID and constructed with Cytoscape, following which the highly connected genes were screened. RESULTS:We obtained a total of 5,570 differential peaks, including 3,726 differentially enriched peaks in tumor samples and 1,844 differentially enriched peaks in normal samples. There were eight significant regions of the peaks. The intergenic region possessed the highest score (51%), followed by intronic (31%) and exonic (11%) regions. The analysis revealed the top 35 highly connected genes, which comprised 33 differential genes (such as YWHAQ, tyrosine 3-monooxygenase/tryptophan 5-monooxygenase activation protein and ? polypeptide) from ChIP-Seq data and 2 differential genes retrieved from the PPI network: UBA52 (ubiquitin A-52 residue ribosomal protein fusion product (1) and SUMO2 (SMT3 suppressor of mif two 3 homolog (2) . CONCLUSIONS:Our findings regarding potential PC-related genes increase the understanding of PC and provides direction for future research.

SUBMITTER: Zhang Y

PROVIDER: S-EPMC4171560 | biostudies-literature | 2014 Sep

REPOSITORIES: biostudies-literature

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Network analysis of ChIP-Seq data reveals key genes in prostate cancer.

Zhang Yu Y Huang Zhen Z Zhu Zhiqiang Z Liu Jianwei J Zheng Xin X Zhang Yuhai Y

European journal of medical research 20140903

<h4>Background</h4>Prostate cancer (PC) is the second most common cancer among men in the United States, and it imposes a considerable threat to human health. A deep understanding of its underlying molecular mechanisms is the premise for developing effective targeted therapies. Recently, deep transcriptional sequencing has been used as an effective genomic assay to obtain insights into diseases and may be helpful in the study of PC.<h4>Methods</h4>In present study, ChIP-Seq data for PC and norma ...[more]

PMID: 25183411

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Project description:Chromatin immunoprecipitation followed by massively parallel DNA sequencing (ChIP-seq) or microarray hybridization (ChIP-chip) has been widely used to determine the genomic occupation of transcription factors (TFs). We have previously developed a probabilistic method, called TIP (Target Identification from Profiles), to identify TF target genes using ChIP-seq/ChIP-chip data. To achieve high specificity, TIP applies a conservative method to estimate significance of target genes, with the trade-off being a relatively low sensitivity of target gene identification compared to other methods. Additionally, TIP's output does not render binding-peak locations or intensity, information highly useful for visualization and general experimental biological use, while the variability of ChIP-seq/ChIP-chip file formats has made input into TIP more difficult than desired.To improve upon these facets, here we present are fined TIP with key extensions. First, it implements a Gaussian mixture model for p-value estimation, increasing target gene identification sensitivity and more accurately capturing the shape of TF binding profile distributions. Second, it enables the incorporation of TF binding-peak data by identifying their locations in significant target gene promoter regions and quantifies their strengths. Finally, for full ease of implementation we have incorporated it into a web server ( http://syslab3.nchu.edu.tw/iTAR/ ) that enables flexibility of input file format, can be used across multiple species and genome assembly versions, and is freely available for public use. The web server additionally performs GO enrichment analysis for the identified target genes to reveal the potential function of the corresponding TF.The iTAR web server provides a user-friendly interface and supports target gene identification in seven species, ranging from yeast to human. To facilitate investigating the quality of ChIP-seq/ChIP-chip data, the web server generates the chart of the characteristic binding profiles and the density plot of normalized regulatory scores. The iTAR web server is a useful tool in identifying TF target genes from ChIP-seq/ChIP-chip data and discovering biological insights.

Dataset Information

Network analysis of ChIP-Seq data reveals key genes in prostate cancer.

Publications

Network analysis of ChIP-Seq data reveals key genes in prostate cancer.

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