Dataset Information

ChIPXpress: using publicly available gene expression data to improve ChIP-seq and ChIP-chip target gene ranking.

ABSTRACT:

Background

ChIPx (i.e., ChIP-seq and ChIP-chip) is increasingly used to map genome-wide transcription factor (TF) binding sites. A single ChIPx experiment can identify thousands of TF bound genes, but typically only a fraction of these genes are functional targets that respond transcriptionally to perturbations of TF expression. To identify promising functional target genes for follow-up studies, researchers usually collect gene expression data from TF perturbation experiments to determine which of the TF targets respond transcriptionally to binding. Unfortunately, approximately 40% of ChIPx studies do not have accompanying gene expression data from TF perturbation experiments. For these studies, genes are often prioritized solely based on the binding strengths of ChIPx signals in order to choose follow-up candidates. ChIPXpress is a novel method that improves upon this ChIPx-only ranking approach by integrating ChIPx data with large amounts of Publicly available gene Expression Data (PED).

Results

We demonstrate that PED does contain useful information to identify functional TF target genes despite its inherent heterogeneity. A truncated absolute correlation measure is developed to better capture the regulatory relationships between TFs and their target genes in PED. By integrating the information from ChIPx and PED, ChIPXpress can significantly increase the chance of finding functional target genes responsive to TF perturbation among the top ranked genes. ChIPXpress is implemented as an easy-to-use R/Bioconductor package. We evaluate ChIPXpress using 10 different ChIPx datasets in mouse and human and find that ChIPXpress rankings are more accurate than rankings based solely on ChIPx data and may result in substantial improvement in prediction accuracy, irrespective of which peak calling algorithm is used to analyze the ChIPx data.

Conclusions

ChIPXpress provides a new tool to better prioritize TF bound genes from ChIPx experiments for follow-up studies when investigators do not have their own gene expression data. It demonstrates that the regulatory information from PED can be used to boost ChIPx data analyses. It also represents an important step towards more fully utilizing the valuable, but highly heterogeneous data contained in public gene expression databases.

SUBMITTER: Wu G

PROVIDER: S-EPMC3684512 | biostudies-literature | 2013 Jun

REPOSITORIES: biostudies-literature

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Publications

ChIPXpress: using publicly available gene expression data to improve ChIP-seq and ChIP-chip target gene ranking.

Wu George G Ji Hongkai H

BMC bioinformatics 20130610

<h4>Background</h4>ChIPx (i.e., ChIP-seq and ChIP-chip) is increasingly used to map genome-wide transcription factor (TF) binding sites. A single ChIPx experiment can identify thousands of TF bound genes, but typically only a fraction of these genes are functional targets that respond transcriptionally to perturbations of TF expression. To identify promising functional target genes for follow-up studies, researchers usually collect gene expression data from TF perturbation experiments to determi ...[more]

PMID: 23758851

Similar Datasets

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

ChIPXpress: using publicly available gene expression data to improve ChIP-seq and ChIP-chip target gene ranking.

Background

Results

Conclusions

Publications

ChIPXpress: using publicly available gene expression data to improve ChIP-seq and ChIP-chip target gene ranking.

Similar Datasets

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