iTAR: a web server for identifying target genes of transcription factors using ChIP-seq or ChIP-chip data

Abstract

BackgroundChromatin 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.DescriptionTo 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.ConclusionsThe 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.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-016-2963-0) contains supplementary material, which is available to authorized users.

Identification of TMPRSS2 and ERG as androgen receptor targeted putative highly significant genes in PC3-AR cells from AR ChIP-Seq and ChIP-chip data

“…However, sometimes, some peaks with only few sequencing tags(to say, as few as only 2 in number) would be predicted by CCAT as putative signi cant peaks. Although one may critically argue that peaks with as few as only 2 sequencing tags might stand for weak transcription factor(TF) or histone modi cation(HM) binding signals, in some other international researchers' opinions, these peaks mightn't be different from background noise and the ChIP-Seq data analysis programs like the CisGenome [39] as well as TIP [44] and iTAR [45] et al would usually lter out these peaks and deem them as insigni cant ones in the ChIP-Seq data analysis. From this sense, since the 'one-sample' ChIP-Seq data analysis would usually results in much more peaks reported than the 'two-sample' ChIP-Seq data analysis, sometimes or in a number of cases, the 'one-sample' ChIP-Seq data analysis mightn't be worse than the 'two-sample' ChIP-Seq data analysis in terms of total number of genuine peaks found if disregarding the total false positives and false discovery rate(FDR) reducing effects.…”

A New Weighted Imputed Neighborhood-Regularized Tri-Factorization One-Class Collaborative Filtering Algorithm: Application to Target Gene Prediction of Transcription Factors

“…Target identification from profile (TIP) is a probabilistic model that ranks target genes for TFs based on the relative binding signal strength from ChIP experiments, with an assumption that the binding signal is normally distributed [26]. Identifying target genes (iTAR) is an online server, which is designed to overcome the limitation from the normality assumption in TIP by applying Gaussian mixture model for p-value estimation [27]. Covariance based extraction of regulatory targets using multiple time series (CERMT) predicts TF target genes under an assumption that the true target genes for TFs will show similar response pattern to the TFs [28].…”

Target Gene Prediction of Transcription Factor Using a New Neighborhood-regularized Tri-factorization One-class Collaborative Filtering Algorithm

“…Target identification from profile (TIP) is a probabilistic model that ranks target genes for TFs based on the relative binding signal strength from ChIP experiments, with an assumption that the binding signal is normally distributed [28]. Identifying target genes (iTAR) is an online server, which is designed to overcome the limitation from the normality assumption in TIP by applying Gaussian mixture model for p-value estimation [29]. Covariance based extraction of regulatory targets using multiple time series (CERMT) predicts TF target genes under an assumption that the true target genes for TFs will show similar response pattern to the TFs [30].…”