Dataset Information

Identifying regulatory targets of cell cycle transcription factors using gene expression and ChIP-chip data.

ABSTRACT: BACKGROUND: ChIP-chip data, which indicate binding of transcription factors (TFs) to DNA regions in vivo, are widely used to reconstruct transcriptional regulatory networks. However, the binding of a TF to a gene does not necessarily imply regulation. Thus, it is important to develop methods to identify regulatory targets of TFs from ChIP-chip data. RESULTS: We developed a method, called Temporal Relationship Identification Algorithm (TRIA), which uses gene expression data to identify a TF's regulatory targets among its binding targets inferred from ChIP-chip data. We applied TRIA to yeast cell cycle microarray data and identified many plausible regulatory targets of cell cycle TFs. We validated our predictions by checking the enrichments for functional annotation and known cell cycle genes. Moreover, we showed that TRIA performs better than two published methods (MA-Network and MFA). It is known that co-regulated genes may not be co-expressed. TRIA has the ability to identify subsets of highly co-expressed genes among the regulatory targets of a TF. Different functional roles are found for different subsets, indicating the diverse functions a TF could have. Finally, for a control, we showed that TRIA also performs well for cell-cycle irrelevant TFs. CONCLUSION: Finding the regulatory targets of TFs is important for understanding how cells change their transcription program to adapt to environmental stimuli. Our algorithm TRIA is helpful for achieving this purpose.

SUBMITTER: Wu WS

PROVIDER: S-EPMC1906835 | biostudies-other | 2007

REPOSITORIES: biostudies-other

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Identifying regulatory targets of cell cycle transcription factors using gene expression and ChIP-chip data.

Wu Wei-Sheng WS Li Wen-Hsiung WH Chen Bor-Sen BS

BMC bioinformatics 20070608

<h4>Background</h4>ChIP-chip data, which indicate binding of transcription factors (TFs) to DNA regions in vivo, are widely used to reconstruct transcriptional regulatory networks. However, the binding of a TF to a gene does not necessarily imply regulation. Thus, it is important to develop methods to identify regulatory targets of TFs from ChIP-chip data.<h4>Results</h4>We developed a method, called Temporal Relationship Identification Algorithm (TRIA), which uses gene expression data to identi ...[more]

PMID: 17559637

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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.

Project description:BackgroundTranscription factor knockout microarrays (TFKMs) provide useful information about gene regulation. By using statistical methods for detecting differentially expressed genes between the gene expression microarray data of the mutant and wild type strains, the TF knockout targets of the knocked-out TF can be identified. However, the identified TF knockout targets may contain a certain amount of false positives due to the experimental noises inherent in the high-throughput microarray technology. Even if the identified TF knockout targets are true, the molecular mechanisms of how a TF regulates its TF knockout targets remain unknown by this kind of statistical approaches.ResultsTo solve these two problems, we developed a method to filter out the false positives in the original TF knockout targets (identified by statistical approaches) so that the biologically interpretable TF knockout targets can be extracted. Our method can further generate experimentally testable hypotheses of the molecular mechanisms of how a TF regulates its biologically interpretable TF knockout targets. The details of our method are as follows. First, a TF binding network was constructed using the ChIP-chip data deposited in the YEASTRACT database. Then for each original TF knockout target, it is said to be biologically interpretable if a path (in the TF binding network) from the knocked-out TF to this target could be identified by our path search algorithm. The identified path explains how the TF may regulate this target either directly by binding to its promoter or indirectly through intermediate TFs. After checking all the original TF knockout targets, the biologically interpretable ones could be extracted and the false positives could be filtered out. We validated the biological significance of our refined (i.e., biologically interpretable) TF knockout targets by assessing their functional enrichment, expression coherence, and the prevalence of protein-protein interactions. Our refined TF knockout targets outperform the original TF knockout targets across all measures.ConclusionsBy jointly analyzing the TFKM and ChIP-chip data, our method can extract the biologically interpretable TF knockout targets by identifying paths (in the TF binding network) from the knocked-out TF to these targets. The identified paths form experimentally testable hypotheses regarding the molecular mechanisms of how a TF may regulate its knockout targets. About seven hundred hypotheses generated by our methods have been experimentally validated in the literature. Our work demonstrates that integrating different data sources is a powerful approach to study complex biological systems.

Dataset Information

Identifying regulatory targets of cell cycle transcription factors using gene expression and ChIP-chip data.

Publications

Identifying regulatory targets of cell cycle transcription factors using gene expression and ChIP-chip data.

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