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CoopTFD: a repository for predicted yeast cooperative transcription factor pairs.

ABSTRACT: In eukaryotic cells, transcriptional regulation of gene expression is usually accomplished by cooperative Transcription Factors (TFs). Therefore, knowing cooperative TFs is helpful for uncovering the mechanisms of transcriptional regulation. In yeast, many cooperative TF pairs have been predicted by various algorithms in the literature. However, until now, there is still no database which collects the predicted yeast cooperative TFs from existing algorithms. This prompts us to construct Cooperative Transcription Factors Database (CoopTFD), which has a comprehensive collection of 2622 predicted cooperative TF pairs (PCTFPs) in yeast from 17 existing algorithms. For each PCTFP, our database also provides five types of validation information: (i) the algorithms which predict this PCTFP, (ii) the publications which experimentally show that this PCTFP has physical or genetic interactions, (iii) the publications which experimentally study the biological roles of both TFs of this PCTFP, (iv) the common Gene Ontology (GO) terms of this PCTFP and (v) the common target genes of this PCTFP. Based on the provided validation information, users can judge the biological plausibility of a PCTFP of interest. We believe that CoopTFD will be a valuable resource for yeast biologists to study the combinatorial regulation of gene expression controlled by cooperative TFs.Database URL: http://cosbi.ee.ncku.edu.tw/CoopTFD/ or http://cosbi2.ee.ncku.edu.tw/CoopTFD/.

SUBMITTER: Wu WS

PROVIDER: S-EPMC4885606 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

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CoopTFD: a repository for predicted yeast cooperative transcription factor pairs.

Wu Wei-Sheng WS Lai Fu-Jou FJ Tu Bor-Wen BW Chang Darby Tien-Hao DT

Database : the journal of biological databases and curation 20160530

In eukaryotic cells, transcriptional regulation of gene expression is usually accomplished by cooperative Transcription Factors (TFs). Therefore, knowing cooperative TFs is helpful for uncovering the mechanisms of transcriptional regulation. In yeast, many cooperative TF pairs have been predicted by various algorithms in the literature. However, until now, there is still no database which collects the predicted yeast cooperative TFs from existing algorithms. This prompts us to construct Cooperat ...[more]

PMID: 27242036

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Project description:BackgroundTranscriptional regulation of gene expression in eukaryotes is usually accomplished by cooperative transcription factors (TFs). Computational identification of cooperative TF pairs has become a hot research topic and many algorithms have been proposed in the literature. A typical algorithm for predicting cooperative TF pairs has two steps. (Step 1) Define the targets of each TF under study. (Step 2) Design a measure for calculating the cooperativity of a TF pair based on the targets of these two TFs. While different algorithms have distinct sophisticated cooperativity measures, the targets of a TF are usually defined using ChIP-chip data. However, there is an inherent weakness in using ChIP-chip data to define the targets of a TF. ChIP-chip analysis can only identify the binding targets of a TF but it cannot distinguish the true regulatory from the binding but non-regulatory targets of a TF.ResultsThis work is the first study which aims to investigate whether the performance of computational identification of cooperative TF pairs could be improved by using a more biologically relevant way to define the targets of a TF. For this purpose, we propose four simple algorithms, all of which consist of two steps. (Step 1) Define the targets of a TF using (i) ChIP-chip data in the first algorithm, (ii) TF binding data in the second algorithm, (iii) TF perturbation data in the third algorithm, and (iv) the intersection of TF binding and TF perturbation data in the fourth algorithm. Compared with the first three algorithms, the fourth algorithm uses a more biologically relevant way to define the targets of a TF. (Step 2) Measure the cooperativity of a TF pair by the statistical significance of the overlap of the targets of these two TFs using the hypergeometric test. By adopting four existing performance indices, we show that the fourth proposed algorithm (PA4) significantly out performs the other three proposed algorithms. This suggests that the computational identification of cooperative TF pairs is indeed improved when using a more biologically relevant way to define the targets of a TF. Strikingly, the prediction results of our simple PA4 are more biologically meaningful than those of the 12 existing sophisticated algorithms in the literature, all of which used ChIP-chip data to define the targets of a TF. This suggests that properly defining the targets of a TF may be more important than designing sophisticated cooperativity measures. In addition, our PA4 has the power to predict several experimentally validated cooperative TF pairs, which have not been successfully predicted by any existing algorithms in the literature.ConclusionsThis study shows that the performance of computational identification of cooperative TF pairs could be improved by using a more biologically relevant way to define the targets of a TF. The main contribution of this study is not to propose another new algorithm but to provide a new thinking for the research of computational identification of cooperative TF pairs. Researchers should put more effort on properly defining the targets of a TF (i.e. Step 1) rather than totally focus on designing sophisticated cooperativity measures (i.e. Step 2). The lists of TF target genes, the Matlab codes and the prediction results of the four proposed algorithms could be downloaded from our companion website http://cosbi3.ee.ncku.edu.tw/TFI/.

Dataset Information

CoopTFD: a repository for predicted yeast cooperative transcription factor pairs.

Publications

CoopTFD: a repository for predicted yeast cooperative transcription factor pairs.

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