CoopTFD: a repository for predicted yeast cooperative transcription factor pairs

Wu, Wei; Lai, Fu Jou; Tu, Bor Wen; Chang, Darby Tien Hao

doi:10.1093/database/baw092

Cited by 3 publications

(5 citation statements)

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“…(ii) A network of cooperative TFs for the chosen CoopTFS is constructed. An edge between two TFs exists if these two TFs have physical interaction ( 23 ), genetic interaction ( 23 ) or predicted cooperativity ( 25 ) ( Figure 3b ). Note that if a CoopTFS is of biological relevance, we expect to see many edges in the network.…”

Section: Utility and Discussionmentioning

confidence: 99%

“…First, Yeast Promoter Atlas (YPA) ( 30 ) integrates nine kinds of promoter features for each yeast gene. Second, Cooperative Transcription Factors Database (CoopTFD) ( 25 ) has a comprehensive collection of 2622 predicted cooperative TF pairs in yeast from 17 existing algorithms. Third, Yeast Combinatorial Regulation Database (YCRD) ( 17 ) deposits 434197 regulatory associations between 2535 cooperative TF pairs and 6243 genes.…”

Section: Utility and Discussionmentioning

confidence: 99%

“…Following SGD, we use BioGRID as the source of interaction data in YGMD. Third, 2622 predicted cooperative TF pairs were collected from 17 existing algorithms [see CoopTFD ( 25 ) for details]. Fourth, nine kinds of associations between 695005 yeast gene-gene pairs were downloaded from YeastNet ( 26 ).…”

Section: Construction and Contentsmentioning

confidence: 99%

“…In this network, two nodes (i.e. TFs) are connected by an edge if they have physical interaction, genetic interaction [retrieved from BioGRID ( 23 )] or predicted cooperativity by existing algorithms [retrieved from CoopTFD ( 25 )]. Finally, a TF set is called a CoopTFS if the corresponding four-node network has only one connected component.…”

Section: Construction and Contentsmentioning

confidence: 99%

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YGMD: a repository for yeast cooperative transcription factor sets and their target gene modules

Chen

et al. 2017

Database

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By organizing the genome into gene modules (GMs), a living cell coordinates the activities of a set of genes to properly respond to environmental changes. The transcriptional regulation of the expression of a GM is usually carried out by a cooperative transcription factor set (CoopTFS) consisting of several cooperative transcription factors (TFs). Therefore, a database which provides CoopTFSs and their target GMs is useful for studying the cellular responses to internal or external stimuli. To address this need, here we constructed YGMD (Yeast Gene Module Database) to provide 34120 CoopTFSs, each of which consists of two to five cooperative TFs, and their target GMs. The cooperativity between TFs in a CoopTFS is suggested by physical/genetic interaction evidence or/and predicted by existing algorithms. The target GM regulated by a CoopTFS is defined as the common target genes of all the TFs in that CoopTFS. The regulatory association between any TF in a CoopTFS and any gene in the target GM is supported by experimental evidence in the literature. In YGMD, users can (i) search the GM regulated by a specific CoopTFS of interest or (ii) search all possible CoopTFSs whose target GMs contain a specific gene of interest. The biological relevance of YGMD is shown by a case study which demonstrates that YGMD can provide a GM enriched with genes known to be regulated by the query CoopTFS (Cbf1-Met4-Met32). We believe that YGMD provides a valuable resource for yeast biologists to study the transcriptional regulation of GMs. Database URL: http://cosbi4.ee.ncku.edu.tw/YGMD/, http://cosbi5.ee.ncku.edu.tw/YGMD/ or http://cosbi.ee.ncku.edu.tw/YGMD/

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Section: Utility and Discussionmentioning

confidence: 99%

Section: Utility and Discussionmentioning

confidence: 99%

Section: Construction and Contentsmentioning

confidence: 99%

Section: Construction and Contentsmentioning

confidence: 99%

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YGMD: a repository for yeast cooperative transcription factor sets and their target gene modules

Chen

et al. 2017

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“…Transcription factors are thought to cooperate with one another and regulate target genes coordinately [16]. Various methods have been proposed to identify regulatory sets that extract the participating genes and TFs in certain cellular responses [17, 18]. But to elucidate the molecular mechanisms behind the functional redundancy that cause the low overlapping between the TF ChIP experiments and knockout experiments, one needs to further genome-widely consider detailed cooperative TF regulatory modules as a whole to understand the molecular basis of back-up mechanisms in transcription programs since TF regulatory modules include different interactions between the TFs and related regulatory proteins that may involve in the regulation process [19].…”

Section: Introductionmentioning

confidence: 99%

Transcription factor regulatory modules provide the molecular mechanisms for functional redundancy observed among transcription factors in yeast

Yang

2019

BMC Bioinformatics

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BackgroundCurrent technologies for understanding the transcriptional reprogramming in cells include the transcription factor (TF) chromatin immunoprecipitation (ChIP) experiments and the TF knockout experiments. The ChIP experiments show the binding targets of TFs against which the antibody directs while the knockout techniques find the regulatory gene targets of the knocked-out TFs. However, it was shown that these two complementary results contain few common targets. Researchers have used the concept of TF functional redundancy to explain the low overlap between these two techniques. But the detailed molecular mechanisms behind TF functional redundancy remain unknown. Without knowing the possible molecular mechanisms, it is hard for biologists to fully unravel the cause of TF functional redundancy.ResultsTo mine out the molecular mechanisms, a novel algorithm to extract TF regulatory modules that help explain the observed TF functional redundancy effect was devised and proposed in this research. The method first searched for candidate TF sets from the TF binding data. Then based on these candidate sets the method utilized the modified Steiner Tree construction algorithm to construct the possible TF regulatory modules from protein-protein interaction data and finally filtered out the noise-induced results by using confidence tests. The mined-out regulatory modules were shown to correlate to the concept of functional redundancy and provided testable hypotheses of the molecular mechanisms behind functional redundancy. And the biological significance of the mined-out results was demonstrated in three different biological aspects: ontology enrichment, protein interaction prevalence and expression coherence. About 23.5% of the mined-out TF regulatory modules were literature-verified. Finally, the biological applicability of the proposed method was shown in one detailed example of a verified TF regulatory module for pheromone response and filamentous growth in yeast.ConclusionIn this research, a novel method that mined out the potential TF regulatory modules which elucidate the functional redundancy observed among TFs is proposed. The extracted TF regulatory modules not only correlate the molecular mechanisms to the observed functional redundancy among TFs, but also show biological significance in inferring TF functional binding target genes. The results provide testable hypotheses for biologists to further design subsequent research and experiments.

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Detecting Cooperativity between Transcription Factors Based on Functional Coherence and Similarity of Their Target Gene Sets

Lai

2016

PLoS ONE

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In eukaryotic cells, transcriptional regulation of gene expression is usually achieved by cooperative transcription factors (TFs). Therefore, knowing cooperative TFs is the first step toward uncovering the molecular mechanisms of gene expression regulation. Many algorithms based on different rationales have been proposed to predict cooperative TF pairs in yeast. Although various types of rationales have been used in the existing algorithms, functional coherence is not yet used. This prompts us to develop a new algorithm based on functional coherence and similarity of the target gene sets to identify cooperative TF pairs in yeast. The proposed algorithm predicted 40 cooperative TF pairs. Among them, three (Pdc2-Thi2, Hot1-Msn1 and Leu3-Met28) are novel predictions, which have not been predicted by any existing algorithms. Strikingly, two (Pdc2-Thi2 and Hot1-Msn1) of the three novel predictions have been experimentally validated, demonstrating the power of the proposed algorithm. Moreover, we show that the predictions of the proposed algorithm are more biologically meaningful than the predictions of 17 existing algorithms under four evaluation indices. In summary, our study suggests that new algorithms based on novel rationales are worthy of developing for detecting previously unidentifiable cooperative TF pairs.

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CoopTFD: a repository for predicted yeast cooperative transcription factor pairs

Cited by 3 publications

References 34 publications

YGMD: a repository for yeast cooperative transcription factor sets and their target gene modules

YGMD: a repository for yeast cooperative transcription factor sets and their target gene modules

Transcription factor regulatory modules provide the molecular mechanisms for functional redundancy observed among transcription factors in yeast

Detecting Cooperativity between Transcription Factors Based on Functional Coherence and Similarity of Their Target Gene Sets

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