YTRP: a repository for yeast transcriptional regulatory pathways

Yang, Tzu-Hsien; Wang, Chung Ching; Wang, Yu Chao; Wu, Wei

doi:10.1093/database/bau014

Cited by 20 publications

(18 citation statements)

References 23 publications

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“…First, yeast is the only organism which has more than 206000 TF-gene documented regulation data available. The useful TF-gene documented regulation data [ 29 ], which provide the experimentally validated regulatory relationships between TFs and genes, are collected from more than 1300 published papers by the team of the YEASTRACT database [ 18 ]. Second, yeast is the only organism that is tested by more than 10 existing algorithms.…”

Section: Discussionmentioning

confidence: 99%

Identifying cooperative transcription factors in yeast using multiple data sources

et al. 2014

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BackgroundTranscriptional regulation of gene expression is usually accomplished by multiple interactive transcription factors (TFs). Therefore, it is crucial to understand the precise cooperative interactions among TFs. Various kinds of experimental data including ChIP-chip, TF binding site (TFBS), gene expression, TF knockout and protein-protein interaction data have been used to identify cooperative TF pairs in existing methods. The nucleosome occupancy data is not yet used for this research topic despite that several researches have revealed the association between nucleosomes and TFBSs.ResultsIn this study, we developed a novel method to infer the cooperativity between two TFs by integrating the TF-gene documented regulation, TFBS and nucleosome occupancy data. TF-gene documented regulation and TFBS data were used to determine the target genes of a TF, and the genome-wide nucleosome occupancy data was used to assess the nucleosome occupancy on TFBSs. Our method identifies cooperative TF pairs based on two biologically plausible assumptions. If two TFs cooperate, then (i) they should have a significantly higher number of common target genes than random expectation and (ii) their binding sites (in the promoters of their common target genes) should tend to be co-depleted of nucleosomes in order to make these binding sites simultaneously accessible to TF binding. Each TF pair is given a cooperativity score by our method. The higher the score is, the more likely a TF pair has cooperativity. Finally, a list of 27 cooperative TF pairs has been predicted by our method. Among these 27 TF pairs, 19 pairs are also predicted by existing methods. The other 8 pairs are novel cooperative TF pairs predicted by our method. The biological relevance of these 8 novel cooperative TF pairs is justified by the existence of protein-protein interactions and co-annotation in the same MIPS functional categories. Moreover, we adopted three performance indices to compare our predictions with 11 existing methods' predictions. We show that our method performs better than these 11 existing methods in identifying cooperative TF pairs in yeast. Finally, the cooperative TF network constructed from the 27 predicted cooperative TF pairs shows that our method has the power to find cooperative TF pairs of different biological processes.ConclusionOur method is effective in identifying cooperative TF pairs in yeast. Many of our predictions are validated by the literature, and our method outperforms 11 existing methods. We believe that our study will help biologists to understand the mechanisms of transcriptional regulation in eukaryotic cells.

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Section: Discussionmentioning

confidence: 99%

Identifying cooperative transcription factors in yeast using multiple data sources

et al. 2014

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“…To overcome this obstacle, a modified approximation algorithm based on biological constraints was devised. In cells, the metabolism pathways and modules are usually energy conserved [8, 52]. This means that cells prefer to having fewer participating proteins if available.…”

Section: Datasets and Methodsmentioning

confidence: 99%

“…Current genome-wide experimental methods for understanding the behaviors of how TFs control cellular gene expression can be divided into two categories [8, 9]. The first type of experiment is based on the chromatin immunoprecipitation (ChIP) techniques.…”

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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“…Transitive regulations can provide some evidence for time delays since they may result from the delays in gene regulation. Yang et al [153] provided yeast transcriptional regulatory pathways (YTRP) database, which recorded the possible transitive regulations. However, these YTRP edges are only predicted transitive regulation from shortest path method, but not validated by laboratory experiments.…”

Section: Performance With Various Priorsmentioning

confidence: 99%

“…A cell cycle is comprised of four phases: (i) Gap 1 (G1) is the checkpoint to ensure that it is ready for division, (ii) Synthesis (S Phase) involves DNA replication, (iii) Gap 2 (G2) is another checkpoint to ensure that the cell is ready to enter the next phase, and (iv) Mitotic (M Phase) refers to cell division. Two yeast cell cycle transcriptional regulatory networks (Net4 and Net5) were randomly chosen from YEASTRACT database[153,135] as benchmark networks for experiments.To show how well the proposed functional similarity measure, FS-GOE score, is able to recover benchmark networks, we also chose the integrated functional association score from STRING database ( http://string-db.org/) as a comparison since it quantitatively integrates multiple data sources including genome context, gene expression and literature, for generating functional association network(FAN)…”

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confidence: 99%

Building and validating gene regulatory networks (GRN) with delays from multiple data sources

Liu¹

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I would like to express my sincere appreciation to my supervisors Prof. Jagath C. Rajapakse and Dr. Kuiyu Chang. Under their supervision, I have learned not only cutting-edge knowledge on data mining and bioinformatics, but also an inspiring and down-to-earth working attitude. I am delighted to have the research support from Singapore's Ministry of Education Academic Research Tier 2 fund MOE2010-T2-1-056 and Tier 2 grant of ARC9/11. I would also like to thank Bioinformatics Research Center (BIRC) and BIRC group members that make the years I spent at BIRC a great experience. I would also like to acknowledge my colleagues at Nanyang Technological University for their friendship and insightful discussions. They include Iti Chaturvedi, Piyushkumar

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YTRP: a repository for yeast transcriptional regulatory pathways

Cited by 20 publications

References 23 publications

Identifying cooperative transcription factors in yeast using multiple data sources

Identifying cooperative transcription factors in yeast using multiple data sources

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

Building and validating gene regulatory networks (GRN) with delays from multiple data sources

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