Evidence map›Paper›PMID 31881824›Full record

ArticleBMC bioinformatics2019

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

Tzu-Hsien Yang

Abstract read
In one paragraph

Article in BMC bioinformatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

Who cites it

11 citing papers in PubMed.

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  5. regCNN: identifyingComputational and structural biotechnology journal · 2022
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4 · The record

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5 · Who and what money

Authors and funding

1 author.

Tzu-Hsien YangDepartment of Information Management, National University of Kaohsiung, 700, Kaohsiung University Rd, Kaohsiung, 81148, Taiwan. thyangza1025@nuk.edu.tw.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

Gene Expression Regulation, FungalGene Regulatory NetworksAlgorithmsModels, BiologicalProtein BindingProtein Interaction MapsRNA, MessengerSaccharomyces cerevisiaeTranscription FactorsRNA, MessengerTranscription FactorsTF functional redundancyTF regulatory moduleTranscription regulation

Identifiers

PMID31881824
PMCPMC6933673

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.