<i>De novo</i> motif discovery facilitates identification of interactions between transcription factors in <i>Saccharomyces cerevisiae</i>

Chen, Mei-Ju May; Chou, Lih-Ching; Hsieh, Tsung-Ting; Lee, Ding‐Dar; Liu, Kaiwei; Yu, Chi-Yuan; Oyang, Yen-Jen; Tsai, Huai-Kuang; Chen, Chien‐Yu

doi:10.1093/bioinformatics/bts002

Cited by 15 publications

(26 citation statements)

References 34 publications

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“…There are thousands published ChIP-seq datasets, among which the MCF-7 cell line has a maximum of 40 distinct TF ChIP-seq datasets [25]. When Chen et al [8] proposed an algorithm to identify TF complexes using paired ChIP-seq data, there were only 780 ( C 2 40 ) potential TF complexes for scrutiny in MCF-7 cell lines. By contrast, there are 966 distinct vertebrate TF motifs in the TRANSFAC database (version 2013.2), resulting in 966 × 40 potential combinations for CST.…”

Section: Discussionmentioning

confidence: 99%

“…Chen et al predicted TF complexes and their target genes using yeast TF ChIP-chip data [8], but their method required paired ChIP-chip data: one assay to determine the binding sites of a primary TF and the other the binding sites of a partner TF. Therefore, we believe CST will lower the cost of using ChIP-seq for these purposes and be valuable to the community.…”

Section: Introductionmentioning

confidence: 99%

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Inferring condition-specific targets of human TF-TF complexes using ChIP-seq data

et al. 2017

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BackgroundTranscription factors (TFs) often interact with one another to form TF complexes that bind DNA and regulate gene expression. Many databases are created to describe known TF complexes identified by either mammalian two-hybrid experiments or data mining. Lately, a wealth of ChIP-seq data on human TFs under different experiment conditions are available, making it possible to investigate condition-specific (cell type and/or physiologic state) TF complexes and their target genes.ResultsHere, we developed a systematic pipeline to infer Condition-Specific Targets of human TF-TF complexes (called the CST pipeline) by integrating ChIP-seq data and TF motifs. In total, we predicted 2,392 TF complexes and 13,504 high-confidence or 127,994 low-confidence regulatory interactions amongst TF complexes and their target genes. We validated our predictions by (i) comparing predicted TF complexes to external TF complex databases, (ii) validating selected target genes of TF complexes using ChIP-qPCR and RT-PCR experiments, and (iii) analysing target genes of select TF complexes using gene ontology enrichment to demonstrate the accuracy of our work. Finally, the predicted results above were integrated and employed to construct a CST database.ConclusionsWe built up a methodology to construct the CST database, which contributes to the analysis of transcriptional regulation and the identification of novel TF-TF complex formation in a certain condition. This database also allows users to visualize condition-specific TF regulatory networks through a user-friendly web interface.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-016-3450-3) contains supplementary material, which is available to authorized users.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Inferring condition-specific targets of human TF-TF complexes using ChIP-seq data

et al. 2017

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“…The increase in number of regulatory TFs led to an increase in percentage of TFs that bound cooperatively to the promoter region (Yang et al, 2010;Chen et al, 2012), with ~30% of the TFs showing co-operative binding in genes of high-noise biological processes compared to ~20% of the TFs exhibiting co-operative binding in genes of low-noise processes (p=0.019; Fig. 2B; Fig.…”

Section: Genes In High-noise Processes Are Regulated By Higher Numbermentioning

confidence: 99%

Transcription factor binding process is the primary driver of noise in gene expression

Parab

Pal

Dhar

2020

Preprint

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SummaryCellular processes driven by coordinated actions of individual genes generate cellular phenotypes. Stochastic variations in these processes lead to phenotypic heterogeneity that often has important implications for antibiotic persistence, mutation penetrance, cancer growth and anti-cancer drug resistance. However, the architecture of noise in cellular processes has remained largely unexplored even though expression noise in individual genes have been widely studied. Here we quantify noise in biological processes in yeast and through an integrated quantitative model show that the number of regulating transcription factors and their binding dynamics are the primary drivers of noise. Specifically, binding dynamics arising from competition and cooperation among TFs for promoter binding can predict a large fraction of noise variation. Our work reveals a novel mechanism of noise regulation that arises out of the dynamic nature of gene regulation and is not dependent on specific transcription factor or specific promoter sequence.

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“…Numerous studies have begun to explore how two or multiple TFs cooperate to regulate genes and what the degree of the cooperativity of these TFs is. Several types of TF-TF interactions were observed [ 3 , 4 ], such as (i) two TFs forming a protein complex before binding together to a TF binding site (TFBS); (ii) two TFs forming a protein complex but using only one TF to bind to a TFBS; (iii) two TFs forming a protein complex before binding to their own TFBSs; (iv) two TFs forming a protein complex to regulate another TF that binds to a TFBS and (v) two TFs compete to bind to a TFBS.…”

Section: Introductionmentioning

confidence: 99%

“…Yang et al (2010) [ 19 ] integrated ChIP-chip data and TF knockout data to predict cooperative TF pairs by identifying the most statistically significant overlap of the target genes regulated by two TFs. Finally, Chen et al (2012) [ 4 ] utilized ChIP-chip data and developed a method to detect the interaction of a TF pair by exploring the degree of their shared target genes.…”

Section: Introductionmentioning

confidence: 99%

A comprehensive performance evaluation on the prediction results of existing cooperative transcription factors identification algorithms

et al. 2014

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BackgroundEukaryotic transcriptional regulation is known to be highly connected through the networks of cooperative transcription factors (TFs). Measuring the cooperativity of TFs is helpful for understanding the biological relevance of these TFs in regulating genes. The recent advances in computational techniques led to various predictions of cooperative TF pairs in yeast. As each algorithm integrated different data resources and was developed based on different rationales, it possessed its own merit and claimed outperforming others. However, the claim was prone to subjectivity because each algorithm compared with only a few other algorithms and only used a small set of performance indices for comparison. This motivated us to propose a series of indices to objectively evaluate the prediction performance of existing algorithms. And based on the proposed performance indices, we conducted a comprehensive performance evaluation.ResultsWe collected 14 sets of predicted cooperative TF pairs (PCTFPs) in yeast from 14 existing algorithms in the literature. Using the eight performance indices we adopted/proposed, the cooperativity of each PCTFP was measured and a ranking score according to the mean cooperativity of the set was given to each set of PCTFPs under evaluation for each performance index. It was seen that the ranking scores of a set of PCTFPs vary with different performance indices, implying that an algorithm used in predicting cooperative TF pairs is of strength somewhere but may be of weakness elsewhere. We finally made a comprehensive ranking for these 14 sets. The results showed that Wang J's study obtained the best performance evaluation on the prediction of cooperative TF pairs in yeast.ConclusionsIn this study, we adopted/proposed eight performance indices to make a comprehensive performance evaluation on the prediction results of 14 existing cooperative TFs identification algorithms. Most importantly, these proposed indices can be easily applied to measure the performance of new algorithms developed in the future, thus expedite progress in this research field.

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De novo motif discovery facilitates identification of interactions between transcription factors in Saccharomyces cerevisiae

Abstract: Supplementary data are available at Bioinformatics online.

Cited by 15 publications

References 34 publications

Inferring condition-specific targets of human TF-TF complexes using ChIP-seq data

Inferring condition-specific targets of human TF-TF complexes using ChIP-seq data

Transcription factor binding process is the primary driver of noise in gene expression

A comprehensive performance evaluation on the prediction results of existing cooperative transcription factors identification algorithms

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