Identifying cooperative transcription factors in yeast using multiple data sources

Lai, Fu Jou; Jhu, Mei Huei; Chiu, Chia Chun; Huang, Yueh Min; Wu, Wei

doi:10.1186/1752-0509-8-s5-s2

Cited by 11 publications

(13 citation statements)

References 39 publications

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“…These networks are usually reverse engineered from large-scale transcriptomic samples and evidence of physical interactions (ARACNE, 27 WGCNA, 28 GENIE3, 29 LICORN 30 ). Our reverse engineering approach, Hybrid- learning co-operative regulation networks (h-LICORN), 30 , 31 combine a data mining technique and a numerical linear regression to effectively infer GRN (see Materials and Methods) and is original principally in terms of the incorporation into the model of the cooperativity between co-regulators, rendering it more relevant for the comprehension of complex phenotype that are likely to be regulated by several regulators rather than by a single one, as shown by us and others in the yeast S. cerevisiae , 30 , 32 as well as in human. 31 , 33 …”

Section: Introductionmentioning

confidence: 99%

Inference and interrogation of a coregulatory network in the context of lipid accumulation in Yarrowia lipolytica

et al. 2017

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Complex phenotypes, such as lipid accumulation, result from cooperativity between regulators and the integration of multiscale information. However, the elucidation of such regulatory programs by experimental approaches may be challenging, particularly in context-specific conditions. In particular, we know very little about the regulators of lipid accumulation in the oleaginous yeast of industrial interest Yarrowia lipolytica. This lack of knowledge limits the development of this yeast as an industrial platform, due to the time-consuming and costly laboratory efforts required to design strains with the desired phenotypes. In this study, we aimed to identify context-specific regulators and mechanisms, to guide explorations of the regulation of lipid accumulation in Y. lipolytica. Using gene regulatory network inference, and considering the expression of 6539 genes over 26 time points from GSE35447 for biolipid production and a list of 151 transcription factors, we reconstructed a gene regulatory network comprising 111 transcription factors, 4451 target genes and 17048 regulatory interactions (YL-GRN-1) supported by evidence of protein–protein interactions. This study, based on network interrogation and wet laboratory validation (a) highlights the relevance of our proposed measure, the transcription factors influence, for identifying phases corresponding to changes in physiological state without prior knowledge (b) suggests new potential regulators and drivers of lipid accumulation and (c) experimentally validates the impact of six of the nine regulators identified on lipid accumulation, with variations in lipid content from +43.2% to −31.2% on glucose or glycerol.

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Section: Introductionmentioning

confidence: 99%

Inference and interrogation of a coregulatory network in the context of lipid accumulation in Yarrowia lipolytica

et al. 2017

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“…The similarity of PPI partners between two TFs may suggest that they participate in the same regulatory mechanism. This rationale has been used in previous studies [ 15 , 16 ] to evaluate the biological plausibility of a PCTFP. The physical PPI data were downloaded from the BioGRID database [ 22 ].…”

Section: Methodsmentioning

confidence: 99%

“…Since a cooperative TF pair co-regulates the expression of a set of genes, they should have similar functions. Functional similarity has been used in several previous studies [ 10 , 15 , 16 ] to evaluate the biological plausibility of a PCTFP. The functional similarity score of a TF pair, which is calculated based on their GO semantic similarity, was retrieved from Yang et al's study [ 24 ].…”

Section: Methodsmentioning

confidence: 99%

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Properly defining the targets of a transcription factor significantly improves the computational identification of cooperative transcription factor pairs in yeast

Lai

2015

BMC Genomics

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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 p...

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“…gene expression data, ChIP-chip data, TF binding site motifs, protein-protein interaction data and TF knockout data), researchers continued developing advanced algorithms to predict cooperative TF pairs. Some algorithms only utilized ChIP-chip data [ 3 - 6 ] or gene expression data [ 7 ], and the others integrated multiple data sources [ 8 - 17 ].…”

Section: Introductionmentioning

confidence: 99%

PCTFPeval: a web tool for benchmarking newly developed algorithms for predicting cooperative transcription factor pairs in yeast

Lai

Chang

2015

BMC Bioinformatics

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BackgroundComputational identification of cooperative transcription factor (TF) pairs helps understand the combinatorial regulation of gene expression in eukaryotic cells. Many advanced algorithms have been proposed to predict cooperative TF pairs in yeast. However, it is still difficult to conduct a comprehensive and objective performance comparison of different algorithms because of lacking sufficient performance indices and adequate overall performance scores. To solve this problem, in our previous study (published in BMC Systems Biology 2014), we adopted/proposed eight performance indices and designed two overall performance scores to compare the performance of 14 existing algorithms for predicting cooperative TF pairs in yeast. Most importantly, our performance comparison framework can be applied to comprehensively and objectively evaluate the performance of a newly developed algorithm. However, to use our framework, researchers have to put a lot of effort to construct it first. To save researchers time and effort, here we develop a web tool to implement our performance comparison framework, featuring fast data processing, a comprehensive performance comparison and an easy-to-use web interface.ResultsThe developed tool is called PCTFPeval (Predicted Cooperative TF Pair evaluator), written in PHP and Python programming languages. The friendly web interface allows users to input a list of predicted cooperative TF pairs from their algorithm and select (i) the compared algorithms among the 15 existing algorithms, (ii) the performance indices among the eight existing indices, and (iii) the overall performance scores from two possible choices. The comprehensive performance comparison results are then generated in tens of seconds and shown as both bar charts and tables. The original comparison results of each compared algorithm and each selected performance index can be downloaded as text files for further analyses.ConclusionsAllowing users to select eight existing performance indices and 15 existing algorithms for comparison, our web tool benefits researchers who are eager to comprehensively and objectively evaluate the performance of their newly developed algorithm. Thus, our tool greatly expedites the progress in the research of computational identification of cooperative TF pairs.

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Identifying cooperative transcription factors in yeast using multiple data sources

Cited by 11 publications

References 39 publications

Inference and interrogation of a coregulatory network in the context of lipid accumulation in Yarrowia lipolytica

Inference and interrogation of a coregulatory network in the context of lipid accumulation in Yarrowia lipolytica

Properly defining the targets of a transcription factor significantly improves the computational identification of cooperative transcription factor pairs in yeast

PCTFPeval: a web tool for benchmarking newly developed algorithms for predicting cooperative transcription factor pairs in yeast

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