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

Abstract

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...

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

“…Here we consider the top 40 TF pairs as the PCTFPs from the proposed algorithm. Considering the top 40 TF pairs is reasonable because the number of the PCTFPs from most (>10) existing algorithms [ 4 , 6 – 10 , 12 – 14 , 18 , 19 ] falls between 13 and 60 (see Table 1 ).…”

“…Another five algorithms [ 5 , 11 , 14 , 18 , 20 ] assume that for a cooperative TF pair, their binding sites have shorter distance, are more co-depleted of nucleosomes or co-occur more often than expected by chance. Some other algorithms [ 15 , 16 , 18 , 19 ] assume that the observed number of the shared target genes of a cooperative TF pair is higher than random expectation (see Table 1 for details). Apart from the above mentioned algorithms which aim to identify cooperative TF pairs in yeast, several advanced algorithms have been proposed to identify cooperative TF pairs in human [ 25 – 27 ].…”

“…Second, the proposed algorithm assumes that two cooperative TFs have similar target gene sets. Since the biological role of two cooperative TFs is to co-regulate the expression of a set of genes, they should have a significant number of shared target genes [ 5 , 11 , 15 , 16 , 18 , 19 ]. In other words, the target gene sets of two cooperative TFs should be similar to each other.…”

GIW and InCoB, two premier bioinformatics conferences in Asia with a combined 40 years of history

“…Most existing cooperative TF pairs prediction algorithms are overly reliant on improved cooperative measures. Wu and Lai [ 28 ] developed an algorithm that considers integrated yeast TF binding and perturbation data and outperformed twelve other algorithms. Interestingly, their benchmarking results also indicate that the process of defining biologically plausible targets of a TF might be more important than optimizing cooperative measures.…”

YCRD: Yeast Combinatorial Regulation Database

“…In YCRD, we collected more than 2500 cooperative TF pairs predicted by 17 existing algorithms in the literature [ 5 – 21 ]. As far as we know, this is the most comprehensive collection of predicted cooperative TF pairs in yeast (see Table 1 for details).…”