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

Fu-Jou Lai,Wei-Sheng Wu,Hong-Tsun Chang,Yueh-Min Huang

doi:10.1186/1752-0509-8-s4-s9

Abstract

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.

Highlights

Eukaryotic transcriptional regulation is known to be highly connected through the networks of cooperative transcription factors (TFs)
Performance comparison using four TF-based performance indices The four TF-based performance indices are developed based on the protein-protein interaction (PPI) partners overlap of a predicted cooperative TF pairs (PCTFPs), shortest path length of a PCTFP in the PPI network, the functional similarity of a PCTFP, and the overlap between a set of PCTFPs and a benchmarked set of 27 known cooperative TF pairs
In this study, we adopted/proposed eight performance indices, of which four are TF-based and four are TGbased, to evaluate and compare the prediction performance of 14 sets of cooperative TF pairs predicted with distinct algorithms

Summary

Introduction

Eukaryotic transcriptional regulation is known to be highly connected through the networks of cooperative transcription factors (TFs). In order to make precise prediction of cooperative TF pairs, researchers developed distinct algorithms that integrated diverse genome-wide datasets such as chromatin immuno-precipitation on chip (ChIP-chip) data, gene expression data, protein-protein interaction (PPI) data, TF knockout data, position weight matrix (PWM) data, TFBS data and protein complex data. In these genomewide datasets, ChIP-chip data were most commonly employed because they explicitly provide the binding targets of TFs. a TF’s binding targets with low binding affinity were subject to being discarded due to stringent P-value cutoff applied to the ChIP-chip data, while they might be functionally significant with the TF [5].

Methods

Results

Conclusion