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Computational identification of cooperative transcription factor pairs

Four simple algorithms, which aim 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. Each algorithms 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 outperforms 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.

Motif-PIE

An approach to understand interactions between TFs. Motif-PIE has been tested using Saccharomyces cerevisiae as a model system. Instead of gene expression profiles, we focus on the sequence motifs in the upstream promoters. The strength of this algorithm lies in the fact that it is sequence-based; it can be applied to genes without expression data or previously determined binding motifs. By taking groups of genes whose upstream sequences are known to be bound by two TFs, we made ab initio predictions of their corresponding TF binding sites and examined the relationship between these two sites on the promoter sequences. The sequence relationships between the binding motifs were examined in terms of preferences in distance and orientation, reflecting possible spatial relationships between TFs. We further analyzed these predicted relationships using gene expression data and found that they are dynamic and condition-dependent.

simTFBS

A method that incorporates de novo motif discovery in the procedures of analyzing ChIP-chip data. simTFBS employs the discovered motifs to refine the lists of target genes, which further improve the accuracy of predicting interactions between TFs by estimating the degree of target overlap between two TFs more correctly. The evaluation conducted in this study reveals that the proposed method, simTFBS, outperforms three naïve methods and two recent studies. The predicted TF–TF interactions are shown to improve the modularity of the currently annotated interacting network which might be still largely incomplete.

Identification of transcription factor cooperativity via stochastic system model

A measurement of transcription factor cooperativity developed according to the regulatory abilities of cooperative transcription factor pairs and the number of their occurrences. Transcription factor cooperativity is based on a stochastic dynamic model. Our method is employed to the yeast cell cycle and reveals successfully many cooperative transcription factor pairs confirmed by previous experiments, e.g. Swi4-Swi6 in G1/S phase and Ndd1-Fkh2 in G2/M phase. Other transcription factor pairs with potential cooperativity mentioned in our results can provide new directions for future experiments. Finally, a cooperative transcription factor network of cell cycle is constructed from significant cooperative transcription factor pairs.

AdaFuzzy

A further enhanced machine-learning (adaptive fuzzy) approach, to infer transcriptional interactions (Tis), which incorporates DNA sequence, ChIP-chip and microarray data. AdaFuzzy proposes a robust position weight matrix and a feature vector. Furthermore, potential TF binding sites in upstream sequences of a specific target gene are identified by an adaptive neuro-fuzzy inference system (ANFIS) using sequence data. ChIP-chip data confirms that TIs do indeed occur under specific experimental conditions. In addition, microarray data is used to classify predicted TIs into activator-target or repressor-target relations via a weighted regression. After potential TIs are identified, AdaFuzzy also classifies their types of promoter architectures to provide insights into the organization of transcriptional regulatory interactions.

MOFA / MOdule Finding Algorithm

An algorithm developed to reconstruct transcriptional regulatory modules (TRMs) of the yeast cell cycle by integrating gene expression data and ChIP-chip data. MOFA identified 87 TRMs, which together contain 336 distinct genes regulated by 40 transcription factors (TFs). Many of these TRMs are in agreement with previous studies. Our analysis shows that different combinations of a fairly small number of TFs are responsible for regulating a large number of genes involved in different cell cycle phases and that there may exist crosstalk between the cell cycle and other cellular processes. MOFA is capable of finding many novel TF-target gene relationships and can determine whether a TF is an activator or/and a repressor. Finally, MOFA refines some clusters proposed by previous studies and provides a better understanding of how the complex expression program of the cell cycle is regulated.

CoopTFD / Cooperative Transcription Factors Database

A repository for 2622 yeast cooperative transcription factors pairs predicted by 17 existing algorithms. CoopTFD provides five types of validation information to help users judge the biological plausibility of a specific predicted cooperative transcription factor pair (PCTFP): the algorithms which predict this PCTFP, the publications which experimentally show that this PCTFP has physical or genetic interactions, the publications which experimentally study the biological roles of both TFs of this PCTFP, the common Gene Ontology terms of this PCTFP, the common target genes of this PCTFP.

LICORN / Learning co-operative regulation networks

Obsolete
A data mining system for inferring transcriptional regulation relationships from RNA expression values. LICORN is particularly suitable for the detection of cooperative transcriptional regulation. We model regulatory relationships as labelled two-layer gene regulatory networks, and describe a method for the efficient learning of these bipartite networks from discretized expression data sets. We also evaluate the statistical significance of such inferred networks and validate our methods on two public yeast expression data sets.