Computational framework for the prediction of transcription factor binding sites by multiple data integration

Control of gene expression is essential to the establishment and maintenance of all cell types, and its dysregulation is involved in pathogenesis of several diseases. Accurate computational predictions of transcription factor regulation may thus help in understanding complex diseases, including mental disorders in which dysregulation of neural gene expression is thought to play a key role. However, biological mechanisms underlying the regulation of gene expression are not completely understood, and predictions via bioinformatics tools are typically poorly specific. We developed a bioinformatics workflow for the prediction of transcription factor binding sites from several independent datasets. We show the advantages of integrating information based on evolutionary conservation and gene expression, when tackling the problem of binding site prediction. Consistent results were obtained on a large simulated dataset consisting of 13050 in silico promoter sequences, on a set of 161 human gene promoters for which binding sites are known, and on a smaller set of promoters of Myc target genes. Our computational framework for binding site prediction can integrate multiple sources of data, and its performance was tested on different datasets. Our results show that integrating information from multiple data sources, such as genomic sequence of genes' promoters, conservation over multiple species, and gene expression data, indeed improves the accuracy of computational predictions

Inferring Transcriptional Interactions by the Optimal Integration of ChIP-chip and Knock-out Data

How to combine heterogeneous data sources for reliable prediction of transcriptional regulation is a challenge. Here we present an easy but powerful method to integrate Chromatin immunoprecipitation (ChIP)-chip and knock-out data. Since these two types of data provide complementary (physical and functional) information about transcription, the method combining them is expected to achieve high detection rates and very low false positive rates. We try to seek the optimal integration of these two data using hyper-geometric distribution. We evaluate our method on yeast data and compare our predictions with YEASTRACT, high-quality ChIP-chip data, and literature. The results show that even using low-quality ChIP-chip data, our method uncovers more relations than those inferred before from high-quality data. Furthermore our method achieves a low false positive rate. We find experimental and computational evidence in literature for most transcription factor (TF)-gene relations uncovered by our method

Comprehensive reanalysis of transcription factor knockout expression data in Saccharomyces cerevisiae reveals many new targets

Transcription factor (TF) perturbation experiments give valuable insights into gene regulation. Genome-scale evidence from microarray measurements may be used to identify regulatory interactions between TFs and targets. Recently, Hu and colleagues published a comprehensive study covering 269 TF knockout mutants for the yeast Saccharomyces cerevisiae. However, the information that can be extracted from this valuable dataset is limited by the method employed to process the microarray data. Here, we present a reanalysis of the original data using improved statistical techniques freely available from the BioConductor project. We identify over 100 000 differentially expressed genes—nine times the total reported by Hu et al. We validate the biological significance of these genes by assessing their functions, the occurrence of upstream TF-binding sites, and the prevalence of protein–protein interactions. The reanalysed dataset outperforms the original across all measures, indicating that we have uncovered a vastly expanded list of relevant targets. In summary, this work presents a high-quality reanalysis that maximizes the information contained in the Hu et al. compendium. The dataset is available from ArrayExpress (accession: E-MTAB-109) and it will be invaluable to any scientist interested in the yeast transcriptional regulatory system

A Linear Model of Genetic Transcription Regulation that Combines Microarray and Genome Sequence Data

The thesis proposes a novel method for the analysis of microarray data based on fitting a specific linear model that combines microarray data with DNA sequence information. The model is both descriptive and predictive: its coefficients provide insight into the structure of the genetic regulatory networks, and its predictive performance may be used to find a set of genes that play important role in transcription regulation (transcription factors). An efficient algorithm is proposed for calculating the least-squares fit for the parameters of the model. The proposed method is tested on a synthetic dataset and the results indicate that the approach is capable of detecting interesting relations in the data