Meta-analysis discovery of tissue-specific DNA sequence motifs from mammalian gene expression data

BACKGROUND: A key step in the regulation of gene expression is the sequence-specific binding of transcription factors (TFs) to their DNA recognition sites. However, elucidating TF binding site (TFBS) motifs in higher eukaryotes has been challenging, even when employing cross-species sequence conservation. We hypothesized that for human and mouse, many orthologous genes expressed in a similarly tissue-specific manner in both human and mouse gene expression data, are likely to be co-regulated by orthologous TFs that bind to DNA sequence motifs present within noncoding sequence conserved between these genomes. RESULTS: We performed automated motif searching and merging across four different motif finding algorithms, followed by filtering of the resulting motifs for those that contain blocks of information content. Applying this motif finding strategy to conserved noncoding regions surrounding co-expressed tissue-specific human genes allowed us to discover both previously known, and many novel candidate, regulatory DNA motifs in all 18 tissue-specific expression clusters that we examined. For previously known TFBS motifs, we observed that if a TF was expressed in the specified tissue of interest, then in most cases we identified a motif that matched its TRANSFAC motif; conversely, of all those discovered motifs that matched TRANSFAC motifs, most of the corresponding TF transcripts were expressed in the tissue(s) corresponding to the expression cluster for which the motif was found. CONCLUSION: Our results indicate that the integration of the results from multiple motif finding tools identifies and ranks highly more known and novel motifs than does the use of just one of these tools. In addition, we believe that our simultaneous enrichment strategies helped to identify likely human cis regulatory elements. A number of the discovered motifs may correspond to novel binding site motifs for as yet uncharacterized tissue-specific TFs. We expect this strategy to be useful for identifying motifs in other metazoan genomes

Identification of co-regulated candidate genes by promoter analysis.

EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Module Finder : a computational model for the identification of Cis regulatory modules

Thesis (S.M.)--Harvard-MIT Division of Health Sciences and Technology, 2005.Includes bibliographical references (leaves 55-57).Regulation of gene expression occurs largely through the binding of sequence- specific transcription factors (TFs) to genomic DNA binding sites (BSs). This thesis presents a rigorous scoring scheme, implemented as a C program termed "ModuleFinder", that evaluates the likelihood that a given genomic region is a cis regulatory module (CRM) for an input set of TFs according to its degree of: (1) homotypic site clustering; (2) heterotypic site clustering; and (3) evolutionary conservation across multiple genomes. Importantly, ModuleFinder obtains all parameters needed to appropriately weight the relative contributions of these sequence features directly from the input sequences and TFBS motifs, and does not need to first be trained. Using two previously described collections of experimentally verified CRMs in mammals as validation datasets, we show that ModuleFinder is able to identify CRMs with great sensitivity and specificity. We also evaluated ModuleFinder on a set of DNA binding site data for the human TFs Hepatocyte Nuclear Factor HNF1 [alpha], HNF4 [alpha] and HNF6 and compared its performance with logistic regression and neural network models.by Fangxue He.S.M

Predicting Gene Expression from Sequence: A Reexamination

Although much of the information regarding genes' expressions is encoded in the genome, deciphering such information has been very challenging. We reexamined Beer and Tavazoie's (BT) approach to predict mRNA expression patterns of 2,587 genes in Saccharomyces cerevisiae from the information in their respective promoter sequences. Instead of fitting complex Bayesian network models, we trained naïve Bayes classifiers using only the sequence-motif matching scores provided by BT. Our simple models correctly predict expression patterns for 79% of the genes, based on the same criterion and the same cross-validation (CV) procedure as BT, which compares favorably to the 73% accuracy of BT. The fact that our approach did not use position and orientation information of the predicted binding sites but achieved a higher prediction accuracy, motivated us to investigate a few biological predictions made by BT. We found that some of their predictions, especially those related to motif orientations and positions, are at best circumstantial. For example, the combinatorial rules suggested by BT for the PAC and RRPE motifs are not unique to the cluster of genes from which the predictive model was inferred, and there are simpler rules that are statistically more significant than BT's ones. We also show that CV procedure used by BT to estimate their method's prediction accuracy is inappropriate and may have overestimated the prediction accuracy by about 10%

A Systems Biology Approach to Transcription Factor Binding Site Prediction

The elucidation of mammalian transcriptional regulatory networks holds great promise for both basic and translational research and remains one the greatest challenges to systems biology. Recent reverse engineering methods deduce regulatory interactions from large-scale mRNA expression profiles and cross-species conserved regulatory regions in DNA. Technical challenges faced by these methods include distinguishing between direct and indirect interactions, associating transcription regulators with predicted transcription factor binding sites (TFBSs), identifying non-linearly conserved binding sites across species, and providing realistic accuracy estimates.We address these challenges by closely integrating proven methods for regulatory network reverse engineering from mRNA expression data, linearly and non-linearly conserved regulatory region discovery, and TFBS evaluation and discovery. Using an extensive test set of high-likelihood interactions, which we collected in order to provide realistic prediction-accuracy estimates, we show that a careful integration of these methods leads to significant improvements in prediction accuracy. To verify our methods, we biochemically validated TFBS predictions made for both transcription factors (TFs) and co-factors; we validated binding site predictions made using a known E2F1 DNA-binding motif on E2F1 predicted promoter targets, known E2F1 and JUND motifs on JUND predicted promoter targets, and a de novo discovered motif for BCL6 on BCL6 predicted promoter targets. Finally, to demonstrate accuracy of prediction using an external dataset, we showed that sites matching predicted motifs for ZNF263 are significantly enriched in recent ZNF263 ChIP-seq data.Using an integrative framework, we were able to address technical challenges faced by state of the art network reverse engineering methods, leading to significant improvement in direct-interaction detection and TFBS-discovery accuracy. We estimated the accuracy of our framework on a human B-cell specific test set, which may help guide future methodological development

Computational analysis of transcriptional regulation in metazoans

This HDR thesis presents my work on transcriptional regulation in metazoans (animals). As a computational biologist, my research activities cover both the development of new bioinformatics tools, and contributions to a better understanding of biological questions. The first part focuses on transcription factors, with a study of the evolution of Hox and ParaHox gene families across meta- zoans, for which I developed HoxPred, a bioinformatics tool to automatically classify these genes into their groups of homology. Transcription factors regulate their target genes by binding to short cis-regulatory elements in DNA. The second part of this thesis introduces the prediction of these cis-regulatory elements in genomic sequences, and my contributions to the development of user- friendly computational tools (RSAT software suite and TRAP). The third part covers the detection of these cis-regulatory elements using high-throughput sequencing experiments such as ChIP-seq or ChIP-exo. The bioinformatics developments include reusable pipelines to process these datasets, and novel motif analysis tools adapted to these large datasets (RSAT peak-motifs and ExoProfiler). As all these approaches are generic, I naturally apply them to diverse biological questions, in close collaboration with experimental groups. In particular, this third part presents the studies uncover- ing new DNA sequences that are driving or preventing the binding of the glucocorticoid receptor. Finally, my research perspectives are introduced, especially regarding further developments within the RSAT suite enabling cross-species conservation analyses, and new collaborations with exper- imental teams, notably to tackle the epigenomic remodelling during osteoporosis.Cette thèse d’HDR présente mes travaux concernant la régulation transcriptionelle chez les métazoaires (animaux). En tant que biologiste computationelle, mes activités de recherche portent sur le développement de nouveaux outils bioinformatiques, et contribuent à une meilleure compréhension de questions biologiques. La première partie concerne les facteurs de transcriptions, avec une étude de l’évolution des familles de gènes Hox et ParaHox chez les métazoaires. Pour cela, j’ai développé HoxPred, un outil bioinformatique qui classe automatiquement ces gènes dans leur groupe d’homologie. Les facteurs de transcription régulent leurs gènes cibles en se fixant à l’ADN sur des petites régions cis-régulatrices. La seconde partie de cette thèse introduit la prédiction de ces éléments cis-régulateurs au sein de séquences génomiques, et présente mes contributions au développement d’outils accessibles aux non-spécialistes (la suite RSAT et TRAP). La troisième partie couvre la détection de ces éléments cis-régulateurs grâce aux expériences basées sur le séquençage à haut débit comme le ChIP-seq ou le ChIP-exo. Les développements bioinformatiques incluent des pipelines réutilisables pour analyser ces jeux de données, ainsi que de nouveaux outils d’analyse de motifs adaptés à ces grands jeux de données (RSAT peak-motifs et ExoProfiler). Comme ces approches sont génériques, je les applique naturellement à des questions biologiques diverses, en étroite collaboration avec des groupes expérimentaux. En particulier, cette troisième partie présente les études qui ont permis de mettre en évidence de nouvelles séquences d’ADN qui favorisent ou empêchent la fixation du récepteur aux glucocorticoides. Enfin, mes perspectives de recherche sont présentées, plus particulièrement concernant les nouveaux développements au sein de la suite RSAT pour permettre des analyses basées sur la conservation inter-espèces, mais aussi de nouvelles collaborations avec des équipes expérimentales, notamment pour éudier le remodelage épigénomique au cours de l’ostéoporose

Fine-Tuning Enhancer Models to Predict Transcriptional Targets across Multiple Genomes

Networks of regulatory relations between transcription factors (TF) and their target genes (TG)- implemented through TF binding sites (TFBS)- are key features of biology. An idealized approach to solving such networks consists of starting from a consensus TFBS or a position weight matrix (PWM) to generate a high accuracy list of candidate TGs for biological validation. Developing and evaluating such approaches remains a formidable challenge in regulatory bioinformatics. We perform a benchmark study on 34 Drosophila TFs to assess existing TFBS and cis-regulatory module (CRM) detection methods, with a strong focus on the use of multiple genomes. Particularly, for CRM-modelling we investigate the addition of orthologous sites to a known PWM to construct phyloPWMs and we assess the added value of phylogenentic footprinting to predict contextual motifs around known TFBSs. For CRM-prediction, we compare motif conservation with network-level conservation approaches across multiple genomes. Choosing the optimal training and scoring strategies strongly enhances the performance of TG prediction for more than half of the tested TFs. Finally, we analyse a 35th TF, namely Eyeless, and find a significant overlap between predicted TGs and candidate TGs identified by microarray expression studies. In summary we identify several ways to optimize TF-specific TG predictions, some of which can be applied to all TFs, and others that can be applied only to particular TFs. The ability to model known TF-TG relations, together with the use of multiple genomes, results in a significant step forward in solving the architecture of gene regulatory networks