Multiple non-collinear TF-map alignments of promoter regions

A Method for the Structure-Based, Genome-Wide Analysis of Bacterial Intergenic Sequences Identifies Shared Compositional and Functional Features

In this paper, we propose a computational strategy for performing genome-wide analyses of intergenic sequences in bacterial genomes. Following similar directions of a previous paper, where a method for genome-wide analysis of eucaryotic Intergenic sequences was proposed, here we developed a tool for implementing similar concepts in bacteria genomes. This allows us to (i) classify intergenic sequences into clusters, characterized by specific global structural features and (ii) draw possible relations with their functional features.

Genome-wide analysis of promoters: clustering by alignment and analysis of regular patterns

In this paper we perform a genome-wide analysis of H. sapiens promoters. To this aim, we developed and combined two mathematical methods that allow us to (i) classify promoters into groups characterized by specific global structural features, and (ii) recover, in full generality, any regular sequence in the different classes of promoters. One of the main findings of this analysis is that H. sapiens promoters can be classified into three main groups. Two of them are distinguished by the prevalence of weak or strong nucleotides and are characterized by short compositionally biased sequences, while the most frequent regular sequences in the third group are strongly correlated with transposons. Taking advantage of the generality of these mathematical procedures, we have compared the promoter database of H. sapiens with those of other species. We have found that the above-mentioned features characterize also the evolutionary content appearing in mammalian promoters, at variance with ancestral species in the phylogenetic tree, that exhibit a definitely lower level of differentiation among promoters.

Graph-based modeling of tandem repeats improves global multiple sequence alignment

Tandem repeats (TRs) are often present in proteins with crucial functions, responsible for resistance, pathogenicity and associated with infectious or neurodegenerative diseases. This motivates numerous studies of TRs and their evolution, requiring accurate multiple sequence alignment. TRs may be lost or inserted at any position of a TR region by replication slippage or recombination, but current methods assume fixed unit boundaries, and yet are of high complexity. We present a new global graph-based alignment method that does not restrict TR unit indels by unit boundaries. TR indels are modeled separately and penalized using the phylogeny-aware alignment algorithm. This ensures enhanced accuracy of reconstructed alignments, disentangling TRs and measuring indel events and rates in a biologically meaningful way. Our method detects not only duplication events but also all changes in TR regions owing to recombination, strand slippage and other events inserting or deleting TR units. We evaluate our method by simulation incorporating TR evolution, by either sampling TRs from a profile hidden Markov model or by mimicking strand slippage with duplications. The new method is illustrated on a family of type III effectors, a pathogenicity determinant in agriculturally important bacteria Ralstonia solanacearum. We show that TR indel rate variation contributes to the diversification of this protein family.

ReLA, a local alignment search tool for the identification of distal and proximal gene regulatory regions and their conserved transcription factor binding sites

Motivation: The prediction and annotation of the genomic regions involved in gene expression has been largely explored. Most of the energy has been devoted to the development of approaches that detect transcription start sites, leaving the identification of regulatory regions and their functional transcription factor binding sites (TFBSs) largely unexplored and with important quantitative and qualitative methodological gaps.

Results: We have developed ReLA (for REgulatory region Local Alignment tool), a unique tool optimized with the Smith–Waterman algorithm that allows local searches of conserved TFBS clusters and the detection of regulatory regions proximal to genes and enhancer regions. ReLA's performance shows specificities of 81 and 50% when tested on experimentally validated proximal regulatory regions and enhancers, respectively.

Availability: The source code of ReLA's is freely available and can be remotely used through our web server under http://www.bsc.es/cg/rela.

Contact:david.torrents@bsc.es

Supplementary information:Supplementary data are available at Bioinformatics online.

Use of ChIP-Seq data for the design of a multiple promoter-alignment method

We address the challenge of regulatory sequence alignment with a new method, Pro-Coffee, a multiple aligner specifically designed for homologous promoter regions. Pro-Coffee uses a dinucleotide substitution matrix estimated on alignments of functional binding sites from TRANSFAC. We designed a validation framework using several thousand families of orthologous promoters. This dataset was used to evaluate the accuracy for predicting true human orthologs among their paralogs. We found that whereas other methods achieve on average 73.5% accuracy, and 77.6% when trained on that same dataset, the figure goes up to 80.4% for Pro-Coffee. We then applied a novel validation procedure based on multi-species ChIP-seq data. Trained and untrained methods were tested for their capacity to correctly align experimentally detected binding sites. Whereas the average number of correctly aligned sites for two transcription factors is 284 for default methods and 316 for trained methods, Pro-Coffee achieves 331, 16.5% above the default average. We find a high correlation between a method's performance when classifying orthologs and its ability to correctly align proven binding sites. Not only has this interesting biological consequences, it also allows us to conclude that any method that is trained on the ortholog data set will result in functionally more informative alignments.

Studying the functional conservation of cis-regulatory modules and their transcriptional output

BACKGROUND

Cis-regulatory modules (CRMs) are distinct, genomic regions surrounding the target gene that can independently activate the promoter to drive transcription. The activation of a CRM is controlled by the binding of a certain combination of transcription factors (TFs). It would be of great benefit if the transcriptional output mediated by a specific CRM could be predicted. Of equal benefit would be identifying in silico a specific CRM as the driver of the expression in a specific tissue or situation. We extend a recently developed biochemical modeling approach to manage both prediction tasks. Given a set of TFs, their protein concentrations, and the positions and binding strengths of each of the TFs in a putative CRM, the model predicts the transcriptional output of the gene. Our approach predicts the location of the regulating CRM by using predicted TF binding sites in regions near the gene as input to the model and searching for the region that yields a predicted transcription rate most closely matching the known rate.

RESULTS

Here we show the ability of the model on the example of one of the CRMs regulating the eve gene, MSE2. A model trained on the MSE2 in D. melanogaster was applied to the surrounding sequence of the eve gene in seven other Drosophila species. The model successfully predicts the correct MSE2 location and output in six out of eight Drosophila species we examine.

CONCLUSION

The model is able to generalize from D. melanogaster to other Drosophila species and accurately predicts the location and transcriptional output of MSE2 in those species. However, we also show that the current model is not specific enough to function as a genome-wide CRM scanner, because it incorrectly predicts other genomic regions to be MSE2s.

BioMoby web services to support clustering of co-regulated genes based on similarity of promoter configurations

Here we present a computational protocol to analyze the promoter regions of a given set of co-expressed genes, and its implementation through the use of Web services technologies. This protocol aims to cluster a set of co-regulated genes in subsets of genes showing similar configurations of transcription factor binding sites. All the steps of this protocol have been developed as web services that are compliant with BioMoby specifications.

AVAILABILITY

{http://genome.imim.es/cgi-bin/moby/GeneClustering_DataSubmission.cgi}.

SUPPLEMENTARY INFORMATION

Supplementary data are available at {http://genome.imim.es/webservices/}