MotEvo: integrated Bayesian probabilistic methods for inferring regulatory sites and motifs on multiple alignments of DNA sequences

Motivation: Probabilistic approaches for inferring transcription factor binding sites (TFBSs) and regulatory motifs from DNA sequences have been developed for over two decades. Previous work has shown that prediction accuracy can be significantly improved by incorporating features such as the competition of multiple transcription factors (TFs) for binding to nearby sites, the tendency of TFBSs for co-regulated TFs to cluster and form cis-regulatory modules and explicit evolutionary modeling of conservation of TFBSs across orthologous sequences. However, currently available tools only incorporate some of these features, and significant methodological hurdles hampered their synthesis into a single consistent probabilistic framework. Results: We present MotEvo, a integrated suite of Bayesian probabilistic methods for the prediction of TFBSs and inference of regulatory motifs from multiple alignments of phylogenetically related DNA sequences, which incorporates all features just mentioned. In addition, MotEvo incorporates a novel model for detecting unknown functional elements that are under evolutionary constraint, and a new robust model for treating gain and loss of TFBSs along a phylogeny. Rigorous benchmarking tests on ChIP-seq datasets show that MotEvo's novel features significantly improve the accuracy of TFBS prediction, motif inference and enhancer prediction. Availability: Source code, a user manual and files with several example applications are available at www.swissregulon.unibas.ch. Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics onlin

SwissRegulon, a database of genome-wide annotations of regulatory sites: recent updates

Identification of genomic regulatory elements is essential for understanding the dynamics of cellular processes. This task has been substantially facilitated by the availability of genome sequences for many species and high-throughput data of transcripts and transcription factor (TF) binding. However, rigorous computational methods are necessary to derive accurate genome-wide annotations of regulatory sites from such data. SwissRegulon (http://swissregulon.unibas.ch) is a database containing genome-wide annotations of regulatory motifs, promoters and TF binding sites (TFBSs) in promoter regions across model organisms. Its binding site predictions were obtained with rigorous Bayesian probabilistic methods that operate on orthologous regions from related genomes, and use explicit evolutionary models to assess the evidence of purifying selection on each site. New in the current version of SwissRegulon is a curated collection of 190 mammalian regulatory motifs associated with ∼340 TFs, and TFBS annotations across a curated set of ∼35 000 promoters in both human and mouse. Predictions of TFBSs for Saccharomyces cerevisiae have also been significantly extended and now cover 158 of yeast's ∼180 TFs. All data are accessible through both an easily navigable genome browser with search functions, and as flat files that can be downloaded for further analysi

Embryonic stem cell-specific microRNAs contribute to pluripotency by inhibiting regulators of multiple differentiation pathways

The findings that microRNAs (miRNAs) are essential for early development in many species and that embryonic miRNAs can reprogram somatic cells into induced pluripotent stem cells suggest that these miRNAs act directly on transcriptional and chromatin regulators of pluripotency. To elucidate the transcription regulatory networks immediately downstream of embryonic miRNAs, we extended the motif activity response analysis approach that infers the regulatory impact of both transcription factors (TFs) and miRNAs from genome-wide expression states. Applying this approach to multiple experimental data sets generated from mouse embryonic stem cells (ESCs) that did or did not express miRNAs of the ESC-specific miR-290-295 cluster, we identified multiple TFs that are direct miRNA targets, some of which are known to be active during cell differentiation. Our results provide new insights into the transcription regulatory network downstream of ESC-specific miRNAs, indicating that these miRNAs act on cell cycle and chromatin regulators at several levels and downregulate TFs that are involved in the innate immune respons

Information content based model for the topological properties of the gene regulatory network of Escherichia coli

Gene regulatory networks (GRN) are being studied with increasingly precise quantitative tools and can provide a testing ground for ideas regarding the emergence and evolution of complex biological networks. We analyze the global statistical properties of the transcriptional regulatory network of the prokaryote Escherichia coli, identifying each operon with a node of the network. We propose a null model for this network using the content-based approach applied earlier to the eukaryote Saccharomyces cerevisiae. (Balcan et al., 2007) Random sequences that represent promoter regions and binding sequences are associated with the nodes. The length distributions of these sequences are extracted from the relevant databases. The network is constructed by testing for the occurrence of binding sequences within the promoter regions. The ensemble of emergent networks yields an exponentially decaying in-degree distribution and a putative power law dependence for the out-degree distribution with a flat tail, in agreement with the data. The clustering coefficient, degree-degree correlation, rich club coefficient and k-core visualization all agree qualitatively with the empirical network to an extent not yet achieved by any other computational model, to our knowledge. The significant statistical differences can point the way to further research into non-adaptive and adaptive processes in the evolution of the E. coli GRN.Comment: 58 pages, 3 tables, 22 figures. In press, Journal of Theoretical Biology (2009)

SwissRegulon: a database of genome-wide annotations of regulatory sites

SwissRegulon () is a database containing genome-wide annotations of regulatory sites in the intergenic regions of genomes. The regulatory site annotations are produced using a number of recently developed algorithms that operate on multiple alignments of orthologous intergenic regions from related genomes in combination with, whenever available, known sites from the literature, and ChIP-on-chip binding data. Currently SwissRegulon contains annotations for yeast and 17 prokaryotic genomes. The database provides information about the sequence, location, orientation, posterior probability and, whenever available, binding factor of each annotated site. To enable easy viewing of the regulatory site annotations in the context of other features annotated on the genomes, the sites are displayed using the GBrowse genome browser interface and can be queried based on any annotated genomic feature. The database can also be queried for regulons, i.e. sites bound by a common factor

Formation of regulatory modules by local sequence duplication

Turnover of regulatory sequence and function is an important part of molecular evolution. But what are the modes of sequence evolution leading to rapid formation and loss of regulatory sites? Here, we show that a large fraction of neighboring transcription factor binding sites in the fly genome have formed from a common sequence origin by local duplications. This mode of evolution is found to produce regulatory information: duplications can seed new sites in the neighborhood of existing sites. Duplicate seeds evolve subsequently by point mutations, often towards binding a different factor than their ancestral neighbor sites. These results are based on a statistical analysis of 346 cis-regulatory modules in the Drosophila melanogaster genome, and a comparison set of intergenic regulatory sequence in Saccharomyces cerevisiae. In fly regulatory modules, pairs of binding sites show significantly enhanced sequence similarity up to distances of about 50 bp. We analyze these data in terms of an evolutionary model with two distinct modes of site formation: (i) evolution from independent sequence origin and (ii) divergent evolution following duplication of a common ancestor sequence. Our results suggest that pervasive formation of binding sites by local sequence duplications distinguishes the complex regulatory architecture of higher eukaryotes from the simpler architecture of unicellular organisms

Temporal and sequential transcriptional dynamics define lineage shifts in corticogenesis

The cerebral cortex contains billions of neurons, and their disorganization or misspecification leads to neurodevelopmental disorders. Understanding how the plethora of projection neuron subtypes are generated by cortical neural stem cells (NSCs) is a major challenge. Here, we focused on elucidating the transcriptional landscape of murine embryonic NSCs, basal progenitors (BPs), and newborn neurons (NBNs) throughout cortical development. We uncover dynamic shifts in transcriptional space over time and heterogeneity within each progenitor population. We identified signature hallmarks of NSC, BP, and NBN clusters and predict active transcriptional nodes and networks that contribute to neural fate specification. We find that the expression of receptors, ligands, and downstream pathway components is highly dynamic over time and throughout the lineage implying differential responsiveness to signals. Thus, we provide an expansive compendium of gene expression during cortical development that will be an invaluable resource for studying neural developmental processes and neurodevelopmental disorders