Mapping the complexity of transcription control in higher eukaryotes

Recent large analyses suggest the importance of combinatorial regulation by broadly expressed transcription factors rather than expression domains characterized by highly specific factors

Spatial preferences of microRNA targets in 3' untranslated regions

<p>Abstract</p> <p>Background</p> <p>MicroRNAs are an important class of regulatory RNAs which repress animal genes by preferentially interacting with complementary sequence motifs in the 3' untranslated region (UTR) of target mRNAs. Computational methods have been developed which can successfully predict which microRNA may target which mRNA on a genome-wide scale.</p> <p>Results</p> <p>We address how predicted target sites may be affected by alternative polyadenylation events changing the 3'UTR sequence. We find that two thirds of targeted genes have alternative 3'UTRs, with 40% of predicted target sites located in alternative UTR segments. We propose three classes based on whether the target sites fall within constitutive and/or alternative UTR segments, and examine the spatial distribution of predicted targets in alternative UTRs. In particular, there is a strong preference for targets to be located in close vicinity of the stop codon and the polyadenylation sites.</p> <p>Conclusion</p> <p>The transcript diversity seen in non-coding regions, as well as the relative location of miRNA target sites defined by it, has a potentially large impact on gene regulation by miRNAs and should be taken into account when defining, predicting or validating miRNA targets.</p

Phylogenetic simulation of promoter evolution: estimation and modeling of binding site turnover events and assessment of their impact on alignment tools

Phylogenetic simulation of promoter evolution were used to analyze functional site turnover in regulatory sequences

ssHMM: extracting intuitive sequence-structure motifs from high-throughput RNA-binding protein data

RNA-binding proteins (RBPs) play an important role in RNA post-transcriptional regulation and recognize target RNAs via sequence-structure motifs. The extent to which RNA structure influences protein binding in the presence or absence of a sequence motif is still poorly understood. Existing RNA motif finders either take the structure of the RNA only partially into account, or employ models which are not directly interpretable as sequence-structure motifs. We developed ssHMM, an RNA motif finder based on a hidden Markov model (HMM) and Gibbs sampling which fully captures the relationship between RNA sequence and secondary structure preference of a given RBP. Compared to previous methods which output separate logos for sequence and structure, it directly produces a combined sequence-structure motif when trained on a large set of sequences. ssHMM’s model is visualized intuitively as a graph and facilitates biological interpretation. ssHMM can be used to find novel bona fide sequence-structure motifs of uncharacterized RBPs, such as the one presented here for the YY1 protein. ssHMM reaches a high motif recovery rate on synthetic data, it recovers known RBP motifs from CLIP-Seq data, and scales linearly on the input size, being considerably faster than MEMERIS and RNAcontext on large datasets while being on par with GraphProt. It is freely available on Github and as a Docker image

Orthologous Transcription Factors in Bacteria Have Different Functions and Regulate Different Genes

Transcription factors (TFs) form large paralogous gene families and have complex evolutionary histories. Here, we ask whether putative orthologs of TFs, from bidirectional best BLAST hits (BBHs), are evolutionary orthologs with conserved functions. We show that BBHs of TFs from distantly related bacteria are usually not evolutionary orthologs. Furthermore, the false orthologs usually respond to different signals and regulate distinct pathways, while the few BBHs that are evolutionary orthologs do have conserved functions. To test the conservation of regulatory interactions, we analyze expression patterns. We find that regulatory relationships between TFs and their regulated genes are usually not conserved for BBHs in Escherichia coli K12 and Bacillus subtilis. Even in the much more closely related bacteria Vibrio cholerae and Shewanella oneidensis MR-1, predicting regulation from E. coli BBHs has high error rates. Using gene–regulon correlations, we identify genes whose expression pattern differs between E. coli and S. oneidensis. Using literature searches and sequence analysis, we show that these changes in expression patterns reflect changes in gene regulation, even for evolutionary orthologs. We conclude that the evolution of bacterial regulation should be analyzed with phylogenetic trees, rather than BBHs, and that bacterial regulatory networks evolve more rapidly than previously thought

Sustained-input switches for transcription factors and microRNAs are central building blocks of eukaryotic gene circuits

WaRSwap is a randomization algorithm that for the first time provides a practical network motif discovery method for large multi-layer networks, for example those that include transcription factors, microRNAs, and non-regulatory protein coding genes. The algorithm is applicable to systems with tens of thousands of genes, while accounting for critical aspects of biological networks, including self-loops, large hubs, and target rearrangements. We validate WaRSwap on a newly inferred regulatory network from Arabidopsis thaliana, and compare outcomes on published Drosophila and human networks. Specifically, sustained input switches are among the few over-represented circuits across this diverse set of eukaryotes.Keywords: gene regulation, transcription factor, microRNA, network moti

Evidence-ranked motif identification

A new computational method for the identification of regulatory motifs from large genomic datasets is presented her