A computational pipeline for high- throughput discovery of cis-regulatory noncoding RNA in prokaryotes

Noncoding RNAs (ncRNAs) are important functional RNAs that do not code for proteins. We present a highly efficient computational pipeline for discovering cis-regulatory ncRNA motifs de novo. The pipeline differs from previous methods in that it is structure-oriented, does not require a multiple-sequence alignment as input, and is capable of detecting RNA motifs with low sequence conservation. We also integrate RNA motif prediction with RNA homolog search, which improves the quality of the RNA motifs significantly. Here, we report the results of applying this pipeline to Firmicute bacteria. Our top-ranking motifs include most known Firmicute elements found in the RNA family database (Rfam). Comparing our motif models with Rfam's hand-curated motif models, we achieve high accuracy in both membership prediction and base-pair–level secondary structure prediction (at least 75% average sensitivity and specificity on both tasks). Of the ncRNA candidates not in Rfam, we find compelling evidence that some of them are functional, and analyze several potential ribosomal protein leaders in depth.     

Synergy: A Web Resource for Exploring Gene Regulation in Synechocystis sp. PCC6803

Despite being a highly studied model organism, most genes of the cyanobacterium Synechocystis sp. PCC 6803 encode proteins with completely unknown function. To facilitate studies of gene regulation in Synechocystis, we have developed Synergy (http://synergy.plantgenie.org), a web application integrating co-expression networks and regulatory motif analysis. Co-expression networks were inferred from publicly available microarray experiments, while regulatory motifs were identified using a phylogenetic footprinting approach. Automatically discovered motifs were shown to be enriched in the network neighborhoods of regulatory proteins much more often than in the neighborhoods of non-regulatory genes, showing that the data provide a sound starting point for studying gene regulation in Synechocystis. Concordantly, we provide several case studies demonstrating that Synergy can be used to find biologically relevant regulatory mechanisms in Synechocystis. Synergy can be used to interactively perform analyses such as gene/motif search, network visualization and motif/function enrichment. Considering the importance of Synechocystis for photosynthesis and biofuel research, we believe that Synergy will become a valuable resource to the research community.     

Genome-Wide Methylation Patterns in Salmonella enterica Subsp. enterica Serovars

The methylation of DNA bases plays an important role in numerous biological processes including development, gene expression, and DNA replication. Salmonella is an important foodborne pathogen, and methylation in Salmonella is implicated in virulence. Using single molecule real-time (SMRT) DNA-sequencing, we sequenced and assembled the complete genomes of eleven Salmonella enterica isolates from nine different serovars, and analysed the whole-genome methylation patterns of each genome. We describe 16 distinct N⁶-methyladenine (m6A) methylated motifs, one N⁴-methylcytosine (m4C) motif, and one combined m6A-m4C motif. Eight of these motifs are novel, i.e., they have not been previously described. We also identified the methyltransferases (MTases) associated with 13 of the motifs. Some motifs are conserved across all Salmonella serovars tested, while others were found only in a subset of serovars. Eight of the nine serovars contained a unique methylated motif that was not found in any other serovar (most of these motifs were part of Type I restriction modification systems), indicating the high diversity of methylation patterns present in Salmonella.     

A network of conserved co-occurring motifs for the regulation of alternative splicing

Cis-acting short sequence motifs play important roles in alternative splicing. It is now possible to identify such sequence motifs as conserved sequence patterns in genome sequence alignments. Here, we report the systematic search for motifs in the neighboring introns of alternatively spliced exons by using comparative analysis of mammalian genome alignments. We identified 11 conserved sequence motifs that might be involved in the regulation of alternative splicing. These motifs are not only significantly overrepresented near alternatively spliced exons, but they also co-occur with each other, thus, forming a network of cis-elements, likely to be the basis for context-dependent regulation. Based on this finding, we applied the motif co-occurrence to predict alternatively skipped exons. We verified exon skipping in 29 cases out of 118 predictions (25%) by EST and mRNA sequences in the databases. For the predictions not verified by the database sequences, we confirmed exon skipping in 10 additional cases by using both RT–PCR experiments and the publicly available RNA-Seq data. These results indicate that even more alternative splicing events will be found with the progress of large-scale and high-throughput analyses for various tissue samples and developmental stages.     

Systematic discovery of new recognition peptides mediating protein interaction networks

Many aspects of cell signalling, trafficking, and targeting are governed by interactions between globular protein domains and short peptide segments. These domains often bind multiple peptides that share a common sequence pattern, or “linear motif” (e.g., SH3 binding to PxxP). Many domains are known, though comparatively few linear motifs have been discovered. Their short length (three to eight residues), and the fact that they often reside in disordered regions in proteins makes them difficult to detect through sequence comparison or experiment. Nevertheless, each new motif provides critical molecular details of how interaction networks are constructed, and can explain how one protein is able to bind to very different partners. Here we show that binding motifs can be detected using data from genome-scale interaction studies, and thus avoid the normally slow discovery process. Our approach based on motif over-representation in non-homologous sequences, rediscovers known motifs and predicts dozens of others. Direct binding experiments reveal that two predicted motifs are indeed protein-binding modules: a DxxDxxxD protein phosphatase 1 binding motif with a K_D of 22 μM and a VxxxRxYS motif that binds Translin with a K_D of 43 μM. We estimate that there are dozens or even hundreds of linear motifs yet to be discovered that will give molecular insight into protein networks and greatly illuminate cellular processes.     

Statistical tests to compare motif count exceptionalities

Background  

Finding over- or under-represented motifs in biological sequences is now a common task in genomics. Thanks to p-value calculation for motif counts, exceptional motifs are identified and represent candidate functional motifs. The present work addresses the related question of comparing the exceptionality of one motif in two different sequences. Just comparing the motif count p-values in each sequence is indeed not sufficient to decide if this motif is significantly more exceptional in one sequence compared to the other one. A statistical test is required.