BACKGROUND: Over the last decade the drop in short-read sequencing costs has allowed experimental techniques utilizing sequencing to address specific biological questions to proliferate, oftentimes outpacing standardized or effective analysis approaches for the data generated. There are growing amounts of bacterial 3'-end sequencing data, yet there is currently no commonly accepted analysis methodology for this datatype. Most data analysis approaches are somewhat ad hoc and, despite the presence of substantial signal within annotated genes, focus on genomic regions outside the annotated genes (e.g. 3' or 5' UTRs). Furthermore, the lack of consistent systematic analysis approaches, as well as the absence of genome-wide ground truth data, make it impossible to compare conclusions generated by different labs, using different organisms. RESULTS: We present PIPETS, (Poisson Identification of PEaks from Term-Seq data), an R package available on Bioconductor that provides a novel analysis method for 3'-end sequencing data. PIPETS is a statistically informed, gene-annotation agnostic methodology. Across two different datasets from two different organisms, PIPETS identified significant 3'-end termination signal across a wider range of annotated genomic contexts than existing analysis approaches, suggesting that existing approaches may miss biologically relevant signal. Furthermore, assessment of the previously called 3'-end positions not captured by PIPETS showed that they were uniformly very low coverage. CONCLUSIONS: PIPETS provides a broadly applicable platform to explore and analyze 3'-end sequencing data sets from across different organisms. It requires only the 3'-end sequencing data, and is broadly accessible to non-expert users.

Riboswitches are conserved structural ribonucleic acid (RNA) sensors that are mainly found to regulate a large number of genes/operons in bacteria. Presently, >50 bacterial riboswitch classes have been discovered, but only the thiamine pyrophosphate riboswitch class is detected in a few eukaryotes like fungi, plants and algae. One of the most important challenges in riboswitch research is to discover existing riboswitch classes in eukaryotes and to understand the evolution of bacterial riboswitches. However, traditional search methods for riboswitch detection have failed to detect eukaryotic riboswitches besides just one class and any distant structural homologs of riboswitches. We developed a novel approach based on inverse RNA folding that attempts to find sequences that match the shape of the target structure with minimal sequence conservation based on key nucleotides that interact directly with the ligand. Then, to support our matched candidates, we expanded the results into a covariance model representing similar sequences preserving the structure. Our method transforms a structure-based search into a sequence-based search that considers the conservation of secondary structure shape and ligand-binding residues. This method enables us to identify a potential structural candidate in fungi that could be the distant homolog of bacterial purine riboswitches. Further, phylogenomic analysis and evolutionary distribution of this structural candidate indicate that the most likely point of origin of this structural candidate in these organisms is associated with the loss of traditional purine riboswitches. The computational approach could be applicable to other domains and problems in RNA research.

In comparison to protein coding sequences, the impact of mutation and natural selection on the sequence and function of non-coding (ncRNA) genes is not well understood. Many ncRNA genes are narrowly distributed to only a few organisms, and appear to be rapidly evolving. Compared to protein coding sequences, there are many challenges associated with assessment of ncRNAs that are not well addressed by conventional phylogenetic approaches, including: short sequence length, lack of primary sequence conservation, and the importance of secondary structure for biological function. Riboswitches are structured ncRNAs that directly interact with small molecules to regulate gene expression in bacteria. They typically consist of a ligand-binding domain (aptamer) whose folding changes drive changes in gene expression. The glycine riboswitch is among the most well-studied due to the widespread occurrence of a tandem aptamer arrangement (tandem), wherein two homologous aptamers interact with glycine and each other to regulate gene expression. However, a significant proportion of glycine riboswitches are comprised of single aptamers (singleton). Here we use graph clustering to circumvent the limitations of traditional phylogenetic analysis when studying the relationship between the tandem and singleton glycine aptamers. Graph clustering enables a broader range of pairwise comparison measures to be used to assess aptamer similarity. Using this approach, we show that one aptamer of the tandem glycine riboswitch pair is typically much more highly conserved, and that which aptamer is conserved depends on the regulated gene. Furthermore, our analysis also reveals that singleton aptamers are more similar to either the first or second tandem aptamer, again based on the regulated gene. Taken together, our findings suggest that tandem glycine riboswitches degrade into functional singletons, with the regulated gene(s) dictating which glycine-binding aptamer is conserved.

BACKGROUND: Proteins recognize many different aspects of RNA ranging from single stranded regions to discrete secondary or tertiary structures. High-throughput sequencing (HTS) of in vitro selected populations offers a large scale method to study RNA-proteins interactions. However, most existing analysis methods require that the binding motifs are enriched in the population relative to earlier rounds, and that motifs are found in a loop or single stranded region of the potential RNA secondary structure. Such methods do not generalize to all RNA-protein interaction as some RNA binding proteins specifically recognize more complex structures such as double stranded RNA. RESULTS: In this study, we use HT-SELEX derived populations to study the landscape of RNAs that interact with Geobacillus kaustophilus ribosomal protein S15. Our data show high sequence and structure diversity and proved intractable to existing methods. Conventional programs identified some sequence motifs, but these are found in less than 5-10% of the total sequence pool. Therefore, we developed a novel framework to analyze HT-SELEX data. Our process accounts for both sequence and structure components by abstracting the overall secondary structure into smaller substructures composed of a single base-pair stack, which allows us to leverage existing approaches already used in k-mer analysis to identify enriched motifs. By focusing on secondary structure motifs composed of specific two base-pair stacks, we identified significantly enriched or depleted structure motifs relative to earlier rounds. CONCLUSIONS: Discrete substructures are likely to be important to RNA-protein interactions, but they are difficult to elucidate. Substructures can help make highly diverse sequence data more tractable. The structure motifs provide limited accuracy in predicting enrichment suggesting that G. kaustophilus S15 can either recognize many different secondary structure motifs or some aspects of the interaction are not captured by the analysis. This highlights the importance of considering secondary and tertiary structure elements and their role in RNA-protein interactions.

MOTIVATION: High-throughput biological research requires simultaneous visualization as well as analysis of genomic data, e.g. read alignments, variant calls and genomic annotations. Traditionally, such integrative analysis required desktop applications operating on locally stored data. Many current terabyte-size datasets generated by large public consortia projects, however, are already only feasibly stored at specialist genome analysis centers. As even small laboratories can afford very large datasets, local storage and analysis are becoming increasingly limiting, and it is likely that most such datasets will soon be stored remotely, e.g. in the cloud. These developments will require web-based tools that enable users to access, analyze and view vast remotely stored data with a level of sophistication and interactivity that approximates desktop applications. As rapidly dropping cost enables researchers to collect data intended to answer questions in very specialized contexts, developers must also provide software libraries that empower users to implement customized data analyses and data views for their particular application. Such specialized, yet lightweight, applications would empower scientists to better answer specific biological questions than possible with general-purpose genome browsers currently available. RESULTS: Using recent advances in core web technologies (HTML5), we developed Scribl, a flexible genomic visualization library specifically targeting coordinate-based data such as genomic features, DNA sequence and genetic variants. Scribl simplifies the development of sophisticated web-based graphical tools that approach the dynamism and interactivity of desktop applications. AVAILABILITY AND IMPLEMENTATION: Software is freely available online at http://chmille4.github.com/Scribl/ and is implemented in JavaScript with all modern browsers supported.