sTarPicker: A Method for Efficient Prediction of Bacterial sRNA Targets Based on a Two-Step Model for Hybridization

Bacterial sRNAs are a class of small regulatory RNAs involved in regulation of expression of a variety of genes. Most sRNAs act in trans via base-pairing with target mRNAs, leading to repression or activation of translation or mRNA degradation. To date, more than 1,000 sRNAs have been identified. However, direct targets have been identified for only approximately 50 of these sRNAs. Computational predictions can provide candidates for target validation, thereby increasing the speed of sRNA target identification. Although several methods have been developed, target prediction for bacterial sRNAs remains challenging.Here, we propose a novel method for sRNA target prediction, termed sTarPicker, which was based on a two-step model for hybridization between an sRNA and an mRNA target. This method first selects stable duplexes after screening all possible duplexes between the sRNA and the potential mRNA target. Next, hybridization between the sRNA and the target is extended to span the entire binding site. Finally, quantitative predictions are produced with an ensemble classifier generated using machine-learning methods. In calculations to determine the hybridization energies of seed regions and binding regions, both thermodynamic stability and site accessibility of the sRNAs and targets were considered. Comparisons with the existing methods showed that sTarPicker performed best in both performance of target prediction and accuracy of the predicted binding sites.sTarPicker can predict bacterial sRNA targets with higher efficiency and determine the exact locations of the interactions with a higher accuracy than competing programs. sTarPicker is available at http://ccb.bmi.ac.cn/starpicker/

Statistical modeling of RNA structure profiling experiments enables parsimonious reconstruction of structure landscapes.

RNA plays key regulatory roles in diverse cellular processes, where its functionality often derives from folding into and converting between structures. Many RNAs further rely on co-existence of alternative structures, which govern their response to cellular signals. However, characterizing heterogeneous landscapes is difficult, both experimentally and computationally. Recently, structure profiling experiments have emerged as powerful and affordable structure characterization methods, which improve computational structure prediction. To date, efforts have centered on predicting one optimal structure, with much less progress made on multiple-structure prediction. Here, we report a probabilistic modeling approach that predicts a parsimonious set of co-existing structures and estimates their abundances from structure profiling data. We demonstrate robust landscape reconstruction and quantitative insights into structural dynamics by analyzing numerous data sets. This work establishes a framework for data-directed characterization of structure landscapes to aid experimentalists in performing structure-function studies

Insights into RNA design from novel molecular tools

RNA, previously recognized merely as a messenger of genetic information, has been recently rediscovered as a versatile molecule with a central role in cellular regulation. These regulatory functions are enabled by its specific chemical makeup that allows it to fold into intricate and flexible structures. In stark contrast with DNA, RNA forms a variety of structural motifs that serve as efficient points of contact in molecular recognition. It is therefore clear, that dynamic RNA structures dictate the binding availability of interfaces that play important roles in molecular regulation inside living cells. As such, the need for tools that can accurately capture and predict RNA structure in vivo continues to be essential to understand RNA function. To this end, my dissertation focuses on the development of molecular tools to predict and characterize accessible RNA interfaces in their native environment. First, I established the usefulness of a fluorescence-based in vivo oligonucleotide hybridization approach to identify accessible interfaces by characterizing numerous RNA regions in several biologically relevant molecules in E. coli. I then described these RNA interactions using a biophysical model based on thermodynamic principles and incorporating large sets of data collected using this fluorescence-based system. This approach displayed improved prediction capabilities of RNA accessibility compared to un-optimized versions without incorporation of in vivo data. Finally, I detailed the development and application of a high throughput tool for the large-scale characterization of accessible interfaces within native RNAs in a single experiment. In this approach, in vivo oligonucleotide hybridization was coupled to transcriptional elongation control to allow analysis via next generation sequencing. This tool was used to obtain complete landscapes of functional structure for 72 regulatory molecules in a single experiment (>1000 regions). Altogether the results of this high throughput approach revealed a pattern indicating that RNA-RNA interaction sites are either highly accessible or highly protected, suggesting their binding status (e.g. actively bound or unbound). In addition, within bacterial small RNAs, our approached revealed the role of the global regulator Hfq as universal structural relaxer. The compendium of these tools provides a unique and fundamental perspective in the study of functional RNA structure, namely, the identification of dynamic structures. Furthermore, the information provided by these approaches significantly aids in the design of synthetic RNAs for a variety of purposes, including gene expression control.Chemical Engineerin

Genomic data mining for the computational prediction of small non-coding RNA genes

The objective of this research is to develop a novel computational prediction algorithm for non-coding RNA (ncRNA) genes using features computable for any genomic sequence without the need for comparative analysis. Existing comparative-based methods require the knowledge of closely related organisms in order to search for sequence and structural similarities. This approach imposes constraints on the type of ncRNAs, the organism, and the regions where the ncRNAs can be found. We have developed a novel approach for ncRNA gene prediction without the limitations of current comparative-based methods. Our work has established a ncRNA database required for subsequent feature and genomic analysis. Furthermore, we have identified significant features from folding-, structural-, and ensemble-based statistics for use in ncRNA prediction. We have also examined higher-order gene structures, namely operons, to discover potential insights into how ncRNAs are transcribed. Being able to automatically identify ncRNAs on a genome-wide scale is immensely powerful for incorporating it into a pipeline for large-scale genome annotation. This work will contribute to a more comprehensive annotation of ncRNA genes in microbial genomes to meet the demands of functional and regulatory genomic studies.Ph.D.Committee Chair: Dr. G. Tong Zhou; Committee Member: Dr. Arthur Koblasz; Committee Member: Dr. Eberhard Voit; Committee Member: Dr. Xiaoli Ma; Committee Member: Dr. Ying X

Computational analysis of noncoding RNAs

Noncoding RNAs have emerged as important key players in the cell. Understanding their surprisingly diverse range of functions is challenging for experimental and computational biology. Here, we review computational methods to analyze noncoding RNAs. The topics covered include basic and advanced techniques to predict RNA structures, annotation of noncoding RNAs in genomic data, mining RNA-seq data for novel transcripts and prediction of transcript structures, computational aspects of microRNAs, and database resources.Austrian Science Fund (Schrodinger Fellowship J2966-B12)German Research Foundation (grant WI 3628/1-1 to SW)National Institutes of Health (U.S.) (NIH award 1RC1CA147187