Motivation: During the last few years, several new small regulatory RNAs (sRNAs) have been discovered in bacteria. Most of them act as post-transcriptional regulators by base pairing to a target mRNA, causing translational repression or activation, or mRNA degradation. Numerous sRNAs have already been identified, but the number of experimentally verified targets is considerably lower. Consequently, computational target prediction is in great demand. Many existing tar-get prediction programs neglect the accessibility of target sites and the existence of a seed, while other approaches are either specialized to certain types of RNAs or too slow for genome-wide searches. Results: We introduce INTARNA, a new general and fast approach to the prediction of RNA-RNA interactions incorporating accessibility of target sites as well as the existence of a user-definable seed. We suc-cessfully applied INTARNA to the prediction of bacterial sRNA targets and determined the exact locations of the interactions with a higher accuracy than competing programs.

Motivation: Regulatory RNAs often unfold their action via RNA-RNA interaction. Transcriptional gene silencing by means of siRNAs and miRNA as well as snoRNA directed RNA editing rely on this mechanism. Additionally ncRNA regulation in bacteria is mainly based upon RNA duplex formation. Finding putative target sites for newly discovered ncRNAs is a lengthy task as tools for cofolding RNA molecules like RNAcofold and RNAup are too slow for genome-wide search. Tools like RNAhybrid that neglects intramolecular interac-tions have runtimes proportional to O(m n), albeit with a large prefactor. Still in many cases the need for even faster methods exists. Results: We present a new program, RNAplex, especially designed to quickly find possible hybridization sites for a query RNA in large RNA databases. RNAplex uses a slightly different energy model which reduces the computational time by a factor 10–27 compared to RNAhybrid. In addition a length penalty allows to focus the target search on short highly stable interactions. Availability: RNAplex can be downloaded at

Abstract. The thermodynamics of RNA-RNA interaction consists of two components: the energy necessary to make a potential binding region accessible, i.e. unpaired, and the energy gained from the base pairing of the two interaction partners. We show here that both components can be efficiently computed using an improved variant of RNAup. The method is then applied to a set of bacterial small RNAs involved in translational control. In all cases of biologically active sRNA target interactions, the target sites predicted by RNAup are in perfect agreement with literature. In addition to prediction of target site location, RNAup can also be used to determine the mode of sRNA action. Using information about target site location and the accessibility change resulting from sRNA binding we can discriminate between positive and negative regulators of translation. 1

Recent interests, such as RNA interference and antisense RNA regulation, strongly motivate the problem of predicting whether two nucleic acid strands interact. Motivation: Regulatory non-coding RNAs (ncRNAs) such as microRNAs play an important role in gene regulation. Studies on both prokaryotic and eukaryotic cells show that such ncRNAs usually bind to their target mRNA to regulate the translation of corresponding genes. The specificity of these interactions depends on the stability of intermolecular and intramolecular base pairing. While methods like deep sequencing allow to discover an ever increasing set of ncRNAs, there are no high-throughput methods available to detect their associated targets. Hence, there is an increasing need for precise computational target prediction. In order to predict basepairing probability of any two bases in interacting nucleic acids, it

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IntaRNA: efficient prediction of bacterial sRNA targets incorporating target site accessibility and seed regions

In the past few years, a large set of new regulatory ncRNAs have been identified, but the number of experimentally verified targets is considerably low. Thus, computational target prediction methods are on high demand. Whereas all previous approaches for predicting a general joint structure have a complexity of O(n 6) running time and O(n 4) space, a more time and space efficient interaction prediction that is able to handle complex joint structures is necessary for genome-wide target prediction problems. In this paper we show how to reduce both the time and space complexity of RNA-RNA interaction prediction problem as described by Alkan et al. [1] by a linear factor via dynamic programming sparsification- which allows to safely discard large portions of DP tables. Applying sparsification techniques reduces the complexity of the original algorithm to O(n 4 ψ(n)) in time and O(n 2 ψ(n) + n 3) in space for some function ψ(n), which turns out to have small values for the range of n that we encounter in practice. By the use of polymer-zeta property for RNA-structures, we demonstrate that ψ(n) = O(n) on average. We evaluate our sparsified algorithm for RNA-RNA interaction prediction through total free energy minimization, based on the energy model of Chitsaz et al. [11], on a set of known interactions. Our results confirm the significant reduction of time and space requirements in practice.

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Abstract. Regulatory antisense RNAs are a class of ncRNAs that regulate gene expression by prohibiting the translation of an mRNA by establishing stable interactions with a target sequence. There is great demand for efficient computational methods to predict the specific interaction between an ncRNA and its target mRNA(s). There are a number of algorithms in the literature which can predict a variety of such interactions- unfortunately at a very high computational cost. Although some existing target prediction approaches are much faster, they are specialized for interactions with a single binding site. In this paper we present a novel algorithm to accurately predict the minimum free energy structure of RNA-RNA interaction under the most general type of interactions studied in the literature. Moreover, we introduce a fast heuristic method to predict the specific (multiple) binding sites of two interacting RNAs. We verify the performance of our algorithms for joint structure and binding site prediction on a set of known interacting RNA pairs. Experimental results show our algorithms are highly accurate and outperform all competitive approaches. 1