Fast prediction of RNA-RNA interaction

<p>Abstract</p> <p>Background</p> <p>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.</p> <p>Methods</p> <p>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.</p> <p>Results</p> <p>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.</p

An Efficient Algorithm for Upper Bound on the Partition Function of Nucleic Acids

It has been shown that minimum free energy structure for RNAs and RNA-RNA interaction is often incorrect due to inaccuracies in the energy parameters and inherent limitations of the energy model. In contrast, ensemble based quantities such as melting temperature and equilibrium concentrations can be more reliably predicted. Even structure prediction by sampling from the ensemble and clustering those structures by Sfold [7] has proven to be more reliable than minimum free energy structure prediction. The main obstacle for ensemble based approaches is the computational complexity of the partition function and base pairing probabilities. For instance, the space complexity of the partition function for RNA-RNA interaction is $O(n^4)$ and the time complexity is $O(n^6)$ which are prohibitively large [4,12]. Our goal in this paper is to give a fast algorithm, based on sparse folding, to calculate an upper bound on the partition function. Our work is based on the recent algorithm of Hazan and Jaakkola [10]. The space complexity of our algorithm is the same as that of sparse folding algorithms, and the time complexity of our algorithm is $O(MFE(n)\ell)$ for single RNA and $O(MFE(m, n)\ell)$ for RNA-RNA interaction in practice, in which $MFE$ is the running time of sparse folding and $\ell \leq n$ ($\ell \leq n + m$) is a sequence dependent parameter

A comprehensive comparison of general RNA-RNA interaction prediction methods

RNA-RNA interactions are fast emerging as a major functional component in many newly discovered non-coding RNAs. Basepairing is believed to be a major contributor to the stability of these intermolecular interactions, much like intramolecular basepairs formed in RNA secondary structure. As such, using algorithms similar to those for predicting RNA secondary structure, computational methods have been recently developed for the prediction of RNA-RNA interactions.We provide the first comprehensive comparison comprising 14 methods that predict general intermolecular basepairs. To evaluate these, we compile an extensive data set of 54 experimentally confirmed fungal snoRNA-rRNA interactions and 102 bacterial sRNA-mRNA interactions. We test the performance accuracy of all methods, evaluating the effects of tool settings, sequence length, and multiple sequence alignment usage and quality.Our results show that-unlike for RNA secondary structure prediction-the overall best performing tools are non-comparative energy-based tools utilizing accessibility information that predict short interactions on this data set. Furthermore, we find that maintaining high accuracy across biologically different data sets and increasing input lengths remains a huge challenge, causing implications for de novo transcriptome-wide searches. Finally, we make our interaction data set publicly available for future development and benchmarking efforts

LinearCoFold and LinearCoPartition: Linear-Time Algorithms for Secondary Structure Prediction of Interacting RNA molecules

Many ncRNAs function through RNA-RNA interactions. Fast and reliable RNA structure prediction with consideration of RNA-RNA interaction is useful. Some existing tools are less accurate due to omitting the competing of intermolecular and intramolecular base pairs, or focus more on predicting the binding region rather than predicting the complete secondary structure of two interacting strands. Vienna RNAcofold, which reduces the problem into the classical single sequence folding by concatenating two strands, scales in cubic time against the combined sequence length, and is slow for long sequences. To address these issues, we present LinearCoFold, which predicts the complete minimum free energy structure of two strands in linear runtime, and LinearCoPartition, which calculates the cofolding partition function and base pairing probabilities in linear runtime. LinearCoFold and LinearCoPartition follows the concatenation strategy of RNAcofold, but are orders of magnitude faster than RNAcofold. For example, on a sequence pair with combined length of 26,190 nt, LinearCoFold is 86.8x faster than RNAcofold MFE mode (0.6 minutes vs. 52.1 minutes), and LinearCoPartition is 642.3x faster than RNAcofold partition function mode (1.8 minutes vs. 1156.2 minutes). Different from the local algorithms, LinearCoFold and LinearCoPartition are global cofolding algorithms without restriction on base pair length. Surprisingly, LinearCoFold and LinearCoPartition's predictions have higher PPV and sensitivity of intermolecular base pairs. Furthermore, we apply LinearCoFold to predict the RNA-RNA interaction between SARS-CoV-2 gRNA and human U4 snRNA, which has been experimentally studied, and observe that LinearCoFold's prediction correlates better to the wet lab results

Topology of RNA-RNA interaction structures

The topological filtration of interacting RNA complexes is studied and the role is analyzed of certain diagrams called irreducible shadows, which form suitable building blocks for more general structures. We prove that for two interacting RNAs, called interaction structures, there exist for fixed genus only finitely many irreducible shadows. This implies that for fixed genus there are only finitely many classes of interaction structures. In particular the simplest case of genus zero already provides the formalism for certain types of structures that occur in nature and are not covered by other filtrations. This case of genus zero interaction structures is already of practical interest, is studied here in detail and found to be expressed by a multiple context-free grammar extending the usual one for RNA secondary structures. We show that in $O(n^6)$ time and $O(n^4)$ space complexity, this grammar for genus zero interaction structures provides not only minimum free energy solutions but also the complete partition function and base pairing probabilities.Comment: 40 pages 15 figure