rsRNASP: A residue-separation-based statistical potential for RNA 3D structure evaluation

Knowledge-based statistical potentials have been shown to be rather effective in protein 3-dimensional (3D) structure evaluation and prediction. Recently, several statistical potentials have been developed for RNA 3D structure evaluation, while their performances are either still at a low level for the test datasets from structure prediction models or dependent on the “black-box” process through neural networks. In this work, we have developed an all-atom distance-dependent statistical potential based on residue separation for RNA 3D structure evaluation, namely rsRNASP, which is composed of short- and long-ranged potentials distinguished by residue separation. The extensive examinations against available RNA test datasets show that rsRNASP has apparently higher performance than the existing statistical potentials for the realistic test datasets with large RNAs from structure prediction models, including the newly released RNA-Puzzles dataset, and is comparable to the existing top statistical potentials for the test datasets with small RNAs or near-native decoys. In addition, rsRNASP is superior to RNA3DCNN, a recently developed scoring function through 3D convolutional neural networks. rsRNASP and the relevant databases are available to the public.     

An algorithm for searching RNA motifs in genomic sequences

RNA molecules, which are found in all living cells, fold into characteristic structures that account for their diverse functional activities. Many of these RNA structures consist of a collection of fundamental RNA motifs. The various combinations of RNA basic components form different RNA classes and define their unique structural and functional properties. The availability of many genome sequences makes it possible to search computationally for functional RNAs. Biological experiments indicate that functional RNAs have characteristic RNA structural motifs represented by specific combinations of base pairings and conserved nucleotides in the loop regions. The searching for those well-ordered RNA structures and their homologues in genomic sequences is very helpful for the understanding of RNA-based gene regulation. In this paper, we consider the following problem: given an RNA sequence with a known secondary structure, efficiently determine candidate segments in genomic sequences that can potentially form RNA secondary structures similar to the given RNA secondary structure. Our new bottom-up approach searches all potential stem-loops similar to ones of the given RNA secondary structure first, and then based on located stem-loops, detects potential homologous structural RNAs in genomic sequences.     

Review of machine learning methods for RNA secondary structure prediction

Secondary structure plays an important role in determining the function of noncoding RNAs. Hence, identifying RNA secondary structures is of great value to research. Computational prediction is a mainstream approach for predicting RNA secondary structure. Unfortunately, even though new methods have been proposed over the past 40 years, the performance of computational prediction methods has stagnated in the last decade. Recently, with the increasing availability of RNA structure data, new methods based on machine learning (ML) technologies, especially deep learning, have alleviated the issue. In this review, we provide a comprehensive overview of RNA secondary structure prediction methods based on ML technologies and a tabularized summary of the most important methods in this field. The current pending challenges in the field of RNA secondary structure prediction and future trends are also discussed.     

SCARNA: fast and accurate structural alignment of RNA sequences by matching fixed-length stem fragments

MOTIVATION: The functions of non-coding RNAs are strongly related to their secondary structures, but it is known that a secondary structure prediction of a single sequence is not reliable. Therefore, we have to collect similar RNA sequences with a common secondary structure for the analyses of a new non-coding RNA without knowing the exact secondary structure itself. Therefore, the sequence comparison in searching similar RNAs should consider not only their sequence similarities but also their potential secondary structures. Sankoff's algorithm predicts the common secondary structures of the sequences, but it is computationally too expensive to apply to large-scale analyses. Because we often want to compare a large number of cDNA sequences or to search similar RNAs in the whole genome sequences, much faster algorithms are required. RESULTS: We propose a new method of comparing RNA sequences based on the structural alignments of the fixed-length fragments of the stem candidates. The implemented software, SCARNA (Stem Candidate Aligner for RNAs), is fast enough to apply to the long sequences in the large-scale analyses. The accuracy of the alignments is better or comparable with the much slower existing algorithms. AVAILABILITY: The web server of SCARNA with graphical structural alignment viewer is available at http://www.scarna.org/.     

A new Motzkin class for joint RNA secondary structures

In general RNA prediction problem includes genetic mapping, physical mapping and structure prediction. The ultimate goal of structure prediction is to obtain the three dimensional structure of bimolecules through computation. The key concept for solving the above mentioned problem is the appropriate representation of the biological structures. Even though, the problems that concern representations of certain biological structures like secondary structures either are characterized as NP-complete or with high complexity, few approximation algorithms and techniques had been constructed, mainly with polynomial complexity, concerning the prediction of RNA secondary structures. In this paper, a new class of Motzkin paths is introduced, the so-called semi-elevated inverse Motzkin peakless paths for the representation of two interacting RNA molecules. The basic combinatorial interpretations on single RNA secondary structures are extended via these new Motzkin paths on two RNA molecules and can be applied to the prediction methods of joint structures formed by interacting RNAs.     

RNA structure prediction from evolutionary patterns of nucleotide composition

Structural elements in RNA molecules have a distinct nucleotide composition, which changes gradually over evolutionary time. We discovered certain features of these compositional patterns that are shared between all RNA families. Based on this information, we developed a structure prediction method that evaluates candidate structures for a set of homologous RNAs on their ability to reproduce the patterns exhibited by biological structures. The method is named SPuNC for ‘Structure Prediction using Nucleotide Composition’. In a performance test on a diverse set of RNA families we demonstrate that the SPuNC algorithm succeeds in selecting the most realistic structures in an ensemble. The average accuracy of top-scoring structures is significantly higher than the average accuracy of all ensemble members (improvements of more than 20% observed). In addition, a consensus structure that includes the most reliable base pairs gleaned from a set of top-scoring structures is generally more accurate than a consensus derived from the full structural ensemble. Our method achieves better accuracy than existing methods on several RNA families, including novel riboswitches and ribozymes. The results clearly show that nucleotide composition can be used to reveal the quality of RNA structures and thus the presented technique should be added to the set of prediction tools.     

Combinatorics of RNA–RNA interaction

RNA–RNA binding is an important phenomenon observed for many classes of non-coding RNAs and plays a crucial role in a number of regulatory processes. Recently several MFE folding algorithms for predicting the joint structure of two interacting RNA molecules have been proposed. Here joint structure means that in a diagram representation the intramolecular bonds of each partner are pseudoknot-free, that the intermolecular binding pairs are noncrossing, and that there is no so-called “zigzag” configuration. This paper presents the combinatorics of RNA interaction structures including their generating function, singularity analysis as well as explicit recurrence relations. In particular, our results imply simple asymptotic formulas for the number of joint structures.     

Detecting riboSNitches with RNA folding algorithms: a genome-wide benchmark

Ribonucleic acid (RNA) secondary structure prediction continues to be a significant challenge, in particular when attempting to model sequences with less rigidly defined structures, such as messenger and non-coding RNAs. Crucial to interpreting RNA structures as they pertain to individual phenotypes is the ability to detect RNAs with large structural disparities caused by a single nucleotide variant (SNV) or riboSNitches. A recently published human genome-wide parallel analysis of RNA structure (PARS) study identified a large number of riboSNitches as well as non-riboSNitches, providing an unprecedented set of RNA sequences against which to benchmark structure prediction algorithms. Here we evaluate 11 different RNA folding algorithms’ riboSNitch prediction performance on these data. We find that recent algorithms designed specifically to predict the effects of SNVs on RNA structure, in particular remuRNA, RNAsnp and SNPfold, perform best on the most rigorously validated subsets of the benchmark data. In addition, our benchmark indicates that general structure prediction algorithms (e.g. RNAfold and RNAstructure) have overall better performance if base pairing probabilities are considered rather than minimum free energy calculations. Although overall aggregate algorithmic performance on the full set of riboSNitches is relatively low, significant improvement is possible if the highest confidence predictions are evaluated independently.     

Experimental identification and analysis of macronuclear non-coding RNAs from the ciliate Tetrahymena thermophila

The ciliate Tetrahymena thermophila is an important eukaryotic model organism that has been used in pioneering studies of general phenomena, such as ribozymes, telomeres, chromatin structure and genome reorganization. Recent work has shown that Tetrahymena has many classes of small RNA molecules expressed during vegetative growth or sexual reorganization. In order to get an overview of medium-sized (40-500 nt) RNAs expressed from the Tetrahymena genome, we created a size-fractionated cDNA library from macronuclear RNA and analyzed 80 RNAs, most of which were previously unknown. The most abundant class was small nucleolar RNAs (snoRNAs), many of which are formed by an unusual maturation pathway. The modifications guided by the snoRNAs were analyzed bioinformatically and experimentally and many Tetrahymena-specific modifications were found, including several in an essential, but not conserved domain of ribosomal RNA. Of particular interest, we detected two methylations in the 5'-end of U6 small nuclear RNA (snRNA) that has an unusual structure in Tetrahymena. Further, we found a candidate for the first U8 outside metazoans, and an unusual U14 candidate. In addition, a number of candidates for new non-coding RNAs were characterized by expression analysis at different growth conditions.     

Prediction of RNA secondary structure based on helical regions distribution

MOTIVATION: RNAs play an important role in many biological processes and knowing their structure is important in understanding their function. Due to difficulties in the experimental determination of RNA secondary structure, the methods of theoretical prediction for known sequences are often used. Although many different algorithms for such predictions have been developed, this problem has not yet been solved. It is thus necessary to develop new methods for predicting RNA secondary structure. The most-used at present is Zuker's algorithm which can be used to determine the minimum free energy secondary structure. However many RNA secondary structures verified by experiments are not consistent with the minimum free energy secondary structures. In order to solve this problem, a method used to search a group of secondary structures whose free energy is close to the global minimum free energy was developed by Zuker in 1989. When considering a group of secondary structures, if there is no experimental data, we cannot tell which one is better than the others. This case also occurs in combinatorial and heuristic methods. These two kinds of methods have several weaknesses. Here we show how the central limit theorem can be used to solve these problems. RESULTS: An algorithm for predicting RNA secondary structure based on helical regions distribution is presented, which can be used to find the most probable secondary structure for a given RNA sequence. It consists of three steps. First, list all possible helical regions. Second, according to central limit theorem, estimate the occurrence probability of every helical region based on the Monte Carlo simulation. Third, add the helical region with the biggest probability to the current structure and eliminate the helical regions incompatible with the current structure. The above processes can be repeated until no more helical regions can be added. Take the current structure as the final RNA secondary structure. In order to demonstrate the confidence of the program, a test on three RNA sequences: tRNAPhe, Pre-tRNATyr, and Tetrahymena ribosomal RNA intervening sequence, is performed. AVAILABILITY: The program is written in Turbo Pascal 7.0. The source code is available upon request. CONTACT: Wujj@nic.bmi.ac.cn or Liwj@mail.bmi.ac.cn      

Use of linear regression model to compare RNA secondary structures

With more and more ribonucleic acid (RNA) secondary structures accumulated, the need for comparing different RNA secondary structures often arises in function prediction and evolutionary analysis. Numerous efficient algorithms were developed for comparing different RNA secondary structures, but challenges remain. In this paper, six new models based on the linear regression model were proposed for the comparison of RNA secondary structures. The proposed models were tested on a mixed data, containing six secondary structures from RNase P RNAs, three secondary structures from SSU rRNA and five secondary structures from 16S ribosomal RNAs. The results have shown the effectiveness of the proposed models. Moreover, the time complexity of our models is favorable by comparing with that of the existing methods which solve the similar problem.     

Evolutionary Triplet Models of Structured RNA

The reconstruction and synthesis of ancestral RNAs is a feasible goal for paleogenetics. This will require new bioinformatics methods, including a robust statistical framework for reconstructing histories of substitutions, indels and structural changes. We describe a “transducer composition” algorithm for extending pairwise probabilistic models of RNA structural evolution to models of multiple sequences related by a phylogenetic tree. This algorithm draws on formal models of computational linguistics as well as the 1985 protosequence algorithm of David Sankoff. The output of the composition algorithm is a multiple-sequence stochastic context-free grammar. We describe dynamic programming algorithms, which are robust to null cycles and empty bifurcations, for parsing this grammar. Example applications include structural alignment of non-coding RNAs, propagation of structural information from an experimentally-characterized sequence to its homologs, and inference of the ancestral structure of a set of diverged RNAs. We implemented the above algorithms for a simple model of pairwise RNA structural evolution; in particular, the algorithms for maximum likelihood (ML) alignment of three known RNA structures and a known phylogeny and inference of the common ancestral structure. We compared this ML algorithm to a variety of related, but simpler, techniques, including ML alignment algorithms for simpler models that omitted various aspects of the full model and also a posterior-decoding alignment algorithm for one of the simpler models. In our tests, incorporation of basepair structure was the most important factor for accurate alignment inference; appropriate use of posterior-decoding was next; and fine details of the model were least important. Posterior-decoding heuristics can be substantially faster than exact phylogenetic inference, so this motivates the use of sum-over-pairs heuristics where possible (and approximate sum-over-pairs). For more exact probabilistic inference, we discuss the use of transducer composition for ML (or MCMC) inference on phylogenies, including possible ways to make the core operations tractable.     

Computer-aided prediction of RNA secondary structures.

A brief survey of computer algorithms that have been developed to generate predictions of the secondary structures of RNA molecules is presented. Two particular methods are described in some detail. The first utilizes a thermodynamic energy minimization algorithm that takes into account the likelihood that short-range folding tends to be favored over long-range interactions. The second utilizes an interactive computer graphic modelling algorithm that enables the user to consider thermodynamic criteria as well as structural data obtained by nuclease susceptibility, chemical reactivity and phylogenetic studies. Examples of structures for prokaryotic 16S and 23S ribosomal RNAs, several eukaryotic 5S ribosomal RNAs and rabbit beta-globin messenger RNA are presented as case studies in order to describe the two techniques. Anm argument is made for integrating the two approaches presented in this paper, enabling the user to generate proposed structures using thermodynamic criteria, allowing interactive refinement of these structures through the application of experimentally derived data.     

Sampled ensemble neutrality as a feature to classify potential structured RNAs

Background

Structured RNAs have many biological functions ranging from catalysis of chemical reactions to gene regulation. Yet, many homologous structured RNAs display most of their conservation at the secondary or tertiary structure level. As a result, strategies for structured RNA discovery rely heavily on identification of sequences sharing a common stable secondary structure. However, correctly distinguishing structured RNAs from surrounding genomic sequence remains challenging, especially during de novo discovery. RNA also has a long history as a computational model for evolution due to the direct link between genotype (sequence) and phenotype (structure). From these studies it is clear that evolved RNA structures, like protein structures, can be considered robust to point mutations. In this context, an RNA sequence is considered robust if its neutrality (extent to which single mutant neighbors maintain the same secondary structure) is greater than that expected for an artificial sequence with the same minimum free energy structure.

Results

In this work, we bring concepts from evolutionary biology to bear on the structured RNA de novo discovery process. We hypothesize that alignments corresponding to structured RNAs should consist of neutral sequences. We evaluate several measures of neutrality for their ability to distinguish between alignments of structured RNA sequences drawn from Rfam and various decoy alignments. We also introduce a new measure of RNA structural neutrality, the structure ensemble neutrality (SEN). SEN seeks to increase the biological relevance of existing neutrality measures in two ways. First, it uses information from an alignment of homologous sequences to identify a conserved biologically relevant structure for comparison. Second, it only counts base-pairs of the original structure that are absent in the comparison structure and does not penalize the formation of additional base-pairs.

Conclusion

We find that several measures of neutrality are effective at separating structured RNAs from decoy sequences, including both shuffled alignments and flanking genomic sequence. Furthermore, as an independent feature classifier to identify structured RNAs, SEN yields comparable performance to current approaches that consider a variety of features including stability and sequence identity. Finally, SEN outperforms other measures of neutrality at detecting mutational robustness in bacterial regulatory RNA structures.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-014-1203-8) contains supplementary material, which is available to authorized users.     

Memory efficient folding algorithms for circular RNA secondary structures

BACKGROUND: A small class of RNA molecules, in particular the tiny genomes of viroids, are circular. Yet most structure prediction algorithms handle only linear RNAs. The most straightforward approach is to compute circular structures from 'internal' and 'external' substructures separated by a base pair. This is incompatible, however, with the memory-saving approach of the Vienna RNA Package which builds a linear RNA structure from shorter (internal) structures only. RESULT: Here we describe how circular secondary structures can be obtained without additional memory requirements as a kind of 'post-processing' of the linear structures. AVAILABILITY: The circular folding algorithm is implemented in the current version of the of RNAfold program of the Vienna RNA Package, which can be downloaded from http://www.tbi.univie.ac.at/RNA/