Programs and computational frameworks for predicting RNA sequences with desired folding properties are continually being developed and expanded. A decade has passed since they were last reviewed in this journal, and this brief review provides an update to the review published at that time. Given a target secondary structure, these programs aim to predict RNA sequences that fold into the desired structure while satisfying various constraints. This procedure is known as inverse RNA folding. Traditionally, inverse RNA folding has been used to design optimized RNAs with favorable properties. This updated review covers some of the most widely used freeware programs developed for this purpose over the past decade. RNAinverse, part of the Vienna RNA package, was the first program devised to address the inverse RNA folding problem, and many subsequent programs were described in the earlier review. Some of the most important computational frameworks are the Infrared framework and DesiRNA. In addition, RNA design capabilities have been incorporated into the RNAstructure package, while NUPACK, as well as MoiRNAiFold, MODENA, incaRNAfbinv, and related tools have undergone recent updates. A variety of strategies have also emerged to address the problem of 3D RNA design and RNA-RNA interactions. The various programs mentioned employ distinct approaches, ranging from replica exchange Monte Carlo to constraint satisfaction, as well as Boltzmann sampling and machine learning approaches. Machine learning methods are being developed for emerging applications in biotechnology such as messenger RNA(mRNA) design and CRISPR guide RNA (gRNA) design. This brief review examines these programs and provides a timely update.

Hepatitis delta virus (HDV) is geographically classified according to eight known genotypes. The combined hepatitis B-hepatitis D (HEPB-HEPD) disease is the severest form of chronic viral hepatitis in humans and is characterized by mortality rates of ~20%. Hepatitis delta virus has no FDA approved therapy and its only available vaccine is the one for HEPB. Because it is the smallest RNA virus known to infect humans, RNA folding predictions by energy minimization of the whole genome can reveal important information on functional RNA secondary structure elements within the genome. A public HDV database (HDVdb) contains 512 HDV strains on which various bioinformatics methods can be applied, aiming to detect strains that could perform RNA editing via conformational switching. Up to date, only one such strain from HDVdb was known to perform that, in HDV genotype 3. Our goal was to locate more such strains, both in genotype 3 and in other possible HDV genotypes. In past work, by an eigenvalue mathematical analysis, we made an initial prediction that this peculiar RNA editing mechanism also exists in HDV genotype 7. We hereby extend our earlier findings and present newly discovered HDV strains from multiple genotypes for further analysis of RNA editing sites within the virus. The relevant strains taken from HDVdb are from both genotype 3 of Peru and genotype 7 of Cameroon. Additionally, the new strains have a variety of optional RNA editing sites that we report, many of which are unknown to date.

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.

Hepatitis B virus (HBV) infection kinetics in immunodeficient mice reconstituted with humanized livers from inoculation to steady state is highly dynamic despite the absence of an adaptive immune response. To recapitulate the multiphasic viral kinetic patterns, we developed an agent-based model that includes intracellular virion production cycles reflecting the cyclic nature of each individual virus lifecycle. The model fits the data well predicting an increase in production cycles initially starting with a long production cycle of 1 virion per 20 hours that gradually reaches 1 virion per hour after approximately 3-4 days before virion production increases dramatically to reach to a steady state rate of 4 virions per hour per cell. Together, modeling suggests that it is the cyclic nature of the virus lifecycle combined with an initial slow but increasing rate of HBV production from each cell that plays a role in generating the observed multiphasic HBV kinetic patterns in humanized mice.

BACKGROUND: RNA deleterious point mutation prediction was previously addressed with programs such as RNAmute and MultiRNAmute. The purpose of these programs is to predict a global conformational rearrangement of the secondary structure of a functional RNA molecule, thereby disrupting its function. RNAmute was designed to deal with only single point mutations in a brute force manner, while in MultiRNAmute an efficient approach to deal with multiple point mutations was developed. The approach used in MultiRNAmute is based on the stabilization of the suboptimal RNA folding prediction solutions and/or destabilization of the optimal folding prediction solution of the wild type RNA molecule. The MultiRNAmute algorithm is significantly more efficient than the brute force approach in RNAmute, but in the case of long sequences and large m-point mutation sets the MultiRNAmute becomes exponential in examining all possible stabilizing and destabilizing mutations. RESULTS: An inherent limitation in the RNAmute and MultiRNAmute programs is their ability to predict only substitution mutations, as these programs were not designed to work with deletion or insertion mutations. To address this limitation we herein develop a very fast algorithm, based on suboptimal folding solutions, to predict a predefined number of multiple point deleterious mutations as specified by the user. Depending on the user's choice, each such set of mutations may contain combinations of deletions, insertions and substitution mutations. Additionally, we prove the hardness of predicting the most deleterious set of point mutations in structural RNAs. CONCLUSIONS: We developed a method that extends our previous MultiRNAmute method to predict insertion and deletion mutations in addition to substitutions. The additional advantage of the new method is its efficiency to find a predefined number of deleterious mutations. Our new method may be exploited by biologists and virologists prior to site-directed mutagenesis experiments, which involve indel mutations along with substitutions. For example, our method may help to investigate the change of function in an RNA virus via mutations that disrupt important motifs in its secondary structure.

SUMMARY: RNA design has conceptually evolved from the inverse RNA folding problem. In the classical inverse RNA problem, the user inputs an RNA secondary structure and receives an output RNA sequence that folds into it. Although modern RNA design methods are based on the same principle, a finer control over the resulting sequences is sought. As an important example, a substantial number of non-coding RNA families show high preservation in specific regions, while being more flexible in others and this information should be utilized in the design. By using the additional information, RNA design tools can help solve problems of practical interest in the growing fields of synthetic biology and nanotechnology. incaRNAfbinv 2.0 utilizes a fragment-based approach, enabling a control of specific RNA secondary structure motifs. The new version allows significantly more control over the general RNA shape, and also allows to express specific restrictions over each motif separately, in addition to other advanced features. AVAILABILITY AND IMPLEMENTATION: incaRNAfbinv 2.0 is available through a standalone package and a web-server at https://www.cs.bgu.ac.il/incaRNAfbinv. Source code, command-line and GUI wrappers can be found at https://github.com/matandro/RNAsfbinv. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

SUMMARY: Riboswitches are cis-regulatory non-coding genomic segments that control the expression of downstream genes by undergoing conformational change upon ligand binding. We present a comprehensive database of prokaryotic riboswitches that allows the user to search for riboswitches using multiple criteria, extract information about riboswitch location and gene/operon it regulates. RiboD provides a very useful resource that can be utilized for the better understanding of riboswitch-based gene regulation in bacteria and archaea. AVAILABILITY AND IMPLEMENTATION: RiboD can be freely accessed on the web at http://ribod.iiserkol.ac.in/.

Computational programs for predicting RNA sequences with desired folding properties have been extensively developed and expanded in the past several years. Given a secondary structure, these programs aim to predict sequences that fold into a target minimum free energy secondary structure, while considering various constraints. This procedure is called inverse RNA folding. Inverse RNA folding has been traditionally used to design optimized RNAs with favorable properties, an application that is expected to grow considerably in the future in light of advances in the expanding new fields of synthetic biology and RNA nanostructures. Moreover, it was recently demonstrated that inverse RNA folding can successfully be used as a valuable preprocessing step in computational detection of novel noncoding RNAs. This review describes the most popular freeware programs that have been developed for such purposes, starting from RNAinverse that was devised when formulating the inverse RNA folding problem. The most recently published ones that consider RNA secondary structure as input are antaRNA, RNAiFold and incaRNAfbinv, each having different features that could be beneficial to specific biological problems in practice. The various programs also use distinct approaches, ranging from ant colony optimization to constraint programming, in addition to adaptive walk, simulated annealing and Boltzmann sampling. This review compares between the various programs and provides a simple description of the various possibilities that would benefit practitioners in selecting the most suitable program. It is geared for specific tasks requiring RNA design based on input secondary structure, with an outlook toward the future of RNA design programs.

A low rank matrix X has been contaminated by uniformly distributed noise, missing values, outliers and corrupt entries. Reconstruction of X from the singular values and singular vectors of the contaminated matrix Y is a key problem in machine learning, computer vision and data science. In this paper we show that common contamination models (including arbitrary combinations of uniform noise, missing values, outliers and corrupt entries) can be described efficiently using a single framework. We develop an asymptotically optimal algorithm that estimates X by manipulation of the singular values of Y, which applies to any of the contamination models considered. Finally, we find an explicit signal-to-noise cutoff, below which estimation of X from the singular value decomposition of Y must fail, in a well-defined sense.

SUMMARY: In RNA design problems, it is plausible to assume that the user would be interested in preserving a particular RNA secondary structure motif, or fragment, for biological reasons. The preservation could be in structure or sequence, or both. Thus, the inverse RNA folding problem could benefit from considering fragment constraints. We have developed a new interactive Java application called RNA fragment-based inverse that allows users to insert an RNA secondary structure in dot-bracket notation. It then performs sequence design that conforms to the shape of the input secondary structure, the specified thermodynamic stability, the specified mutational robustness and the user-selected fragment after shape decomposition. In this shape-based design approach, specific RNA structural motifs with known biological functions are strictly enforced, while others can possess more flexibility in their structure in favor of preserving physical attributes and additional constraints. AVAILABILITY: RNAfbinv is freely available for download on the web at http://www.cs.bgu.ac.il/~RNAexinv/RNAfbinv. The site contains a help file with an explanation regarding the exact use.

Programs for RNA mutational analysis that are structure-based and rely on secondary structure prediction have been developed and expanded in the past several years. They can be used for a variety of purposes, such as in suggesting point mutations that will alter RNA virus replication or translation initiation, investigating the effect of deleterious and compensatory mutations in allosteric ribozymes and riboswitches, computing an optimal path of mutations to get from one ribozyme fold to another, or analyzing regulatory RNA sequences by their mutational profile. This review describes three different freeware programs (RNAMute, RDMAS and RNAmutants) that have been developed for such purposes. RNAMute and RDMAS in principle perform energy minimization prediction by available software such as RNAfold from the Vienna RNA package or Zuker's Mfold, while RNAmutants provides an efficient method using essential ingredients from energy minimization prediction. Both RNAMute in its extended version that uses RNAsubopt from the Vienna RNA package and the RNAmutants software are able to predict multiple-point mutations using developed methodologies, while RDMAS is currently restricted to single-point mutations. The strength of RNAMute in its extended version is the ability to predict a small number of point mutations in an accurate manner. RNAmutants is well fit for large scale simulations involving the calculation of all k-mutants, where k can be a large integer number, of a given RNA sequence.

BACKGROUND: RNAexinv is an interactive java application that performs RNA sequence design, constrained to yield a specific RNA shape and physical attributes. It is an extended inverse RNA folding program with the rationale behind that the generated sequences should not only fold into a desired structure, but they should also exhibit favorable attributes such as thermodynamic stability and mutational robustness. RNAexinv considers not only the secondary structure in order to design sequences, but also the mutational robustness and the minimum free energy. The sequences that are generated may not fully conform with the given RNA secondary structure, but they will strictly conform with the RNA shape of the given secondary structure and thereby take into consideration the recommended values of thermodynamic stability and mutational robustness that are provided. RESULTS: The output consists of designed sequences that are generated by the proposed method. Selecting a sequence displays the secondary structure drawings of the target and the predicted fold of the sequence, including some basic information about the desired and achieved thermodynamic stability and mutational robustness. RNAexinv can be used successfully without prior experience, simply specifying an initial RNA secondary structure in dot-bracket notation and numerical values for the desired neutrality and minimum free energy. The package runs under LINUX operating system. Secondary structure predictions are performed using the Vienna RNA package. CONCLUSIONS: RNAexinv is a user friendly tool that can be used for RNA sequence design. It is especially useful in cases where a functional stem-loop structure of a natural sequence should be strictly kept in the designed sequences but a distant motif in the rest of the structure may contain one more or less nucleotide at the expense of another, as long as the global shape is preserved. This allows the insertion of physical observables as constraints. RNAexinv is available at http://www.cs.bgu.ac.il/~RNAexinv.

SUMMARY: The DNA in eukaryotic cells is packed into the chromatin that is composed of nucleosomes. Positioning of the nucleosome core particles on the sequence is a problem of great interest because of the role nucleosomes play in different cellular processes including gene regulation. Using the sequence structure of 10.4 base DNA repeat presented in our previous works and nucleosome core DNA sequences database, we have derived the complete nucleosome DNA bendability matrix of Caenorhabditis elegans. We have developed a web server named FineStr that allows users to upload genomic sequences in FASTA format and to perform a single-base-resolution nucleosome mapping on them. AVAILABILITY: FineStr server is freely available for use on the web at http:/www.cs.bgu.ac.il/ approximately nucleom. The site contains a help file with explanation regarding the exact usage. CONTACT: [email protected].

BACKGROUND: RNAmute is an interactive Java application which, given an RNA sequence, calculates the secondary structure of all single point mutations and organizes them into categories according to their similarity to the predicted structure of the wild type. The secondary structure predictions are performed using the Vienna RNA package. A more efficient implementation of RNAmute is needed, however, to extend from the case of single point mutations to the general case of multiple point mutations, which may often be desired for computational predictions alongside mutagenesis experiments. But analyzing multiple point mutations, a process that requires traversing all possible mutations, becomes highly expensive since the running time is O(nm) for a sequence of length n with m-point mutations. Using Vienna's RNAsubopt, we present a method that selects only those mutations, based on stability considerations, which are likely to be conformational rearranging. The approach is best examined using the dot plot representation for RNA secondary structure. RESULTS: Using RNAsubopt, the suboptimal solutions for a given wild-type sequence are calculated once. Then, specific mutations are selected that are most likely to cause a conformational rearrangement. For an RNA sequence of about 100 nts and 3-point mutations (n = 100, m = 3), for example, the proposed method reduces the running time from several hours or even days to several minutes, thus enabling the practical application of RNAmute to the analysis of multiple-point mutations. CONCLUSION: A highly efficient addition to RNAmute that is as user friendly as the original application but that facilitates the practical analysis of multiple-point mutations is presented. Such an extension can now be exploited prior to site-directed mutagenesis experiments by virologists, for example, who investigate the change of function in an RNA virus via mutations that disrupt important motifs in its secondary structure. A complete explanation of the application, called MultiRNAmute, is available at [1].

We focus on finding a consensus motif of a set of homologous or functionally related RNA molecules. Recent approaches to this problem have been limited to simple motifs, require sequence alignment, and make prior assumptions concerning the data set. We use genetic programming to predict RNA consensus motifs based solely on the data set. Our system -- dubbed GeRNAMo (Genetic programming of RNA Motifs) -- predicts the most common motifs without sequence alignment and is capable of dealing with any motif size. Our program only requires the maximum number of stems in the motif, and if prior knowledge is available the user can specify other attributes of the motif (e.g., the range of the motif's minimum and maximum sizes), thereby increasing both sensitivity and speed. We describe several experiments using either ferritin iron response element (IRE); signal recognition particle (SRP); or microRNA sequences, showing that the most common motif is found repeatedly, and that our system offers substantial advantages over previous methods.

BACKGROUND: RNAMute is an interactive Java application that calculates the secondary structure of all single point mutations, given an RNA sequence, and organizes them into categories according to their similarity with respect to the wild type predicted structure. The secondary structure predictions are performed using the Vienna RNA package. Several alternatives are used for the categorization of single point mutations: Vienna's RNAdistance based on dot-bracket representation, as well as tree edit distance and second eigenvalue of the Laplacian matrix based on Shapiro's coarse grain tree graph representation. RESULTS: Selecting a category in each one of the processed tables lists all single point mutations belonging to that category. Selecting a mutation displays a graphical drawing of the single point mutation and the wild type, and includes basic information such as associated energies, representations and distances. RNAMute can be used successfully with very little previous experience and without choosing any parameter value alongside the initial RNA sequence. The package runs under LINUX operating system. CONCLUSION: RNAMute is a user friendly tool that can be used to predict single point mutations leading to conformational rearrangements in the secondary structure of RNAs. In several cases of substantial interest, notably in virology, a point mutation may lead to a loss of important functionality such as the RNA virus replication and translation initiation because of a conformational rearrangement in the secondary structure.

MOTIVATION: Conformational switching in RNAs is thought to be of fundamental importance in several biological processes, including translational regulation, regulation of self-cleavage in viruses, protein biosynthesis and mRNA splicing. Current methods for detecting bi-stable RNAs that can lead to structural switching when triggered by an outside event rely on kinetics, energetics and properties of the combinatorial structure space of RNAs. Based on these properties, tools have been developed to predict whether a given sequence folds to a structure characterized by a bi-stable conformation, or to design multi-stable RNAs by an iterative algorithm. A useful addition is in developing a local procedure to prescribe, given an initial sequence, the least amount of mutations needed to drive the system into an optimal bi-stable conformation. RESULTS: We introduce a local procedure for predicting mutations, by generating and analyzing eigenvalue tables, that are capable of transforming the wild-type sequence into a bi-stable conformation. The method is independent of the folding algorithms but relies on their success. It can be used in conjunction with existing tools, as well as being incorporated into more general RNA prediction packages. We apply this procedure on three well-studied structures. First, the method is validated on the mutation leading to a conformational switch in the spliced leader RNA from Leptomonas collosoma, a mutation that has already been confirmed by an experiment. Second, the method is used to predict a mutation that can lead to a novel conformational switch in the P5abc subdomain of the group I intron ribozyme in Tetrahymena thermophila. Third, the method is applied on Hepatitis delta virus to predict mutations that transform the wild-type into a bi-stable conformation, a configuration assessed by calculating the free energies using folding prediction algorithms. The predictions in the final examples need to be verified experimentally, whereas the mutation predicted in the first example complies with the experiment. This supports the use of our proposed method on other known structures, as well as genetically engineered ones. AVAILABILITY: An eigenvalue application will be available in the near future attached to one of the existing tools.

In this paper, the relationship between bilateral filtering and anisotropic diffusion is examined. The bilateral filtering approach represents a large class of nonlinear digital image filters. We first explore the connection between anisotropic diffusion and adaptive smoothing, and then the connection between adaptive smoothing and bilateral filtering. Previously, adaptive smoothing was considered to be an inconsistent approximation to the nonlinear diffusion equation. We extend adaptive smoothing to make it consistent, thus enabling a unified viewpoint that relates nonlinear digital image filters and the nonlinear diffusion equation.