RNA-RNA Interaction Prediction Research Articles

SUDAZFLNC – a curated and searchable online database for zebrafish lncRNAs, mRNAs, miRNAs, and circadian expression profiles

The zebrafish (Danio rerio) has emerged as a model organism for investigating lncRNAs-driven fundamental biological processes, such as circadian rhythms, physiology, metabolism, and various diseases. While state-of-the-art sequencing technologies have identified an increasing number of lncRNAs in zebrafish, their annotations are far from complete. In this study, we collect 28,925 lncRNAs from both the published studies and our own RNA-seq analyses and establish a novel webserver-based database called SUDAZFLNC (https://sudarna.website/). The database, containing 28,925 lncRNAs, 25,432 mRNAs, and 368 miRNAs, provides several crucial features and annotations for the zebrafish RNAs, such as sequence identifiers (IDs), sequence length, hexamer score, coding probabilities, GO and KEGG annotations, and micropeptides. SUDAZFLNC also includes time-course expression profiles of 3288 lncRNAs, 25,432 mRNAs, and 342 miRNAs generated from our RNA-seq experiments, and 149, 4407, and 43 rhythmically expressed lncRNAs, mRNAs, and miRNAs, respectively. Based on the peak expression patterns, we classified these RNAs into morning RNAs, evening RNAs, and night RNAs. Users of the database can access the RNA sequences and their expression profiles by searching the corresponding IDs from the Graphical User Interface (GUI) of the database. The database supports several features to investigate RNA sequences and expression profiles, including BLAST, search of sequence and data, ID conversion, and RNA-RNA interaction prediction. This is the largest curated database of zebrafish RNAs and their expression profiles to date.

MiR-21 Targets Long Noncoding RNA PCAT29 to Promote Cell Proliferation in Neuroblastoma

Long noncoding RNA (lncRNA) PCAT29 has been characterized as a tumor suppressor in several types of cancer, although its involvement in neuroblastoma (NB) is unknown. In this study, we analyzed the role of PCAT29 in NB. In paired NB and nontumor tissues from 56 patients with NB, microRNA (miR)-21 and PCAT29 expression was determined with reverse transcription quantitative PCR. Correlation between miR-21 and PCAT29 was evaluated with linear regression. The interaction between miR-21 and PCAT29 was predicted by the IntaRNA 2.0 program. In NB cells, miR-21 and PCAT29 were overexpressed to explore their relationship. In NB cell proliferation, the roles of miR-21 and PCAT29 were analyzed with propidium iodide staining and Ki67 staining assays. The results showed that PCAT29 was downregulated and miR-21 was upregulated in NB. MiR-21 was inversely correlated with PCAT29. RNA-RNA interaction prediction revealed that miR-21 might target PCAT29. MiR-21 overexpression reduced PCAT29 expression and increased NB cell proliferation, whereas PCAT29 overexpression inhibited NB cell proliferation. PCAT29 overexpression promoted NB cell apoptosis, while miR-21 overexpression inhibited NB cell apoptosis and attenuated PCAT29 overexpression-mediated NB cell apoptosis. In conclusion, MiR-21 may target PCAT29 to promote cell apoptosis in NB.

LncRNA HAND2-AS1 exerts anti-oncogenic effects on bladder cancer via restoration of RARB as a sponge of microRNA-146

BackgroundGrowing evidence has shown that long noncoding RNA: microRNA: mRNA is implicated in tumor initiation, development, and progression. Long noncoding RNA HAND2-AS1 exhibits anti-cancer effects in diverse cancers. However, the knowledge of HAND-AS1 in bladder cancer development remains unknown.MethodsLncRNA and miRNA microarray was conducted to explore different expressed RNA in primary bladder cancer specimens. RNA-RNA interaction prediction tools miRcode (http://www.mircode.org/), DIANA-lncBase v2 (https://carolina.imis.athena-innovation.gr/diana_tools/web/index.php?r=lncbasev2%2Findex-experimental), DIANA-TarBase v.8 (https://carolina.imis.athena-innovation.gr/diana_tools/web/index.php?r=tarbasev8%2Findex) and miRDB (http://www.mirdb.org/) were employed to predict the interactions between RNA. Bladder cancer cell lines were used to perform cell proliferation and apoptosis assays. Western blot and quantitative Real-time Polymerase Chain Reaction were used to determine the expression of protein and RNA separately. Dual-luciferase assay was conducted to determine the activity of three prime untranslated region of retinoic acid receptor beta (RARB). Furthermore, 5637 human bladder cancer mouse models were established to investigate the interactions of lncRNA: miRNA: mRNA in vivo.ResultsBased on the RT2 lncRNA PCR Arrays analysis, we validated HAND2-AS1 declined in bladder cancer and negatively correlated with the depth of invasion and grades. The overexpression of HAND2-AS1 in human bladder cancer cells 5637 and RT4 hampered cell proliferation by provoking Caspase 3-triggered cell apoptosis. Besides, one of the HAND2-AS1 sponges, miR-146, elevated in bladder cancer and targeted the tumor suppressor, retinoic acid receptor beta (RARB). We further demonstrated that the HAND2-AS1: miR-146: RARB complex promoted Caspase 3-mediated apoptosis by suppressing COX-2 expression. Finally, the results gained in mouse xenografts suggested that HAND2-AS1 diminished miR-146 expression, thereby reversing the suppression of miR-146 on RARB-mediated apoptosis and contributing to bladder cancer regression.ConclusionThe present study sheds light on the fact that lncRNA HAND2-AS1 exerted as a tumor suppressor by releasing RARB from miR-146, leading to tumor proliferation and invasion inhibition. The findings expanded HAND2-AS-mediated regulatory networks' knowledge and provided novel insights to improve the RARB-targeted regimens against bladder cancer.

LncRNA UASR1 sponges miR-107 in colorectal cancer to upregulate oncogenic CDK8 and promote cell proliferation.

lncRNA UASR1 (UASR1) has been characterized as an oncogenic lncRNA in breast cancer. UASR1 was predicted to interact with miR-107, which serves tumor suppressive roles mainly by targeting CDK8. The present study was performed to investigate the interactions among UASR1, miR-107 and CDK8 in colorectal cancer (CRC). A total of 62 patients with CRC, including 40 males and 22 females (age range, 38–67 years; mean age, 57.2±7.6 years) were enrolled at the Second Hospital of Shandong University between July 2012 and July 2014. The expression of UASR1 in tissues and cells were detected by reverse transcription-quantitative polymerase chain reaction. The interaction between UASR1 and miR-107 was investigated by performing dual luciferase activity assay, and the effects of overexpression of UASR1, miR-107 and CDK8 on the proliferation of CR4 cells were analyzed by performing cell proliferation analysis. It was observed that UASR1 is upregulated in CRC and its high expression levels predicted poor survival in patients with CRC. RNA-RNA interaction prediction demonstrated that UASR1 may interact with miR-107. In CRC cells, overexpression of UASR1 and miR-107 did not affect each other. However, the expression of CDK8, a target of miR-107, was upregulated following overexpression of UASR1. Notably, overexpression of UASR1 decreased the inhibitory effects of miR-107 on cell proliferation and the expression of CDK8. Therefore, UASR1 may sponge miR-107 to upregulate oncogenic CDK8, thereby promoting CRC cell proliferation.

A ceRNA network and a potential regulatory axis in gastric cancer with different degrees of immune cell infiltration.

Immune cell infiltration is an important indicator of whether tumor patients will benefit from immunotherapy. Gastric cancer is one of the most common tumors in the world, and new indicators of immunotherapy are urgently needed. The aim of this study was to construct ceRNA networks in gastric cancer with different degrees of immune cell infiltration. We analyzed the expression profiles of different gastric cancer with different degrees of immune cell infiltration retrieved from The Cancer Genome Atlas (TCGA) database and found differentially expressed lncRNAs, mRNAs, and miRNAs. A ceRNA regulatory network of gastric cancer with different degrees of immune cell infiltration was constructed using functional annotation, RNA‐RNA interaction prediction, correlation analysis, survival analysis, and other comprehensive bioinformatics methods. The interaction and correlation between ceRNAs were verified using experiments on tumor tissues and cell lines. Cell line experiments showed a potential RP11‐1094M14.8/miR‐1269a/CXCL9 axis that was consistent with the ceRNA theory. qRT‐PCR results showed that RP11‐1094M14.8 knockdown significantly reduced the expression of CXCL9, and RP11‐1094M14.8 overexpression had the opposite effect. The results of clinical analysis of gastric cancer samples showed that RP11‐1094M14.8 and CXCL9 were highly expressed in hot tumors, and CXCL9 was positively correlated with a better prognosis for patients. The constructed novel ceRNA network and the potential regulatory axis may provide a comprehensive understanding of the potential mechanisms of development in gastric cancer with different degrees of immune cell infiltration. The RP11‐1094M14.8/miR‐1269a/CXCL9 axis may serve as a potential immune‐therapeutic target for gastric cancer with different degrees of immune cell infiltration.

CopomuS-Ranking Compensatory Mutations to Guide RNA-RNA Interaction Verification Experiments.

In silico RNA-RNA interaction prediction is widely applied to identify putative interaction partners and to assess interaction details in base pair resolution. To verify specific interactions, in vitro evidence can be obtained via compensatory mutation experiments. Unfortunately, the selection of compensatory mutations is non-trivial and typically based on subjective ad hoc decisions. To support the decision process, we introduce our COmPensatOry MUtation Selector CopomuS. CopomuS evaluates the effects of mutations on RNA-RNA interaction formation using a set of objective criteria, and outputs a reliable ranking of compensatory mutation candidates. For RNA-RNA interaction assessment, the state-of-the-art IntaRNA prediction tool is applied. We investigate characteristics of successfully verified RNA-RNA interactions from the literature, which guided the design of CopomuS. Finally, we evaluate its performance based on experimentally validated compensatory mutations of prokaryotic sRNAs and their target mRNAs. CopomuS predictions highly agree with known results, making it a valuable tool to support the design of verification experiments for RNA-RNA interactions. It is part of the IntaRNA package and available as stand-alone webserver for ad hoc application.

The impact of various seed, accessibility and interaction constraints on sRNA target prediction- a systematic assessment

BackgroundSeed and accessibility constraints are core features to enable highly accurate sRNA target screens based on RNA-RNA interaction prediction. Currently, available tools provide different (sets of) constraints and default parameter sets. Thus, it is hard to impossible for users to estimate the influence of individual restrictions on the prediction results.ResultsHere, we present a systematic assessment of the impact of established and new constraints on sRNA target prediction both on a qualitative as well as computational level. This is done exemplarily based on the performance of IntaRNA, one of the most exact sRNA target prediction tools. IntaRNA provides various ways to constrain considered seed interactions, e.g. based on seed length, its accessibility, minimal unpaired probabilities, or energy thresholds, beside analogous constraints for the overall interaction. Thus, our results reveal the impact of individual constraints and their combinations.ConclusionsThis provides both a guide for users what is important and recommendations for existing and upcoming sRNA target prediction approaches.We show on a large sRNA target screen benchmark data set that only by altering the parameter set, IntaRNA recovers 30% more verified interactions while becoming 5-times faster. This exemplifies the potential of seed, accessibility and interaction constraints for sRNA target prediction.

LncRRIsearch: A Web Server for lncRNA-RNA Interaction Prediction Integrated With Tissue-Specific Expression and Subcellular Localization Data.

Long non-coding RNAs (lncRNAs) play critical roles in various biological processes, but the function of the majority of lncRNAs is still unclear. One approach for estimating a function of a lncRNA is the identification of its interaction target because functions of lncRNAs are expressed through interaction with other biomolecules in quite a few cases. In this paper, we developed “LncRRIsearch,” which is a web server for comprehensive prediction of human and mouse lncRNA-lncRNA and lncRNA-mRNA interaction. The prediction was conducted using RIblast, which is a fast and accurate RNA-RNA interaction prediction tool. Users can investigate interaction target RNAs of a particular lncRNA through a web interface. In addition, we integrated tissue-specific expression and subcellular localization data for the lncRNAs with the web server. These data enable users to examine tissue-specific or subcellular localized lncRNA interactions. LncRRIsearch is publicly accessible at http://rtools.cbrc.jp/LncRRIsearch/.

RCPred: RNA complex prediction as a constrained maximum weight clique problem

BackgroundRNAs can interact and form complexes, which have various biological roles. The secondary structure prediction of those complexes is a first step towards the identification of their 3D structure. We propose an original approach that takes advantage of the high number of RNA secondary structure and RNA-RNA interaction prediction tools. We formulate the problem of RNA complex prediction as the determination of the best combination (according to the free energy) of predicted RNA secondary structures and RNA-RNA interactions.ResultsWe model those predicted structures and interactions as a graph in order to have a combinatorial optimization problem that is a constrained maximum weight clique problem. We propose an heuristic based on Breakout Local Search to solve this problem and a tool, called RCPred, that returns several solutions, including motifs like internal and external pseudoknots. On a large number of complexes, RCPred gives competitive results compared to the methods of the state of the art.ConclusionsWe propose in this paper a method called RCPred for the prediction of several secondary structures of RNA complexes, including internal and external pseudoknots. As further works we will propose an improved computation of the global energy and the insertion of 3D motifs in the RNA complexes.

Long non-coding RNAs as pan-cancer master gene regulators of associated protein-coding genes: a systems biology approach.

Despite years of research, we are still unraveling crucial stages of gene expression regulation in cancer. On the basis of major biological hallmarks, we hypothesized that there must be a uniform gene expression pattern and regulation across cancer types. Among non-coding genes, long non-coding RNAs (lncRNAs) are emerging as key gene regulators playing powerful roles in cancer. Using TCGA RNAseq data, we analyzed coding (mRNA) and non-coding (lncRNA) gene expression across 15 and 9 common cancer types, respectively. 70 significantly differentially expressed genes common to all 15 cancer types were enlisted. Correlating with protein expression levels from Human Protein Atlas, we observed 34 positively correlated gene sets which are enriched in gene expression, transcription from RNA Pol-II, regulation of transcription and mitotic cell cycle biological processes. Further, 24 lncRNAs were among common significantly differentially expressed non-coding genes. Using guilt-by-association method, we predicted lncRNAs to be involved in same biological processes. Combining RNA-RNA interaction prediction and transcription regulatory networks, we identified E2F1, FOXM1 and PVT1 regulatory path as recurring pan-cancer regulatory entity. PVT1 is predicted to interact with SYNE1 at 3′-UTR; DNAJC9, RNPS1 at 5′-UTR and ATXN2L, ALAD, FOXM1 and IRAK1 at CDS sites. The key findings are that through E2F1, FOXM1 and PVT1 regulatory axis and possible interactions with different coding genes, PVT1 may be playing a prominent role in pan-cancer development and progression.

Interactive implementations of thermodynamics-based RNA structure and RNA-RNA interaction prediction approaches for example-driven teaching.

The investigation of RNA-based regulation of cellular processes is becoming an increasingly important part of biological or medical research. For the analysis of this type of data, RNA-related prediction tools are integrated into many pipelines and workflows. In order to correctly apply and tune these programs, the user has to have a precise understanding of their limitations and concepts. Within this manuscript, we provide the mathematical foundations and extract the algorithmic ideas that are core to state-of-the-art RNA structure and RNA–RNA interaction prediction algorithms. To allow the reader to change and adapt the algorithms or to play with different inputs, we provide an open-source web interface to JavaScript implementations and visualizations of each algorithm. The conceptual, teaching-focused presentation enables a high-level survey of the approaches, while providing sufficient details for understanding important concepts. This is boosted by the simple generation and study of examples using the web interface available at http://rna.informatik.uni-freiburg.de/Teaching/. In combination, we provide a valuable resource for teaching, learning, and understanding the discussed prediction tools and thus enable a more informed analysis of RNA-related effects.

A Novel Method for Assessing the Statistical Significance of RNA-RNA Interactions Between Two Long RNAs.

RNA-RNA interactions are key mechanisms through which noncoding RNA (ncRNA) regions exert biological functions. Computational prediction of RNA-RNA interactions is an essential method for detecting novel RNA-RNA interactions because their comprehensive detection by biological experimentation is still quite difficult. Many RNA-RNA interaction prediction tools have been developed, but they tend to produce many false positives. Accordingly, assessment of the statistical significance of computationally predicted interactions is an important task. However, there is no method to evaluate the statistical significance of RNA-RNA interactions that is applicable to interactions between two long RNA sequences. We developed a method to calculate the p-value for the minimal interaction energy between two long RNA sequences. The developed method depends on the fact that minimum interaction energies of RNA-RNA interactions between long RNAs follow a Gumbel distribution when repeat sequences in RNAs are masked. To show the usefulness of the developed method, we applied it to whole human 5'-untranslated region (UTR) and 3'-UTR sequences to detect novel 5'-UTR-3'-UTR interactions. We thus identified two significant 5'-UTR-3'-UTR interactions. Specifically, the human small proline-rich repeat protein 3 shows conserved 5'-UTR-3'-UTR interactions with some nucleotide variations preserving base pairings among primates. Our developed method enables us to detect statistically significant RNA-RNA interactions between long RNAs such as long ncRNAs. Statistical significance estimates help in identification of interactions for experimental validation and provide novel insights into the function of ncRNA regions.

Freiburg RNA tools: a central online resource for RNA-focused research and teaching.

The Freiburg RNA tools webserver is a well established online resource for RNA-focused research. It provides a unified user interface and comprehensive result visualization for efficient command line tools. The webserver includes RNA-RNA interaction prediction (IntaRNA, CopraRNA, metaMIR), sRNA homology search (GLASSgo), sequence-structure alignments (LocARNA, MARNA, CARNA, ExpaRNA), CRISPR repeat classification (CRISPRmap), sequence design (antaRNA, INFO-RNA, SECISDesign), structure aberration evaluation of point mutations (RaSE), and RNA/protein-family models visualization (CMV), and other methods. Open education resources offer interactive visualizations of RNA structure and RNA-RNA interaction prediction as well as basic and advanced sequence alignment algorithms. The services are freely available at http://rna.informatik.uni-freiburg.de.

Prediction of lncRNAs and their interactions with nucleic acids: benchmarking bioinformatics tools.

The genomes of mammalian species are pervasively transcribed producing as many noncoding as protein-coding RNAs. There is a growing body of evidence supporting their functional role. Long noncoding RNA (lncRNA) can bind both nucleic acids and proteins through several mechanisms. A reliable computational prediction of the most probable mechanism of lncRNA interaction can facilitate experimental validation of its function. In this study, we benchmarked computational tools capable to discriminate lncRNA from mRNA and predict lncRNA interactions with other nucleic acids. We assessed the performance of 9 tools for distinguishing protein-coding from noncoding RNAs, as well as 19 tools for prediction of RNA-RNA and RNA-DNA interactions. Our conclusions about the considered tools were based on their performances on the entire genome/transcriptome level, as it is the most common task nowadays. We found that FEELnc and CPAT distinguish between coding and noncoding mammalian transcripts in the most accurate manner. ASSA, RIBlast and LASTAL, as well as Triplexator, turned out to be the best predictors of RNA-RNA and RNA-DNA interactions, respectively. We showed that the normalization of the predicted interaction strength to the transcript length and GC content may improve the accuracy of inferring RNA interactions. Yet, all the current tools have difficulties to make accurate predictions of short-trans RNA-RNA interactions-stretches of sparse contacts. All over, there is still room for improvement in each category, especially for predictions of RNA interactions.

Structure and Interaction Prediction in Prokaryotic RNA Biology.

Many years of research in RNA biology have soundly established the importance of RNA-based regulation far beyond most early traditional presumptions. Importantly, the advances in "wet" laboratory techniques have produced unprecedented amounts of data that require efficient and precise computational analysis schemes and algorithms. Hence, many in silico methods that attempt topological and functional classification of novel putative RNA-based regulators are available. In this review, we technically outline thermodynamics-based standard RNA secondary structure and RNA-RNA interaction prediction approaches that have proven valuable to the RNA research community in the past and present. For these, we highlight their usability with a special focus on prokaryotic organisms and also briefly mention recent advances in whole-genome interactomics and how this may influence the field of predictive RNA research.

Prediction and Depiction of Potential RNA-Based Therapeutics for Oncogenic E6 and E7 Genes of Human Papilloma Virus Types 16 & 18: A New Class of Treatment for Lung Cancer

Unlike almost all the cervical, penile, vulvar, and anal cancers, where Human papilloma virus has long known to play a vital role, a causative link between carcinogenic Human papilloma virus and lung cancer have been found to be highly variable and contradictory. Data also shows geography and race-dependenty. Apart from etiological factors, viral carcinogen can manipulate the cell cycle, hamper cell apoptosis and also interrupt the cell division in host cell which lead to the lung cancer. Molecular studies of carcinogenic Human papilloma virus have found that E6/E7 acts as mitotic mutators which play an important role in pathogenicity and oncogenicity. Analysis of genome sequence of Human papilloma virus revealed that ORF having conserved in early region, E6 and E7 required for viral pathogenicity and oncogenicity can be the suitable target for RNAi technology. RNAi works by silencing or turning off gene expression to control pathogenicity and oncogenicity by blocking its replication processes. Therefore, the work is done on the basis of rational siRNA designing method by targeting viral oncogenic E6 and E7 genes of Human papilloma virus types16 & 18. Searching siRNA target sequences, multiple sequence alignment, forecasting secondary structure and RNA-RNA interaction prediction was done by various computational software tools for designing RNA-based therapeutics (siRNA). In this study, four effective siRNA were predicted rationally for oncogenic E6 and E7 genes of Human papilloma virus types 16 & 18 which might be used as a potential RNA based therapeutics to control the rate of carcinogenesis and degree of oncogenicity. The outcome of this study provides a basis of the researchers towards understating to development of RNA-based therapeutics (siRNA) at genomic level.

RIsearch2: suffix array-based large-scale prediction of RNA-RNA interactions and siRNA off-targets.

Intermolecular interactions of ncRNAs are at the core of gene regulation events, and identifying the full map of these interactions bears crucial importance for ncRNA functional studies. It is known that RNA–RNA interactions are built up by complementary base pairings between interacting RNAs and high level of complementarity between two RNA sequences is a powerful predictor of such interactions. Here, we present RIsearch2, a large-scale RNA–RNA interaction prediction tool that enables quick localization of potential near-complementary RNA–RNA interactions between given query and target sequences. In contrast to previous heuristics which either search for exact matches while including G−U wobble pairs or employ simplified energy models, we present a novel approach using a single integrated seed-and-extend framework based on suffix arrays. RIsearch2 enables fast discovery of candidate RNA–RNA interactions on genome/transcriptome-wide scale. We furthermore present an siRNA off-target discovery pipeline that not only predicts the off-target transcripts but also computes the off-targeting potential of a given siRNA. This is achieved by combining genome-wide RIsearch2 predictions with target site accessibilities and transcript abundance estimates. We show that this pipeline accurately predicts siRNA off-target interactions and enables off-targeting potential comparisons between different siRNA designs. RIsearch2 and the siRNA off-target discovery pipeline are available as stand-alone software packages from http://rth.dk/resources/risearch.

RNA-RNA interaction prediction using genetic algorithm.

BackgroundRNA-RNA interaction plays an important role in the regulation of gene expression and cell development. In this process, an RNA molecule prohibits the translation of another RNA molecule by establishing stable interactions with it. In the RNA-RNA interaction prediction problem, two RNA sequences are given as inputs and the goal is to find the optimal secondary structure of two RNAs and between them. Some different algorithms have been proposed to predict RNA-RNA interaction structure. However, most of them suffer from high computational time.ResultsIn this paper, we introduce a novel genetic algorithm called GRNAs to predict the RNA-RNA interaction. The proposed algorithm is performed on some standard datasets with appropriate accuracy and lower time complexity in comparison to the other state-of-the-art algorithms. In the proposed algorithm, each individual is a secondary structure of two interacting RNAs. The minimum free energy is considered as a fitness function for each individual. In each generation, the algorithm is converged to find the optimal secondary structure (minimum free energy structure) of two interacting RNAs by using crossover and mutation operations.ConclusionsThis algorithm is properly employed for joint secondary structure prediction. The results achieved on a set of known interacting RNA pairs are compared with the other related algorithms and the effectiveness and validity of the proposed algorithm have been demonstrated. It has been shown that time complexity of the algorithm in each iteration is as efficient as the other approaches.

The RNA Newton polytope and learnability of energy parameters

Motivation: Computational RNA structure prediction is a mature important problem that has received a new wave of attention with the discovery of regulatory non-coding RNAs and the advent of high-throughput transcriptome sequencing. Despite nearly two score years of research on RNA secondary structure and RNA–RNA interaction prediction, the accuracy of the state-of-the-art algorithms are still far from satisfactory. So far, researchers have proposed increasingly complex energy models and improved parameter estimation methods, experimental and/or computational, in anticipation of endowing their methods with enough power to solve the problem. The output has disappointingly been only modest improvements, not matching the expectations. Even recent massively featured machine learning approaches were not able to break the barrier. Why is that?Approach: The first step toward high-accuracy structure prediction is to pick an energy model that is inherently capable of predicting each and every one of known structures to date. In this article, we introduce the notion of learnability of the parameters of an energy model as a measure of such an inherent capability. We say that the parameters of an energy model are learnable iff there exists at least one set of such parameters that renders every known RNA structure to date the minimum free energy structure. We derive a necessary condition for the learnability and give a dynamic programming algorithm to assess it. Our algorithm computes the convex hull of the feature vectors of all feasible structures in the ensemble of a given input sequence. Interestingly, that convex hull coincides with the Newton polytope of the partition function as a polynomial in energy parameters. To the best of our knowledge, this is the first approach toward computing the RNA Newton polytope and a systematic assessment of the inherent capabilities of an energy model. The worst case complexity of our algorithm is exponential in the number of features. However, dimensionality reduction techniques can provide approximate solutions to avoid the curse of dimensionality.Results: We demonstrated the application of our theory to a simple energy model consisting of a weighted count of A-U, C-G and G-U base pairs. Our results show that this simple energy model satisfies the necessary condition for more than half of the input unpseudoknotted sequence–structure pairs (55%) chosen from the RNA STRAND v2.0 database and severely violates the condition for ∼13%, which provide a set of hard cases that require further investigation. From 1350 RNA strands, the observed 3D feature vector for 749 strands is on the surface of the computed polytope. For 289 RNA strands, the observed feature vector is not on the boundary of the polytope but its distance from the boundary is not more than one. A distance of one essentially means one base pair difference between the observed structure and the closest point on the boundary of the polytope, which need not be the feature vector of a structure. For 171 sequences, this distance is larger than two, and for only 11 sequences, this distance is larger than five.Availability: The source code is available on http://compbio.cs.wayne.edu/software/rna-newton-polytope.Contact: chitsaz@wayne.edu

How to do RNA-RNA Interaction Prediction? A Use-Case Driven Handbook Using IntaRNA.

Computational prediction of RNA-RNA interactions (RRI) is a central methodology for the specific investigation of inter-molecular RNA interactions and regulatory effects of non-coding RNAs like eukaryotic microRNAs or prokaryotic small RNAs. Available methods can be classified according to their underlying prediction strategies, each implicating specific capabilities and restrictions often not transparent to the non-expert user. Within this work, we review seven classes of RRI prediction strategies and discuss the advantages and limitations of respective tools, since such knowledge is essential for selecting the right tool in the first place.Among the RRI prediction strategies, accessibility-based approaches have been shown to provide the most reliable predictions. Here, we describe how IntaRNA, as one of the state-of-the-art accessibility-based tools, can be applied in various use cases for the task of computational RRI prediction. Detailed hands-on examples for individual RRI predictions as well as large-scale target prediction scenarios are provided. We illustrate the flexibility and capabilities of IntaRNA through the examples. Each example is designed using real-life data from the literature and is accompanied by instructions on interpreting the respective results from IntaRNA output. Our use-case driven instructions enable non-expert users to comprehensively understand and utilize IntaRNA's features for effective RRI predictions.