Computational analysis of noncoding RNAs

Trials and Tribulations of MicroRNA Therapeutics

The discovery of the link between microRNAs (miRNAs) and a myriad of human diseases, particularly various cancer types, has generated significant interest in exploring their potential as a novel class of drugs. This has led to substantial investments in interdisciplinary research fields such as biology, chemistry, and medical science for the development of miRNA-based therapies. Furthermore, the recent global success of SARS-CoV-2 mRNA vaccines against the COVID-19 pandemic has further revitalized interest in RNA-based immunotherapies, including miRNA-based approaches to cancer treatment. Consequently, RNA therapeutics have emerged as highly adaptable and modular options for cancer therapy. Moreover, advancements in RNA chemistry and delivery methods have been pivotal in shaping the landscape of RNA-based immunotherapy, including miRNA-based approaches. Consequently, the biotechnology and pharmaceutical industry has witnessed a resurgence of interest in incorporating RNA-based immunotherapies and miRNA therapeutics into their development programs. Despite substantial progress in preclinical research, the field of miRNA-based therapeutics remains in its early stages, with only a few progressing to clinical development, none reaching phase III clinical trials or being approved by the US Food and Drug Administration (FDA), and several facing termination due to toxicity issues. These setbacks highlight existing challenges that must be addressed for the broad clinical application of miRNA-based therapeutics. Key challenges include establishing miRNA sensitivity, specificity, and selectivity towards their intended targets, mitigating immunogenic reactions and off-target effects, developing enhanced methods for targeted delivery, and determining optimal dosing for therapeutic efficacy while minimizing side effects. Additionally, the limited understanding of the precise functions of miRNAs limits their clinical utilization. Moreover, for miRNAs to be viable for cancer treatment, they must be technically and economically feasible for the widespread adoption of RNA therapies. As a result, a thorough risk evaluation of miRNA therapeutics is crucial to minimize off-target effects, prevent overdosing, and address various other issues. Nevertheless, the therapeutic potential of miRNAs for various diseases is evident, and future investigations are essential to determine their applicability in clinical settings.

Circulating microRNAs as Potential Biomarkers in Pancreatic Cancer-Advances and Challenges

There is an urgent unmet need for robust and reliable biomarkers for early diagnosis, prognosis, and prediction of response to specific treatments of many aggressive and deadly cancers, such as pancreatic cancer, and liquid biopsy-based miRNA profiling has the potential for this. MiRNAs are a subset of non-coding RNAs that regulate the expression of a multitude of genes post-transcriptionally and thus are potential diagnostic, prognostic, and predictive biomarkers and have also emerged as potential therapeutics. Because miRNAs are involved in the post-transcriptional regulation of their target mRNAs via repressing gene expression, defects in miRNA biogenesis pathway and miRNA expression perturb the expression of a multitude of oncogenic or tumor-suppressive genes that are involved in the pathogenesis of various cancers. As such, numerous miRNAs have been identified to be downregulated or upregulated in many cancers, functioning as either oncomes or oncosuppressor miRs. Moreover, dysregulation of miRNA biogenesis pathways can also change miRNA expression and function in cancer. Profiling of dysregulated miRNAs in pancreatic cancer has been shown to correlate with disease diagnosis, indicate optimal treatment options and predict response to a specific therapy. Specific miRNA signatures can track the stages of pancreatic cancer and hold potential as diagnostic, prognostic, and predictive markers, as well as therapeutics such as miRNA mimics and miRNA inhibitors (antagomirs). Furthermore, identified specific miRNAs and genes they regulate in pancreatic cancer along with downstream pathways can be used as potential therapeutic targets. However, a limited understanding and validation of the specific roles of miRNAs, lack of tissue specificity, methodological, technical, or analytical reproducibility, harmonization of miRNA isolation and quantification methods, the use of standard operating procedures, and the availability of automated and standardized assays to improve reproducibility between independent studies limit bench-to-bedside translation of the miRNA biomarkers for clinical applications. Here I review recent findings on miRNAs in pancreatic cancer pathogenesis and their potential as diagnostic, prognostic, and predictive markers.

MncR: Late Integration Machine Learning Model for Classification of ncRNA Classes Using Sequence and Structural Encoding

Non-coding RNA (ncRNA) classes take over important housekeeping and regulatory functions and are quite heterogeneous in terms of length, sequence conservation and secondary structure. High-throughput sequencing reveals that the expressed novel ncRNAs and their classification are important to understand cell regulation and identify potential diagnostic and therapeutic biomarkers. To improve the classification of ncRNAs, we investigated different approaches of utilizing primary sequences and secondary structures as well as the late integration of both using machine learning models, including different neural network architectures. As input, we used the newest version of RNAcentral, focusing on six ncRNA classes, including lncRNA, rRNA, tRNA, miRNA, snRNA and snoRNA. The late integration of graph-encoded structural features and primary sequences in our MncR classifier achieved an overall accuracy of >97%, which could not be increased by more fine-grained subclassification. In comparison to the actual best-performing tool ncRDense, we had a minimal increase of 0.5% in all four overlapping ncRNA classes on a similar test set of sequences. In summary, MncR is not only more accurate than current ncRNA prediction tools but also allows the prediction of long ncRNA classes (lncRNAs, certain rRNAs) up to 12.000 nts and is trained on a more diverse ncRNA dataset retrieved from RNAcentral.

Restoring Ravaged Heart: Molecular Mechanisms and Clinical Application of miRNA in Heart Regeneration

Human heart development is a complex and tightly regulated process, conserving proliferation, and multipotency of embryonic cardiovascular progenitors. At terminal stage, progenitor cell type gets suppressed for terminal differentiation and maturation. In the human heart, most cardiomyocytes are terminally differentiated and so have limited proliferation capacity. MicroRNAs (miRNAs) are non-coding single-stranded RNA that regulate gene expression and mRNA silencing at the post-transcriptional level. These miRNAs play a crucial role in numerous biological events, including cardiac development, and cardiomyocyte proliferation. Several cardiac cells specific miRNAs have been discovered. Inhibition or overexpression of these miRNAs could induce cardiac regeneration, cardiac stem cell proliferation and cardiomyocyte proliferation. Clinical application of miRNAs extends to heart failure, wherein the cell cycle arrest of terminally differentiated cardiac cells inhibits the heart regeneration. The regenerative capacity of the myocardium can be enhanced by cardiomyocyte specific miRNAs controlling the cell cycle. In this review, we focus on cardiac-specific miRNAs involved in cardiac regeneration and cardiomyocyte proliferation, and their potential as a new clinical therapy for heart regeneration.

MGcount: a total RNA-seq quantification tool to address multi-mapping and multi-overlapping alignments ambiguity in non-coding transcripts

BACKGROUND

Total-RNA sequencing (total-RNA-seq) allows the simultaneous study of both the coding and the non-coding transcriptome. Yet, computational pipelines have traditionally focused on particular biotypes, making assumptions that are not fullfilled by total-RNA-seq datasets. Transcripts from distinct RNA biotypes vary in length, biogenesis, and function, can overlap in a genomic region, and may be present in the genome with a high copy number. Consequently, reads from total-RNA-seq libraries may cause ambiguous genomic alignments, demanding for flexible quantification approaches.

RESULTS

Here we present Multi-Graph count (MGcount), a total-RNA-seq quantification tool combining two strategies for handling ambiguous alignments. First, MGcount assigns reads hierarchically to small-RNA and long-RNA features to account for length disparity when transcripts overlap in the same genomic position. Next, MGcount aggregates RNA products with similar sequences where reads systematically multi-map using a graph-based approach. MGcount outputs a transcriptomic count matrix compatible with RNA-sequencing downstream analysis pipelines, with both bulk and single-cell resolution, and the graphs that model repeated transcript structures for different biotypes. The software can be used as a python module or as a single-file executable program.

CONCLUSIONS

MGcount is a flexible total-RNA-seq quantification tool that successfully integrates reads that align to multiple genomic locations or that overlap with multiple gene features. Its approach is suitable for the simultaneous estimation of protein-coding, long non-coding and small non-coding transcript concentration, in both precursor and processed forms. Both source code and compiled software are available at https://github.com/hitaandrea/MGcount .

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.

Therapeutic Targeting of MicroRNAs in the Tumor Microenvironment

The tumor-microenvironment (TME) is an amalgamation of various factors derived from malignant cells and infiltrating host cells, including cells of the immune system. One of the important factors of the TME is microRNAs (miRs) that regulate target gene expression at a post transcriptional level. MiRs have been found to be dysregulated in tumor as well as in stromal cells and they emerged as important regulators of tumorigenesis. In fact, miRs regulate almost all hallmarks of cancer, thus making them attractive tools and targets for novel anti-tumoral treatment strategies. Tumor to stroma cell cross-propagation of miRs to regulate protumoral functions has been a salient feature of the TME. MiRs can either act as tumor suppressors or oncogenes (oncomiRs) and both miR mimics as well as miR inhibitors (antimiRs) have been used in preclinical trials to alter cancer and stromal cell phenotypes. Owing to their cascading ability to regulate upstream target genes and their chemical nature, which allows specific pharmacological targeting, miRs are attractive targets for anti-tumor therapy. In this review, we cover a recent update on our understanding of dysregulated miRs in the TME and provide an overview of how these miRs are involved in current cancer-therapeutic approaches from bench to bedside.

RNAknot: A new algorithm for RNA secondary structure prediction based on genetic algorithm and GRASP method

The prediction of the optimal secondary structure for a given RNA sequence represents a challenging computational problem in bioinformatics. This challenge becomes harder especially with the discovery of different pseudoknot classes, which is a complex topology that plays diverse roles in biological processes. Many recent studies have been proposed to predict RNA secondary structure with some pseudoknot classes, but only a few of them have reached satisfying results in terms of both complexity and accuracy. Here we present RNAknot, a new method for predicting RNA secondary structure that contains the following components: stems, hairpin loops, multi-branched loops or multi-loops, bulge loops, and internal loops, in addition to two types of pseudoknots, H-type pseudoknot and Hairpin kissing. RNAknot is based on a genetic algorithm and Greedy Randomized Adaptive Search Procedure (GRASP), and it uses the free energy as fitness function to evaluate the obtained structures. In order to validate the performance of the presented method 131 tests have been performed using two datasets of 26 and 105 RNA sequences, which have been taken from the two data bases RNAstrand and Pseudobase respectively. The obtained results are compared with those of some RNA secondary structure prediction programs such as Vs_subopt, CyloFold, IPknot, Kinefold, RNAstructure, and Sfold. The results of this comparative study show that the prediction accuracy of our proposed approach is significantly improved compared to those obtained by the other programs. For the first dataset, RNAknot has the highest specificity (SP) (71.23%) and sensitivity (SN) (72.15%) averages compared to the other programs. Concerning the second dataset, the RNA secondary structure predictions obtained by the RNAknot correspond to the highest averages of SP (85.49%) and F-measure (79.97%) compared to the other programs. The program is available as a jar file in the link: www.bachmek.umi.ac.ma/wp-content/uploads/RNAknot.0.0.2.rar.

SMRT sequencing of the full-length transcriptome of the Rhynchophorus ferrugineus (Coleoptera: Curculionidae)

Background

Red palm weevil Rhynchophorus ferrugineus (Coleoptera: Curculionidae) is one of the most destructive insects for palm trees in the world. However, its genome resources are still in the blank stage, which limits the study of molecular and growth development analysis.

Methods

In this study, we used PacBio Iso-Seq and Illumina RNA-seq to first generate transcriptome from three developmental stages of R. ferrugineus (pupa, 7th larva, female and male) to increase our understanding of the life cycle and molecular characteristics of R. ferrugineus.

Results

A total of 63,801 nonredundant full-length transcripts were generated with an average length of 2,964 bp from three developmental stages, including the 7th instar larva, pupa, female adult and male adult. These transcripts showed a high annotation rate in seven public databases, with 54,999 (86.20%) successfully annotated. Meanwhile, 2,184 alternative splicing (AS) events, 2,084 transcription factors (TFs), 66,230 simple sequence repeats (SSR) and 9,618 Long noncoding RNAs (lncRNAs) were identified. In summary, our results provide a new source of full-length transcriptional data and information for the further study of gene expression and genetics in R. ferrugineus.

The rare lncRNA GOLLD is widespread and structurally conserved among Mycobacterium tRNA arrays

Noncoding RNA (ncRNA) genes produce transcripts involved in a wide range of functions, including catalytic and regulatory functions. Besides, some transcripts have highly complex structures that may impact their activities. Among the largest bacterial ncRNAs, there is the rare GOLLD RNA, which is associated with tRNA genes and supposed to be chromosome- and phage-encoded in specialized groups of bacteria, including those from Lactobacillales and Actinomycetales orders. The only GOLLD structure was inferred from a variety of sequences, including many marine metagenomes. To explore GOLLD RNA in bacterial genomes, we mined the GOLLD gene in thousands of Mycobacterium and virus genomes using Infernal software. We identified this gene in 350 mycobacteria, including megaplasmids, and 39 bacteriophages, mainly in the genomic context of tRNA arrays. Mycobacterium GOLLD genes presented a high diversity and were distributed in three phylogenetic groups: (i) Mycobacterium exclusive; (ii) Mycobacterium and mycobacteriophages; and (iii) mycobacteriophage exclusive. We also determined the GOLLD secondary structure of each group using R2 R software based on GOLLD alignments generated by Infernal software. All GOLLD groups displayed a 3' half conserved structure, including utter E-loops pseudoknots substructures, also shared by non-Mycobacterium GOLLD while the 5' half motif was different among the groups. Here, we showed that the lncRNA GOLLD is widespread in Mycobacterium within tRNA arrays and corroborated the previously predicted GOLLD secondary structure.

Developing and Characterization of Chemically Modified RNA Aptamers for Targeting Wild Type and Mutated c-KIT Receptor Tyrosine Kinases

The c-KIT receptor represents an attractive target for cancer therapy. Aptamers are emerging as a new promising class of nucleic acid therapeutics. In this study, a conventional SELEX approach was applied against the kinase domain of a group of c-KIT proteins (c-KIT^WT, c-KIT^D816V, and c-KIT^D816H) to select aptamers from a random RNA pool that can bind to the kinase domain of each target with high affinity and can selectively interfere with their kinase activities. Interestingly, our data indicated that one candidate aptamer, called V15, can specifically inhibit the in vitro kinase activity of mutant c-KIT^D816V with an IC₅₀ value that is 9-fold more potent than the sunitinib drug tested under the same conditions. Another aptamer, named as H5/V36, showed the potential to distinguish between the c-KIT kinases by modulating the phosphorylation activity of each in a distinct mechanism of action and in a different potency.

LinearFold: linear-time approximate RNA folding by 5'-to-3' dynamic programming and beam search

MOTIVATION

Predicting the secondary structure of an ribonucleic acid (RNA) sequence is useful in many applications. Existing algorithms [based on dynamic programming] suffer from a major limitation: their runtimes scale cubically with the RNA length, and this slowness limits their use in genome-wide applications.

RESULTS

We present a novel alternative O(n3)-time dynamic programming algorithm for RNA folding that is amenable to heuristics that make it run in O(n) time and O(n) space, while producing a high-quality approximation to the optimal solution. Inspired by incremental parsing for context-free grammars in computational linguistics, our alternative dynamic programming algorithm scans the sequence in a left-to-right (5'-to-3') direction rather than in a bottom-up fashion, which allows us to employ the effective beam pruning heuristic. Our work, though inexact, is the first RNA folding algorithm to achieve linear runtime (and linear space) without imposing constraints on the output structure. Surprisingly, our approximate search results in even higher overall accuracy on a diverse database of sequences with known structures. More interestingly, it leads to significantly more accurate predictions on the longest sequence families in that database (16S and 23S Ribosomal RNAs), as well as improved accuracies for long-range base pairs (500+ nucleotides apart), both of which are well known to be challenging for the current models.

AVAILABILITY AND IMPLEMENTATION

Our source code is available at https://github.com/LinearFold/LinearFold, and our webserver is at http://linearfold.org (sequence limit: 100 000nt).

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Identification, Prediction and Data Analysis of Noncoding RNAs: A Review

BACKGROUND

Noncoding RNAs (ncRNAs) which play an important role in various cellular processes are important in medicine as well as in drug design strategies. Different studies have shown that ncRNAs are dis-regulated in cancer cells and play an important role in human tumorigenesis. Therefore, it is important to identify and predict such molecules by experimental and computational methods, respectively. However, to avoid expensive experimental methods, computational algorithms have been developed for accurately and fast prediction of ncRNAs.

OBJECTIVE

The aim of this review was to introduce the experimental and computational methods to identify and predict ncRNAs structure. Also, we explained the ncRNA's roles in cellular processes and drugs design, briefly.

METHOD

In this survey, we will introduce ncRNAs and their roles in biological and medicinal processes. Then, some important laboratory techniques will be studied to identify ncRNAs. Finally, the state-of-the-art models and algorithms will be introduced along with important tools and databases.

RESULTS

The results showed that the integration of experimental and computational approaches improves to identify ncRNAs. Moreover, the high accurate databases, algorithms and tools were compared to predict the ncRNAs.

CONCLUSION

ncRNAs prediction is an exciting research field, but there are different difficulties. It requires accurate and reliable algorithms and tools. Also, it should be mentioned that computational costs of such algorithm including running time and usage memory are very important. Finally, some suggestions were presented to improve computational methods of ncRNAs gene and structural prediction.

The Potential for microRNA Therapeutics and Clinical Research

As FDA-approved small RNA drugs start to enter clinical medicine, ongoing studies for the microRNA (miRNA) class of small RNAs expand its preclinical and clinical research applications. A growing number of reports suggest a significant utility of miRNAs as biomarkers for pathogenic conditions, modulators of drug resistance, and/or as drugs for medical intervention in almost all human health conditions. The pleiotropic nature of this class of nonprotein-coding RNAs makes them particularly attractive drug targets for diseases with a multifactorial origin and no current effective treatments. As candidate miRNAs begin to proceed toward initiation and completion of potential phase 3 and 4 trials in the future, the landscape of both diagnostic and interventional medicine will arguably continue to evolve. In this mini-review, we discuss miRNA drug discovery development and their current status in clinical trials.

LncRNA secondary structure in the cardiovascular system

Long non-coding RNAs (lncRNAs) have been increasingly studied during the past decade. This led to an immense number of annotated transcripts, out of which many were linked to a diverse range of biological mechanisms and diseases. Due to the variety of their regulatory potential, they are seen as an important link in understanding complex epigenetic mechanisms. Prominent examples of lncRNAs in the cardiovascular system are ANRIL, Braveheart, MALAT1 and HOTAIR which have been excessively studied. But despite the impressive number of described transcripts, only a few examples are characterized functionally. One way to do this is to identify accessible structural domains in the RNA secondary structure which have the ability to bind to DNA, RNA or proteins. Through recent improvements in computational as well as experimental methods, this exploration of secondary structure became not only more efficient than traditional methods like crystallization, but also feasible to investigate whole genome RNA structures. The purpose of this review is to highlight the recent advances in secondary structure probing methods and how these can be applied in order to investigate the functional roles of lncRNAs in the cardiovascular system.

Physico-chemical fingerprinting of RNA genes

We advance here a novel concept for characterizing different classes of RNA genes on the basis of physico-chemical properties of DNA sequences. As knowledge-based approaches could yield unsatisfactory outcomes due to limitations of training on available experimental data sets, alternative approaches that utilize properties intrinsic to DNA are needed to supplement training based methods and to eventually provide molecular insights into genome organization. Based on a comprehensive series of molecular dynamics simulations of Ascona B-DNA consortium, we extracted hydrogen bonding, stacking and solvation energies of all combinations of DNA sequences at the dinucleotide level and calculated these properties for different types of RNA genes. Considering ∼7.3 million mRNA, 255 524 tRNA, 40 649 rRNA (different subunits) and 5250 miRNA, 3747 snRNA, gene sequences from 9282 complete genome chromosomes of all prokaryotes and eukaryotes available at NCBI, we observed that physico-chemical properties of different functional units on genomic DNA differ in their signatures.

Prediction of bacterial small RNAs in the RsmA (CsrA) and ToxT pathways: a machine learning approach

BACKGROUND

Small RNAs (sRNAs) constitute an important class of post-transcriptional regulators that control critical cellular processes in bacteria. Recent research using high-throughput transcriptomic approaches has led to a dramatic increase in the discovery of bacterial sRNAs. However, it is generally believed that the currently identified sRNAs constitute a limited subset of the bacterial sRNA repertoire. In several cases, sRNAs belonging to a specific class are already known and the challenge is to identify additional sRNAs belonging to the same class. In such cases, machine-learning approaches can be used to predict novel sRNAs in a given class.

METHODS

In this work, we develop novel bioinformatics approaches that integrate sequence and structure-based features to train machine-learning models for the discovery of bacterial sRNAs. We show that features derived from recurrent structural motifs in the ensemble of low energy secondary structures can distinguish the RNA classes with high accuracy.

RESULTS

We apply this approach to predict new members in two broad classes of bacterial small RNAs: 1) sRNAs that bind to the RNA-binding protein RsmA/CsrA in diverse bacterial species and 2) sRNAs regulated by the master regulator of virulence, ToxT, in Vibrio cholerae.

CONCLUSION

The involvement of sRNAs in bacterial adaptation to changing environments is an increasingly recurring theme in current research in microbiology. It is likely that future research, combining experimental and computational approaches, will discover many more examples of sRNAs as components of critical regulatory pathways in bacteria. We have developed a novel approach for prediction of small RNA regulators in important bacterial pathways. This approach can be applied to specific classes of sRNAs for which several members have been identified and the challenge is to identify additional sRNAs.

Comparative genomics of microsporidian genomes reveals a minimal non-coding RNA set and new insights for transcription in minimal eukaryotic genomes

Microsporidia are ubiquitous intracellular pathogens whose opportunistic nature led to their increased recognition with the rise of the AIDS pandemic. As the RNA world was largely unexplored in this parasitic lineage, we developed a dedicated in silico methodology to carry out exhaustive identification of ncRNAs across the Encephalitozoon and Nosema genera. Thus, the previously missing U1 small nuclear RNA (snRNA) and small nucleolar RNAs (snoRNAs) targeting only the LSU rRNA were highlighted and were further validated using 5' and 3'RACE-PCR experiments. Overall, the 15 ncRNAs that were found shared between Encephalitozoon and Nosema spp. may represent the minimal core set required for parasitic life. Interestingly, the systematic presence of a CCC- or GGG-like motif in 5' of all ncRNA and mRNA gene transcripts regardless of the RNA polymerase involved suggests that the RNA polymerase machineries in microsporidia species could use common factors. Our data provide additional insights in accordance with the simplification processes observed in these reduce genomes and underline the usefulness of sequencing closely related species to help identify highly divergent ncRNAs in these parasites.

An improved method for identification of small non-coding RNAs in bacteria using support vector machine

Bacterial small non-coding RNAs (sRNAs) are not translated into proteins, but act as functional RNAs. They are involved in diverse biological processes like virulence, stress response and quorum sensing. Several high-throughput techniques have enabled identification of sRNAs in bacteria, but experimental detection remains a challenge and grossly incomplete for most species. Thus, there is a need to develop computational tools to predict bacterial sRNAs. Here, we propose a computational method to identify sRNAs in bacteria using support vector machine (SVM) classifier. The primary sequence and secondary structure features of experimentally-validated sRNAs of Salmonella Typhimurium LT2 (SLT2) was used to build the optimal SVM model. We found that a tri-nucleotide composition feature of sRNAs achieved an accuracy of 88.35% for SLT2. We validated the SVM model also on the experimentally-detected sRNAs of E. coli and Salmonella Typhi. The proposed model had robustly attained an accuracy of 81.25% and 88.82% for E. coli K-12 and S. Typhi Ty2, respectively. We confirmed that this method significantly improved the identification of sRNAs in bacteria. Furthermore, we used a sliding window-based method and identified sRNAs from complete genomes of SLT2, S. Typhi Ty2 and E. coli K-12 with sensitivities of 89.09%, 83.33% and 67.39%, respectively.

Measuring RNA structure transcriptome-wide with icSHAPE

RNA molecules can be found at the heart of nearly every aspect of gene regulation: from gene expression to protein translation. The ability of RNA molecules to fold into intricate structures guides their function. Chemical methods to measure RNA structure have been part of the RNA biologists toolkit for several decades. These methods, although often cumbersome and difficult to perform on large RNAs, are notable for their accuracy and precision of structural measurements. Recent extension of these methods to transcriptome-wide analyses has opened the door to interrogating the structure of complete RNA molecules inside cells. Within this manuscript we describe the biochemical basis for the methodology behind a novel technology, icSHAPE, which measures RNA flexibility and single-strandedness in RNA. Novel methods such as icSHAPE have greatly expanded our understanding of RNA function and have paved the way to expansive analyses of large groups of RNA structures as they function inside the native environment of the cell.

Novel Approach to Analyzing MFE of Noncoding RNA Sequences

Genomic studies have become noncoding RNA (ncRNA) centric after the study of different genomes provided enormous information on ncRNA over the past decades. The function of ncRNA is decided by its secondary structure, and across organisms, the secondary structure is more conserved than the sequence itself. In this study, the optimal secondary structure or the minimum free energy (MFE) structure of ncRNA was found based on the thermodynamic nearest neighbor model. MFE of over 2600 ncRNA sequences was analyzed in view of its signal properties. Mathematical models linking MFE to the signal properties were found for each of the four classes of ncRNA analyzed. MFE values computed with the proposed models were in concordance with those obtained with the standard web servers. A total of 95% of the sequences analyzed had deviation of MFE values within ±15% relative to those obtained from standard web servers.

RNAlien - Unsupervised RNA family model construction

Determining the function of a non-coding RNA requires costly and time-consuming wet-lab experiments. For this reason, computational methods which ascertain the homology of a sequence and thereby deduce functionality and family membership are often exploited. In this fashion, newly sequenced genomes can be annotated in a completely computational way. Covariance models are commonly used to assign novel RNA sequences to a known RNA family. However, to construct such models several examples of the family have to be already known. Moreover, model building is the work of experts who manually edit the necessary RNA alignment and consensus structure. Our method, RNAlien, starting from a single input sequence collects potential family member sequences by multiple iterations of homology search. RNA family models are fully automatically constructed for the found sequences. We have tested our method on a subset of the Rfam RNA family database. RNAlien models are a starting point to construct models of comparable sensitivity and specificity to manually curated ones from the Rfam database. RNAlien Tool and web server are available at http://rna.tbi.univie.ac.at/rnalien/.

RCK: accurate and efficient inference of sequence- and structure-based protein-RNA binding models from RNAcompete data

MOTIVATION

Protein-RNA interactions, which play vital roles in many processes, are mediated through both RNA sequence and structure. CLIP-based methods, which measure protein-RNA binding in vivo, suffer from experimental noise and systematic biases, whereas in vitro experiments capture a clearer signal of protein RNA-binding. Among them, RNAcompete provides binding affinities of a specific protein to more than 240 000 unstructured RNA probes in one experiment. The computational challenge is to infer RNA structure- and sequence-based binding models from these data. The state-of-the-art in sequence models, Deepbind, does not model structural preferences. RNAcontext models both sequence and structure preferences, but is outperformed by GraphProt. Unfortunately, GraphProt cannot detect structural preferences from RNAcompete data due to the unstructured nature of the data, as noted by its developers, nor can it be tractably run on the full RNACompete dataset.

RESULTS

We develop RCK, an efficient, scalable algorithm that infers both sequence and structure preferences based on a new k-mer based model. Remarkably, even though RNAcompete data is designed to be unstructured, RCK can still learn structural preferences from it. RCK significantly outperforms both RNAcontext and Deepbind in in vitro binding prediction for 244 RNAcompete experiments. Moreover, RCK is also faster and uses less memory, which enables scalability. While currently on par with existing methods in in vivo binding prediction on a small scale test, we demonstrate that RCK will increasingly benefit from experimentally measured RNA structure profiles as compared to computationally predicted ones. By running RCK on the entire RNAcompete dataset, we generate and provide as a resource a set of protein-RNA structure-based models on an unprecedented scale.

AVAILABILITY AND IMPLEMENTATION

Software and models are freely available at http://rck.csail.mit.edu/

CONTACT

bab@mit.edu

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

De Novo Transcriptome Analysis of Medicinally Important Plantago ovata Using RNA-Seq

Plantago ovata is an economically and medicinally important plant of the family Plantaginaceae. It is used extensively for the production of seed husk for its application in pharmaceutical, food and cosmetic industries. In the present study, the transcriptome of P. ovata ovary was sequenced using Illumina Genome Analyzer platform to characterize the mucilage biosynthesis pathway in the plant. De novo assembly was carried out using Oases followed by velvet. A total of 46,955 non-redundant transcripts (≥100 bp) using ~29 million high-quality paired end reads were generated. Functional categorization of these transcripts revealed the presence of several genes involved in various biological processes like metabolic pathways, mucilage biosynthesis, biosynthesis of secondary metabolites and antioxidants. In addition, simple sequence-repeat motifs, non-coding RNAs and transcription factors were also identified. Expression profiling of some genes involved in mucilage biosynthetic pathway was performed in different tissues of P. ovata using Real time PCR analysis. The study has resulted in a valuable resource for further studies on gene expression, genomics and functional genomics in P. ovata.

Progress and challenges for chemical probing of RNA structure inside living cells

Proper gene expression is essential for the survival of every cell. Once thought to be a passive transporter of genetic information, RNA has recently emerged as a key player in nearly every pathway in the cell. A full description of its structure is critical to understanding RNA function. Decades of research have focused on utilizing chemical tools to interrogate the structures of RNAs, with recent focus shifting to performing experiments inside living cells. This Review will detail the design and utility of chemical reagents used in RNA structure probing. We also outline how these reagents have been used to gain a deeper understanding of RNA structure in vivo. We review the recent merger of chemical probing with deep sequencing. Finally, we outline some of the hurdles that remain in fully characterizing the structure of RNA inside living cells, and how chemical biology can uniquely tackle such challenges.

RNA motif discovery: a computational overview

Genomic studies have greatly expanded our knowledge of structural non-coding RNAs (ncRNAs). These RNAs fold into characteristic secondary structures and perform specific-structure dependent biological functions. Hence RNA secondary structure prediction is one of the most well studied problems in computational RNA biology. Comparative sequence analysis is one of the more reliable RNA structure prediction approaches as it exploits information of multiple related sequences to infer the consensus secondary structure. This class of methods essentially learns a global secondary structure from the input sequences. In this paper, we consider the more general problem of unearthing common local secondary structure based patterns from a set of related sequences. The input sequences for example could correspond to 3(') or 5(') untranslated regions of a set of orthologous genes and the unearthed local patterns could correspond to regulatory motifs found in these regions. These sequences could also correspond to in vitro selected RNA, genomic segments housing ncRNA genes from the same family and so on. Here, we give a detailed review of the various computational techniques proposed in literature attempting to solve this general motif discovery problem. We also give empirical comparisons of some of the current state of the art methods and point out future directions of research.

The Prediction and Validation of Small CDSs Expand the Gene Repertoire of the Smallest Known Eukaryotic Genomes

The proper prediction of the gene catalogue of an organism is essential to obtain a representative snapshot of its overall lifestyle, especially when it is not amenable to culturing. Microsporidia are obligate intracellular, sometimes hard to culture, eukaryotic parasites known to infect members of every animal phylum. To date, sequencing and annotation of microsporidian genomes have revealed a poor gene complement with highly reduced gene sizes. In the present paper, we investigated whether such gene sizes may have induced biases for the methodologies used for genome annotation, with an emphasis on small coding sequence (CDS) gene prediction. Using better delineated intergenic regions from four Encephalitozoon genomes, we predicted de novo new small CDSs with sizes ranging from 78 to 255 bp (median 168) and corroborated these predictions by RACE-PCR experiments in Encephalitozoon cuniculi. Most of the newly found genes are present in other distantly related microsporidian species, suggesting their biological relevance. The present study provides a better framework for annotating microsporidian genomes and to train and evaluate new computational methods dedicated at detecting ultra-small genes in various organisms.

In silico discovery and modeling of non-coding RNA structure in viruses

This review covers several computational methods for discovering structured non-coding RNAs in viruses and modeling their putative secondary structures. Here we will use examples from two target viruses to highlight these approaches: influenza A virus-a relatively small, segmented RNA virus; and Epstein-Barr virus-a relatively large DNA virus with a complex transcriptome. Each system has unique challenges to overcome and unique characteristics to exploit. From these particular cases, generically useful approaches can be derived for the study of additional viral targets.

SHAPE Selection (SHAPES) enrich for RNA structure signal in SHAPE sequencing-based probing data

Selective 2' Hydroxyl Acylation analyzed by Primer Extension (SHAPE) is an accurate method for probing of RNA secondary structure. In existing SHAPE methods, the SHAPE probing signal is normalized to a no-reagent control to correct for the background caused by premature termination of the reverse transcriptase. Here, we introduce a SHAPE Selection (SHAPES) reagent, N-propanone isatoic anhydride (NPIA), which retains the ability of SHAPE reagents to accurately probe RNA structure, but also allows covalent coupling between the SHAPES reagent and a biotin molecule. We demonstrate that SHAPES-based selection of cDNA-RNA hybrids on streptavidin beads effectively removes the large majority of background signal present in SHAPE probing data and that sequencing-based SHAPES data contain the same amount of RNA structure data as regular sequencing-based SHAPE data obtained through normalization to a no-reagent control. Moreover, the selection efficiently enriches for probed RNAs, suggesting that the SHAPES strategy will be useful for applications with high-background and low-probing signal such as in vivo RNA structure probing.

Laser capture microdissection: Big data from small samples

Any tissue is made up of a heterogeneous mix of spatially distributed cell types. In response to any (patho) physiological cue, responses of each cell type in any given tissue may be unique and cannot be homogenized across cell-types and spatial co-ordinates. For example, in response to myocardial infarction, on one hand myocytes and fibroblasts of the heart tissue respond differently. On the other hand, myocytes in the infarct core respond differently compared to those in the peri-infarct zone. Therefore, isolation of pure targeted cells is an important and essential step for the molecular analysis of cells involved in the progression of disease. Laser capture microdissection (LCM) is powerful to obtain a pure targeted cell subgroup, or even a single cell, quickly and precisely under the microscope, successfully tackling the problem of tissue heterogeneity in molecular analysis. This review presents an overview of LCM technology, the principles, advantages and limitations and its down-stream applications in the fields of proteomics, genomics and transcriptomics. With powerful technologies and appropriate applications, this technique provides unprecedented insights into cell biology from cells grown in their natural tissue habitat as opposed to those cultured in artificial petri dish conditions.

Genome-wide transcript profiling reveals novel breast cancer-associated intronic sense RNAs

Non-coding RNAs (ncRNAs) play major roles in development and cancer progression. To identify novel ncRNAs that may identify key pathways in breast cancer development, we performed high-throughput transcript profiling of tumor and normal matched-pair tissue samples. Initial transcriptome profiling using high-density genome-wide tiling arrays revealed changes in over 200 novel candidate genomic regions that map to intronic regions. Sixteen genomic loci were identified that map to the long introns of five key protein-coding genes, CRIM1, EPAS1, ZEB2, RBMS1, and RFX2. Consistent with the known role of the tumor suppressor ZEB2 in the cancer-associated epithelial to mesenchymal transition (EMT), in situ hybridization reveals that the intronic regions deriving from ZEB2 as well as those from RFX2 and EPAS1 are down-regulated in cells of epithelial morphology, suggesting that these regions may be important for maintaining normal epithelial cell morphology. Paired-end deep sequencing analysis reveals a large number of distinct genomic clusters with no coding potential within the introns of these genes. These novel transcripts are only transcribed from the coding strand. A comprehensive search for breast cancer associated genes reveals enrichment for transcribed intronic regions from these loci, pointing to an underappreciated role of introns or mechanisms relating to their biology in EMT and breast cancer.

Signal transduction: From the atomic age to the post-genomic era

We have come a long way in the 55 years since Edmond Fischer and the late Edwin Krebs discovered that the activity of glycogen phosphorylase is regulated by reversible protein phosphorylation. Many of the fundamental molecular mechanisms that operate in biological signaling have since been characterized and the vast web of interconnected pathways that make up the cellular signaling network has been mapped in considerable detail. Nonetheless, it is important to consider how fast this field is still moving and the issues at the current boundaries of our understanding. One must also appreciate what experimental strategies have allowed us to attain our present level of knowledge. We summarize here some key issues (both conceptual and methodological), raise unresolved questions, discuss potential pitfalls, and highlight areas in which our understanding is still rudimentary. We hope these wide-ranging ruminations will be useful to investigators who carry studies of signal transduction forward during the rest of the 21st century.

Hantavirus immunology of rodent reservoirs: current status and future directions

Hantaviruses are hosted by rodents, insectivores and bats. Several rodent-borne hantaviruses cause two diseases that share many features in humans, hemorrhagic fever with renal syndrome in Eurasia or hantavirus cardiopulmonary syndrome in the Americas. It is thought that the immune response plays a significant contributory role in these diseases. However, in reservoir hosts that have been closely examined, little or no pathology occurs and infection is persistent despite evidence of adaptive immune responses. Because most hantavirus reservoirs are not model organisms, it is difficult to conduct meaningful experiments that might shed light on how the viruses evade sterilizing immune responses and why immunopathology does not occur. Despite these limitations, recent advances in instrumentation and bioinformatics will have a dramatic impact on understanding reservoir host responses to hantaviruses by employing a systems biology approach to identify important pathways that mediate virus/reservoir relationships.

Specific RNA-binding antibodies with a four-amino-acid code

Numerous large non-coding RNAs are rapidly being discovered, and many of them have been shown to play vital roles in gene expression, gene regulation, and human diseases. Given their often structured nature, specific recognition with an antibody fragment becomes feasible and may help define the structure and function of these non-coding RNAs. As demonstrated for protein antigens, specific antibodies may aid in RNA crystal structure elucidation or the development of diagnostic tools and therapeutic drugs targeting disease-causing RNAs. Recent success and limitation of RNA antibody development has made it imperative to generate an effective antibody library specifically targeting RNA molecules. Adopting the reduced chemical diversity design and further restricting the interface diversity to tyrosines, serines, glycines, and arginines only, we have constructed a RNA-targeting Fab library. Phage display selection and downstream characterization showed that this library yielded high-affinity Fabs for all three RNA targets tested. Using a quantitative specificity assay, we found that these Fabs are highly specific, possibly due to the alternate codon design we used to avoid consecutive arginines in the Fab interface. In addition, the effectiveness of the minimal Fab library may challenge our view of the protein-RNA binding interface and provide a unique solution for future design of RNA-binding proteins.

NanoCAGE analysis of the mouse olfactory epithelium identifies the expression of vomeronasal receptors and of proximal LINE elements

By coupling laser capture microdissection to nanoCAGE technology and next-generation sequencing we have identified the genome-wide collection of active promoters in the mouse Main Olfactory Epithelium (MOE). Transcription start sites (TSSs) for the large majority of Olfactory Receptors (ORs) have been previously mapped increasing our understanding of their promoter architecture. Here we show that in our nanoCAGE libraries of the mouse MOE we detect a large number of tags mapped in loci hosting Type-1 and Type-2 Vomeronasal Receptors genes (V1Rs and V2Rs). These loci also show a massive expression of Long Interspersed Nuclear Elements (LINEs). We have validated the expression of selected receptors detected by nanoCAGE with in situ hybridization, RT-PCR and qRT-PCR. This work extends the repertory of receptors capable of sensing chemical signals in the MOE, suggesting intriguing interplays between MOE and VNO for pheromone processing and positioning transcribed LINEs as candidate regulatory RNAs for VRs expression.

Sebnif: an integrated bioinformatics pipeline for the identification of novel large intergenic noncoding RNAs (lincRNAs)--application in human skeletal muscle cells

Ab initio assembly of transcriptome sequencing data has been widely used to identify large intergenic non-coding RNAs (lincRNAs), a novel class of gene regulators involved in many biological processes. To differentiate real lincRNA transcripts from thousands of assembly artifacts, a series of filtering steps such as filters of transcript length, expression level and coding potential, need to be applied. However, an easy-to-use and publicly available bioinformatics pipeline that integrates these filters is not yet available. Hence, we implemented sebnif, an integrative bioinformatics pipeline to facilitate the discovery of bona fide novel lincRNAs that are suitable for further functional characterization. Specifically, sebnif is the only pipeline that implements an algorithm for identifying high-quality single-exonic lincRNAs that were often omitted in many studies. To demonstrate the usage of sebnif, we applied it on a real biological RNA-seq dataset from Human Skeletal Muscle Cells (HSkMC) and built a novel lincRNA catalog containing 917 highly reliable lincRNAs. Sebnif is available at http://sunlab.lihs.cuhk.edu.hk/sebnif/.

De novo discovery of structured ncRNA motifs in genomic sequences

De novo discovery of "motifs" capturing the commonalities among related noncoding ncRNA structured RNAs is among the most difficult problems in computational biology. This chapter outlines the challenges presented by this problem, together with some approaches towards solving them, with an emphasis on an approach based on the CMfinder CMfinder program as a case study. Applications to genomic screens for novel de novo structured ncRNA ncRNA s, including structured RNA elements in untranslated portions of protein-coding genes, are presented.

Evidence for a crucial role of a host non-coding RNA in influenza A virus replication

A growing body of evidence suggests the non-protein coding human genome is of vital importance for human cell function. Besides small RNAs, the diverse class of long non-coding RNAs (lncRNAs) recently came into focus. However, their relevance for infection, a major evolutionary driving force, remains elusive. Using two commercially available microarray systems, namely NCode™ and Sureprint™ G3, we identified differential expression of 42 ncRNAs during influenza A virus (IAV) infection in human lung epithelial cells. This included several classes of lncRNAs, including large intergenic ncRNAs (lincRNAs). As analyzed by qRT-PCR, expression of one lincRNA, which we termed virus inducible lincRNA (VIN), is induced by several IAV strains (H1N1, H3N2, H7N7) as well as vesicular stomatitis virus. However, we did not observe an induction of VIN by influenza B virus, treatment with RNA mimics, or IFNβ. Thus, VIN expression seems to be a specific response to certain viral infections. RNA fractionation and RNA-FISH experiments revealed that VIN is localized to the host cell nucleus. Most importantly, we show that abolition of VIN by RNA interference restricts IAV replication and viral protein synthesis, highlighting the relevance of this lincRNA for productive IAV infection. Our observations suggest that viral pathogens interfere with the non-coding portion of the human genome, thereby guaranteeing their successful propagation, and that the expression of VIN correlates with their virulence. Consequently, our study provides a novel approach for understanding virus pathogenesis in greater detail, which will enable future design of new antiviral strategies targeting the host's non-protein coding genome.

Regulatory RNAs: charming gene management styles for synthetic biology applications

RNAs have many important functional properties, including that they are independently controllable and highly tunable. As a result of these advantageous properties, their use in a myriad of sophisticated devices has been widely explored. Yet, the exploitation of RNAs for synthetic applications is highly dependent on the ability to characterize the many new molecules that continue to be discovered by large-scale sequencing and high-throughput screening techniques. In this review, we present an exhaustive survey of the most recent synthetic bacterial riboswitches and small RNAs while emphasizing their virtues in gene expression management. We also explore the use of these RNA components as building blocks in the RNA synthetic biology toolbox and discuss examples of synthetic RNA components used to rewire bacterial regulatory circuitry. We anticipate that this field will expand its catalog of smart devices by mimicking and manipulating natural RNA mechanisms and functions.

Chromatin-bound RNA and the neurobiology of psychiatric disease

A large, and still rapidly expanding literature on epigenetic regulation in the nervous system has provided fundamental insights into the dynamic regulation of DNA methylation and post-translational histone modifications in the context of neuronal plasticity in health and disease. Remarkably, however, very little is known about the potential role of chromatin-bound RNAs, including many long non-coding transcripts and various types of small RNAs. Here, we provide an overview on RNA-mediated regulation of chromatin structure and function, with focus on histone lysine methylation and psychiatric disease. Examples of recently discovered chromatin-bound long non-coding RNAs important for neuronal health and function include the brain-derived neurotrophic factor antisense transcript (Bdnf-AS) which regulates expression of the corresponding sense transcript, and LOC389023 which is associated with human-specific histone methylation signatures at the chromosome 2q14.1 neurodevelopmental risk locus by regulating expression of DPP10, an auxillary subunit for voltage-gated K(+) channels. We predict that the exploration of chromatin-bound RNA will significantly advance our current knowledge base in neuroepigenetics and biological psychiatry.

Panning for Long Noncoding RNAs

The recent advent of high-throughput approaches has revealed widespread transcription of the human genome, leading to a new appreciation of transcription regulation, especially from noncoding regions. Distinct from most coding and small noncoding RNAs, long noncoding RNAs (lncRNAs) are generally expressed at low levels, are less conserved and lack protein-coding capacity. These intrinsic features of lncRNAs have not only hampered their full annotation in the past several years, but have also generated controversy concerning whether many or most of these lncRNAs are simply the result of transcriptional noise. Here, we assess these intrinsic features that have challenged lncRNA discovery and further summarize recent progress in lncRNA discovery with integrated methodologies, from which new lessons and insights can be derived to achieve better characterization of lncRNA expression regulation. Full annotation of lncRNA repertoires and the implications of such annotation will provide a fundamental basis for comprehensive understanding of pervasive functions of lncRNAs in biological regulation.