Updates to the RNA mapping database (RMDB), version 2

Computational Prediction of Gene Regulation by lncRNAs

High-throughput sequencing technologies and innovative bioinformatics tools discovered that most of the genome is transcribed into RNA. However, only a fraction of the RNAs in cell translates into proteins, while the majority of them are categorized as noncoding RNAs (ncRNAs). The ncRNAs with more than 200 nt without protein-coding ability are termed long noncoding RNAs (lncRNAs). Hundreds of studies established that lncRNAs are a crucial RNA family regulating gene expression. Regulatory RNAs, including lncRNAs, modulate gene expression by interacting with RNA, DNA, and proteins. Several databases and computational tools have been developed to explore the functions of lncRNAs in cellular physiology. This chapter discusses the tools available for lncRNA functional analysis and provides a detailed workflow for the computational analysis of lncRNAs.

Ribonanza: deep learning of RNA structure through dual crowdsourcing

Prediction of RNA structure from sequence remains an unsolved problem, and progress has been slowed by a paucity of experimental data. Here, we present Ribonanza, a dataset of chemical mapping measurements on two million diverse RNA sequences collected through Eterna and other crowdsourced initiatives. Ribonanza measurements enabled solicitation, training, and prospective evaluation of diverse deep neural networks through a Kaggle challenge, followed by distillation into a single, self-contained model called RibonanzaNet. When fine tuned on auxiliary datasets, RibonanzaNet achieves state-of-the-art performance in modeling experimental sequence dropout, RNA hydrolytic degradation, and RNA secondary structure, with implications for modeling RNA tertiary structure.

Integrating cryo-OrbiSIMS with computational modelling and metadynamics simulations enhances RNA structure prediction at atomic resolution

The 3D architecture of RNAs governs their molecular interactions, chemical reactions, and biological functions. However, a large number of RNAs and their protein complexes remain poorly understood due to the limitations of conventional structural biology techniques in deciphering their complex structures and dynamic interactions. To address this limitation, we have benchmarked an integrated approach that combines cryogenic OrbiSIMS, a state-of-the-art solid-state mass spectrometry technique, with computational methods for modelling RNA structures at atomic resolution with enhanced precision. Furthermore, using 7SK RNP as a test case, we have successfully determined the full 3D structure of a native RNA in its apo, native and disease-remodelled states, which offers insights into the structural interactions and plasticity of the 7SK complex within these states. Overall, our study establishes cryo-OrbiSIMS as a valuable tool in the field of RNA structural biology as it enables the study of challenging, native RNA systems.

A review on the role of RNA methylation in aging-related diseases

Senescence is the underlying mechanism of organism aging and is robustly regulated at the post-transcriptional level. This regulation involves the chemical modifications, of which the RNA methylation is the most common. Recently, a rapidly growing number of studies have demonstrated that methylation is relevant to aging and aging-associated diseases. Owing to the rapid development of detection methods, the understanding on RNA methylation has gone deeper. In this review, we summarize the current understanding on the influence of RNA modification on cellular senescence, with a focus on mRNA methylation in aging-related diseases, and discuss the emerging potential of RNA modification in diagnosis and therapy.

Genome-Wide RNA Secondary Structure Prediction

The information of RNA secondary structure has been widely applied to the inference of RNA function. However, a classical prediction method is not feasible to long RNAs such as mRNA due to the problems of computational time and numerical errors. To overcome those problems, sliding window methods have been applied while their results are not directly comparable to global RNA structure prediction. In this chapter, we introduce ParasoR, a method designed for parallel computation of genome-wide RNA secondary structures. To enable genome-wide prediction, ParasoR distributes dynamic programming (DP) matrices required for structure prediction to multiple computational nodes. Using the database of not the original DP variable but the ratio of variables, ParasoR can locally compute the structure scores such as stem probability or accessibility on demand. A comprehensive analysis of local secondary structures by ParasoR is expected to be a promising way to detect the statistical constraints on long RNAs.

RNA Secondary Structure Alteration Caused by Single Nucleotide Variants

A point mutation in coding RNA can cause not only an amino acid substitution but also a dynamic change of RNA secondary structure, leading to a dysfunctional RNA. Although in silico structure prediction has been used to detect structure-disrupting point mutations known as riboSNitches, exhaustive simulation of long RNAs is needed to detect a significant enrichment or depletion of riboSNitches in functional RNAs. Here, we have developed a novel algorithm Radiam (RNA secondary structure Analysis with Deletion, Insertion, And substitution Mutations) for a comprehensive riboSNitch analysis of long RNAs. Radiam is based on the ParasoR framework, which efficiently computes local RNA secondary structures for long RNAs. ParasoR can compute a variety of structure scores over globally consistent structures with maximal span constraints for the base pair distance. Using the reusable structure database made by ParasoR, Radiam performs an efficient recomputation of the secondary structures for mutated sequences. An exhaustive simulation of Radiam is expected to find reliable riboSNitch candidates on long RNAs by evaluating their statistical significance in terms of the change of local structure stability.

Combinatorial optimization of mRNA structure, stability, and translation for RNA-based therapeutics

Therapeutic mRNAs and vaccines are being developed for a broad range of human diseases, including COVID-19. However, their optimization is hindered by mRNA instability and inefficient protein expression. Here, we describe design principles that overcome these barriers. We develop an RNA sequencing-based platform called PERSIST-seq to systematically delineate in-cell mRNA stability, ribosome load, as well as in-solution stability of a library of diverse mRNAs. We find that, surprisingly, in-cell stability is a greater driver of protein output than high ribosome load. We further introduce a method called In-line-seq, applied to thousands of diverse RNAs, that reveals sequence and structure-based rules for mitigating hydrolytic degradation. Our findings show that highly structured "superfolder" mRNAs can be designed to improve both stability and expression with further enhancement through pseudouridine nucleoside modification. Together, our study demonstrates simultaneous improvement of mRNA stability and protein expression and provides a computational-experimental platform for the enhancement of mRNA medicines.

Decoding LncRNAs

Non-coding RNAs (ncRNAs) have been considered as unimportant additions to the transcriptome. Yet, in light of numerous studies, it has become clear that ncRNAs play important roles in development, health and disease. Long-ignored, long non-coding RNAs (lncRNAs), ncRNAs made of more than 200 nucleotides have gained attention due to their involvement as drivers or suppressors of a myriad of tumours. The detailed understanding of some of their functions, structures and interactomes has been the result of interdisciplinary efforts, as in many cases, new methods need to be created or adapted to characterise these molecules. Unlike most reviews on lncRNAs, we summarize the achievements on lncRNA studies by taking into consideration the approaches for identification of lncRNA functions, interactomes, and structural arrangements. We also provide information about the recent data on the involvement of lncRNAs in diseases and present applications of these molecules, especially in medicine.

Learning cis-regulatory principles of ADAR-based RNA editing from CRISPR-mediated mutagenesis

Adenosine-to-inosine (A-to-I) RNA editing catalyzed by ADAR enzymes occurs in double-stranded RNAs. Despite a compelling need towards predictive understanding of natural and engineered editing events, how the RNA sequence and structure determine the editing efficiency and specificity (i.e., cis-regulation) is poorly understood. We apply a CRISPR/Cas9-mediated saturation mutagenesis approach to generate libraries of mutations near three natural editing substrates at their endogenous genomic loci. We use machine learning to integrate diverse RNA sequence and structure features to model editing levels measured by deep sequencing. We confirm known features and identify new features important for RNA editing. Training and testing XGBoost algorithm within the same substrate yield models that explain 68 to 86 percent of substrate-specific variation in editing levels. However, the models do not generalize across substrates, suggesting complex and context-dependent regulation patterns. Our integrative approach can be applied to larger scale experiments towards deciphering the RNA editing code.

Combinatorial optimization of mRNA structure, stability, and translation for RNA-based therapeutics

Therapeutic mRNAs and vaccines are being developed for a broad range of human diseases, including COVID-19. However, their optimization is hindered by mRNA instability and inefficient protein expression. Here, we describe design principles that overcome these barriers. We develop a new RNA sequencing-based platform called PERSIST-seq to systematically delineate in-cell mRNA stability, ribosome load, as well as in-solution stability of a library of diverse mRNAs. We find that, surprisingly, in-cell stability is a greater driver of protein output than high ribosome load. We further introduce a method called In-line-seq, applied to thousands of diverse RNAs, that reveals sequence and structure-based rules for mitigating hydrolytic degradation. Our findings show that "superfolder" mRNAs can be designed to improve both stability and expression that are further enhanced through pseudouridine nucleoside modification. Together, our study demonstrates simultaneous improvement of mRNA stability and protein expression and provides a computational-experimental platform for the enhancement of mRNA medicines.

The lncRNA Toolkit: Databases and In Silico Tools for lncRNA Analysis

Long non-coding RNAs (lncRNAs) are a rapidly expanding field of research, with many new transcripts identified each year. However, only a small subset of lncRNAs has been characterized functionally thus far. To aid investigating the mechanisms of action by which new lncRNAs act, bioinformatic tools and databases are invaluable. Here, we review a selection of computational tools and databases for the in silico analysis of lncRNAs, including tissue-specific expression, protein coding potential, subcellular localization, structural conformation, and interaction partners. The assembled lncRNA toolkit is aimed primarily at experimental researchers as a useful starting point to guide wet-lab experiments, mainly containing multi-functional, user-friendly interfaces. With more and more new lncRNA analysis tools available, it will be essential to provide continuous updates and maintain the availability of key software in the future.

Anomalous Reverse Transcription through Chemical Modifications in Polyadenosine Stretches

Thermostable reverse transcriptases are workhorse enzymes underlying nearly all modern techniques for RNA structure mapping and for the transcriptome-wide discovery of RNA chemical modifications. Despite their wide use, these enzymes' behaviors at chemical modified nucleotides remain poorly understood. Wellington-Oguri et al. recently reported an apparent loss of chemical modification within putatively unstructured polyadenosine stretches modified by dimethyl sulfate or 2' hydroxyl acylation, as probed by reverse transcription. Here, reanalysis of these and other publicly available data, capillary electrophoresis experiments on chemically modified RNAs, and nuclear magnetic resonance spectroscopy on (A)₁₂ and variants show that this effect is unlikely to arise from an unusual structure of polyadenosine. Instead, tests of different reverse transcriptases on chemically modified RNAs and molecules synthesized with single 1-methyladenosines implicate a previously uncharacterized reverse transcriptase behavior: near-quantitative bypass through chemical modifications within polyadenosine stretches. All tested natural and engineered reverse transcriptases (MMLV; SuperScript II, III, and IV; TGIRT-III; and MarathonRT) exhibit this anomalous bypass behavior. Accurate DMS-guided structure modeling of the polyadenylated HIV-1 3' untranslated region requires taking into account this anomaly. Our results suggest that poly(rA-dT) hybrid duplexes can trigger an unexpectedly effective reverse transcriptase bypass and that chemical modifications in mRNA poly(A) tails may be generally undercounted.

Evidence of an Unusual Poly(A) RNA Signature Detected by High-Throughput Chemical Mapping

Homopolymeric adenosine RNA plays numerous roles in both cells and noncellular genetic material. We report herein an unusual poly(A) signature in chemical mapping data generated by the Eterna Massive Open Laboratory. Poly(A) sequences of length seven or more show unexpected results in the selective 2'-hydroxyl acylation read out by primer extension (SHAPE) and dimethyl sulfate (DMS) chemical probing. This unusual signature first appears in poly(A) sequences of length seven and grows to its maximum strength at length ∼10. In a long poly(A) sequence, the substitution of a single A by any other nucleotide disrupts the signature, but only for the 6 or so nucleotides on the 5' side of the substitution.

SMAtool reveals sequences and structural principles of protein-RNA interaction

Protein binding events on RNA are highly related to RNA secondary structure, which affects post-transcriptional regulation and translation. However, it remains challenging to describe the association between RNA secondary structure and protein binding events. Here, we present Structure Motif Analysis tool (SMAtool), a pipeline that integrates RNA secondary structure and protein binding site information to profile the binding structure preference of each protein. As an example of applying SMAtool, we extracted the RNA-structure and binding site information respectively from the DMS-seq and eCLIP-seq data of the K562 cell-line, and used SMAtool to analyze the structure motif of each RNA binding protein (RBP). This new approach provided results consistent with X-ray crystallography data from the protein data bank (PDB) database, demonstrating that it can help researchers investigate the structure preference of RBP, and understand the role of RNA secondary structure in gene expression. Availability and implementation: https://github.com/QuKunLab/SMAtool.

High-throughput determination of RNA structures

RNA performs and regulates a diverse range of cellular processes, with new functional roles being uncovered at a rapid pace. Interest is growing in how these functions are linked to RNA structures that form in the complex cellular environment. A growing suite of technologies that use advances in RNA structural probes, high-throughput sequencing and new computational approaches to interrogate RNA structure at unprecedented throughput are beginning to provide insights into RNA structures at new spatial, temporal and cellular scales.

SnapShot: RNA Structure Probing Technologies

Chemical probing coupled to high-throughput sequencing offers a flexible approach to uncover many aspects of RNA structure relevant to its cellular function. With a wide variety of chemical probes available that each report on different features of RNA molecules, a broad toolkit exists for investigating in vivo and in vitro RNA structure and interactions with other molecules.

Blind prediction of noncanonical RNA structure at atomic accuracy

Prediction of RNA structure from nucleotide sequence remains an unsolved grand challenge of biochemistry and requires distinct concepts from protein structure prediction. Despite extensive algorithmic development in recent years, modeling of noncanonical base pairs of new RNA structural motifs has not been achieved in blind challenges. We report a stepwise Monte Carlo (SWM) method with a unique add-and-delete move set that enables predictions of noncanonical base pairs of complex RNA structures. A benchmark of 82 diverse motifs establishes the method's general ability to recover noncanonical pairs ab initio, including multistrand motifs that have been refractory to prior approaches. In a blind challenge, SWM models predicted nucleotide-resolution chemical mapping and compensatory mutagenesis experiments for three in vitro selected tetraloop/receptors with previously unsolved structures (C7.2, C7.10, and R1). As a final test, SWM blindly and correctly predicted all noncanonical pairs of a Zika virus double pseudoknot during a recent community-wide RNA-Puzzle. Stepwise structure formation, as encoded in the SWM method, enables modeling of noncanonical RNA structure in a variety of previously intractable problems.

The 2018 Nucleic Acids Research database issue and the online molecular biology database collection

The 2018 Nucleic Acids Research Database Issue contains 181 papers spanning molecular biology. Among them, 82 are new and 84 are updates describing resources that appeared in the Issue previously. The remaining 15 cover databases most recently published elsewhere. Databases in the area of nucleic acids include 3DIV for visualisation of data on genome 3D structure and RNArchitecture, a hierarchical classification of RNA families. Protein databases include the established SMART, ELM and MEROPS while GPCRdb and the newcomer STCRDab cover families of biomedical interest. In the area of metabolism, HMDB and Reactome both report new features while PULDB appears in NAR for the first time. This issue also contains reports on genomics resources including Ensembl, the UCSC Genome Browser and ENCODE. Update papers from the IUPHAR/BPS Guide to Pharmacology and DrugBank are highlights of the drug and drug target section while a number of proteomics databases including proteomicsDB are also covered. The entire Database Issue is freely available online on the Nucleic Acids Research website (https://academic.oup.com/nar). The NAR online Molecular Biology Database Collection has been updated, reviewing 138 entries, adding 88 new resources and eliminating 47 discontinued URLs, bringing the current total to 1737 databases. It is available at http://www.oxfordjournals.org/nar/database/c/.