Parallel computation of genome-scale RNA secondary structure to detect structural constraints on human genome

mRNA nuclear export: how mRNA identity features distinguish functional RNAs from junk transcripts

The division of the cellular space into nucleoplasm and cytoplasm promotes quality control mechanisms that prevent misprocessed mRNAs and junk RNAs from gaining access to the translational machinery. Here, we explore how properly processed mRNAs are distinguished from both misprocessed mRNAs and junk RNAs by the presence or absence of various 'identity features'.

DeepRaccess: high-speed RNA accessibility prediction using deep learning

RNA accessibility is a useful RNA secondary structural feature for predicting RNA-RNA interactions and translation efficiency in prokaryotes. However, conventional accessibility calculation tools, such as Raccess, are computationally expensive and require considerable computational time to perform transcriptome-scale analysis. In this study, we developed DeepRaccess, which predicts RNA accessibility based on deep learning methods. DeepRaccess was trained to take artificial RNA sequences as input and to predict the accessibility of these sequences as calculated by Raccess. Simulation and empirical dataset analyses showed that the accessibility predicted by DeepRaccess was highly correlated with the accessibility calculated by Raccess. In addition, we confirmed that DeepRaccess could predict protein abundance in E.coli with moderate accuracy from the sequences around the start codon. We also demonstrated that DeepRaccess achieved tens to hundreds of times software speed-up in a GPU environment. The source codes and the trained models of DeepRaccess are freely available at https://github.com/hmdlab/DeepRaccess.

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.

LinAliFold and CentroidLinAliFold: fast RNA consensus secondary structure prediction for aligned sequences using beam search methods

Motivation

RNA consensus secondary structure prediction from aligned sequences is a powerful approach for improving the secondary structure prediction accuracy. However, because the computational complexities of conventional prediction tools scale with the cube of the alignment lengths, their application to long RNA sequences, such as viral RNAs or long non-coding RNAs, requires significant computational time.

Results

In this study, we developed LinAliFold and CentroidLinAliFold, fast RNA consensus secondary structure prediction tools based on minimum free energy and maximum expected accuracy principles, respectively. We achieved software acceleration using beam search methods that were successfully used for fast secondary structure prediction from a single RNA sequence. Benchmark analyses showed that LinAliFold and CentroidLinAliFold were much faster than the existing methods while preserving the prediction accuracy. As an empirical application, we predicted the consensus secondary structure of coronaviruses with approximately 30 000 nt in 5 and 79 min by LinAliFold and CentroidLinAliFold, respectively. We confirmed that the predicted consensus secondary structure of coronaviruses was consistent with the experimental results.

Availability and implementation

The source codes of LinAliFold and CentroidLinAliFold are freely available at https://github.com/fukunagatsu/LinAliFold-CentroidLinAliFold.

Supplementary information

Supplementary data are available at Bioinformatics Advances online.

Structure-based screening for functional non-coding RNAs in fission yeast identifies a factor repressing untimely initiation of sexual differentiation

Non-coding RNAs (ncRNAs) ubiquitously exist in normal and cancer cells. Despite their prevalent distribution, the functions of most long ncRNAs remain uncharacterized. The fission yeast Schizosaccharomyces pombe expresses >1800 ncRNAs annotated to date, but most unconventional ncRNAs (excluding tRNA, rRNA, snRNA and snoRNA) remain uncharacterized. To discover the functional ncRNAs, here we performed a combinatory screening of computational and biological tests. First, all S. pombe ncRNAs were screened in silico for those showing conservation in sequence as well as in secondary structure with ncRNAs in closely related species. Almost a half of the 151 selected conserved ncRNA genes were uncharacterized. Twelve ncRNA genes that did not overlap with protein-coding sequences were next chosen for biological screening that examines defects in growth or sexual differentiation, as well as sensitivities to drugs and stresses. Finally, we highlighted an ncRNA transcribed from SPNCRNA.1669, which inhibited untimely initiation of sexual differentiation. A domain that was predicted as conserved secondary structure by the computational operations was essential for the ncRNA to function. Thus, this study demonstrates that in silico selection focusing on conservation of the secondary structure over species is a powerful method to pinpoint novel functional ncRNAs.

Detection and Characterization of Ribosome-Associated Long Noncoding RNAs

Ribosome profiling shows potential for studying the function of long noncoding RNAs (lncRNAs). We introduce a bioinformatics pipeline for detecting ribosome-associated lncRNAs (ribo-lncRNAs) from ribosome profiling data. Further, we describe a machine-learning approach for the characterization of ribo-lncRNAs based on their sequence features. Scripts for ribo-lncRNA analysis can be accessed at ( https://ribolnc.hamadalab.com/ ).

RNA structure-wide discovery of functional interactions with multiplexed RNA motif library

Biochemical assays and computational analyses have discovered RNA structures throughout various transcripts. However, the roles of these structures are mostly unknown. Here we develop folded RNA element profiling with structure library (FOREST), a multiplexed affinity assay system to identify functional interactions from transcriptome-wide RNA structure datasets. We generate an RNA structure library by extracting validated or predicted RNA motifs from gene-annotated RNA regions. The RNA structure library with an affinity enrichment assay allows for the comprehensive identification of target-binding RNA sequences and structures in a high-throughput manner. As a proof-of-concept, FOREST discovers multiple RNA-protein interaction networks with quantitative scores, including translational regulatory elements that function in living cells. Moreover, FOREST reveals different binding landscapes of RNA G-quadruplex (rG4) structures-binding proteins and discovers rG4 structures in the terminal loops of precursor microRNAs. Overall, FOREST serves as a versatile platform to investigate RNA structure-function relationships on a large scale.

Structured RNA motifs can be obtained by structure probing, duplex capture, and motif prediction. Here the authors develop a multiplexed affinity assay system to identify functional protein interactors from an RNA structure library with validated or predicted RNA motifs.

LncRNA Xist, X-chromosome Instability and Alzheimer's Disease

Neurodegenerative Diseases (NDD) are the major contributors to age-related causes of mental disability on a global scale. Most NDDs, like Alzheimer's Disease (AD), are complex in nature - implying that they are multi-parametric both in terms of heterogeneous clinical outcomes and underlying molecular paradigms. Emerging evidence from high throughput genomic, transcriptomic and small RNA sequencing experiments hint at the roles of long non-coding RNAs (lncRNAs) in AD. X-inactive Specific Transcript (XIST), a component of the Xic, the X-chromosome inactivation centre, is an RNA gene on the X chromosome of the placental mammals indispensable for the X inactivation process. An extensive literature survey shows that aberrations in Xist expression and in some cases, a disruption of the Xchromosome inactivation as a whole play a significant role in AD. Considering the enormous potential of Xist as an endogenous silencing molecule, the idea of using Xist as a non-conventional chromosome silencer to treat diseases harboring chromosomal alterations is also being implemented. Comprehensive knowledge about how Xist could play such a role in AD is still elusive. In this review, we have collated the available knowledge on the possible Xist involvement and deregulation from the perspective of molecular mechanisms governing NDDs with a primary focus on Alzheimer's disease. Possibilities of XIST mediated therapeutic intervention and linkages between XIC and preferential predisposition of females to AD have also been discussed.

Lionheart LincRNA alleviates cardiac systolic dysfunction under pressure overload

Recent high-throughput approaches have revealed a vast number of transcripts with unknown functions. Many of these transcripts are long noncoding RNAs (lncRNAs), and intergenic region-derived lncRNAs are classified as long intergenic noncoding RNAs (lincRNAs). Although Myosin heavy chain 6 (Myh6) encoding primary contractile protein is down-regulated in stressed hearts, the underlying mechanisms are not fully clarified especially in terms of lincRNAs. Here, we screen upregulated lincRNAs in pressure overloaded hearts and identify a muscle-abundant lincRNA termed Lionheart. Compared with controls, deletion of the Lionheart in mice leads to decreased systolic function and a reduction in MYH6 protein levels following pressure overload. We reveal decreased MYH6 results from an interaction between Lionheart and Purine-rich element-binding protein A after pressure overload. Furthermore, human LIONHEART levels in left ventricular biopsy specimens positively correlate with cardiac systolic function. Our results demonstrate Lionheart plays a pivotal role in cardiac remodeling via regulation of MYH6.

Kuwabara et al. identify a novel long intergenic noncoding RNA (lincRNA), termed Lionheart, upregulated in pressure overloaded hearts in mice. Deleting this gene results in decreased systolic function and reduction in MYH6 protein levels following pressure overload. They demonstrate that Lionheart interacts with PURA, preventing its binding to the promoter region of Myh6 locus, leading to reduced MYH6 protein expression.

RintC: fast and accuracy-aware decomposition of distributions of RNA secondary structures with extended logsumexp

Background

Analysis of secondary structures is essential for understanding the functions of RNAs. Because RNA molecules thermally fluctuate, it is necessary to analyze the probability distributions of their secondary structures. Existing methods, however, are not applicable to long RNAs owing to their high computational complexity. Additionally, previous research has suffered from two numerical difficulties: overflow and significant numerical errors.

Result

In this research, we reduced the computational complexity of calculating the landscape of the probability distribution of secondary structures by introducing a maximum-span constraint. In addition, we resolved numerical computation problems through two techniques: extended logsumexp and accuracy-guaranteed numerical computation. We analyzed the stability of the secondary structures of 16S ribosomal RNAs at various temperatures without overflow. The results obtained are consistent with previous research on thermophilic bacteria, suggesting that our method is applicable in thermal stability analysis. Furthermore, we quantitatively assessed numerical stability using our method..

Conclusion

These results demonstrate that the proposed method is applicable to long RNAs..

Caliciviral protein-based artificial translational activator for mammalian gene circuits with RNA-only delivery

Synthetic RNA-based gene circuits enable sophisticated gene regulation without the risk of insertional mutagenesis. While various RNA binding proteins have been used for translational repression in gene circuits, the direct translational activation of synthetic mRNAs has not been achieved. Here we develop Caliciviral VPg-based Translational activator (CaVT), which activates the translation of synthetic mRNAs without the canonical 5'-cap. The level of translation can be modulated by changing the locations, sequences, and modified nucleosides of CaVT-binding motifs in the target mRNAs, enabling the simultaneous translational activation and repression of different mRNAs with RNA-only delivery. We demonstrate the efficient regulation of apoptosis and genome editing by tuning translation levels with CaVT. In addition, we design programmable CaVT that responds to endogenous microRNAs or small molecules, achieving both cell-state-specific and conditional translational activation from synthetic mRNAs. CaVT will become an important tool in synthetic biology for both biological studies and future therapeutic applications.

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.

reactIDR: evaluation of the statistical reproducibility of high-throughput structural analyses towards a robust RNA structure prediction

Background

Recently, next-generation sequencing techniques have been applied for the detection of RNA secondary structures, which is referred to as high-throughput RNA structural (HTS) analyses, and many different protocols have been used to detect comprehensive RNA structures at single-nucleotide resolution. However, the existing computational analyses heavily depend on the experimental methodology to generate data, which results in difficulties associated with statistically sound comparisons or combining the results obtained using different HTS methods.

Results

Here, we introduced a statistical framework, reactIDR, which can be applied to the experimental data obtained using multiple HTS methodologies. Using this approach, nucleotides are classified into three structural categories, loop, stem/background, and unmapped. reactIDR uses the irreproducible discovery rate (IDR) with a hidden Markov model to discriminate between the true and spurious signals obtained in the replicated HTS experiments accurately, and it is able to incorporate an expectation-maximization algorithm and supervised learning for efficient parameter optimization. The results of our analyses of the real-life HTS data showed that reactIDR had the highest accuracy in the classification of ribosomal RNA stem/loop structures when using both individual and integrated HTS datasets, and its results corresponded the best to the three-dimensional structures.

Conclusions

We have developed a novel software, reactIDR, for the prediction of stem/loop regions from the HTS analysis datasets. For the rRNA structure analyses, reactIDR was shown to have robust accuracy across different datasets by using the reproducibility criterion, suggesting its potential for increasing the value of existing HTS datasets. reactIDR is publicly available at https://github.com/carushi/reactIDR.

Electronic supplementary material

The online version of this article (10.1186/s12859-019-2645-4) contains supplementary material, which is available to authorized users.

Identifying sequence features that drive ribosomal association for lncRNA

BACKGROUND

With the increasing number of annotated long noncoding RNAs (lncRNAs) from the genome, researchers are continually updating their understanding of lncRNAs. Recently, thousands of lncRNAs have been reported to be associated with ribosomes in mammals. However, their biological functions or mechanisms are still unclear.

RESULTS

In this study, we tried to investigate the sequence features involved in the ribosomal association of lncRNA. We have extracted ninety-nine sequence features corresponding to different biological mechanisms (i.e., RNA splicing, putative ORF, k-mer frequency, RNA modification, RNA secondary structure, and repeat element). An [Formula: see text]-regularized logistic regression model was applied to screen these features. Finally, we obtained fifteen and nine important features for the ribosomal association of human and mouse lncRNAs, respectively.

CONCLUSION

To our knowledge, this is the first study to characterize ribosome-associated lncRNAs and ribosome-free lncRNAs from the perspective of sequence features. These sequence features that were identified in this study may shed light on the biological mechanism of the ribosomal association and provide important clues for functional analysis of lncRNAs.

RIblast: an ultrafast RNA-RNA interaction prediction system based on a seed-and-extension approach

Motivation

LncRNAs play important roles in various biological processes. Although more than 58 000 human lncRNA genes have been discovered, most known lncRNAs are still poorly characterized. One approach to understanding the functions of lncRNAs is the detection of the interacting RNA target of each lncRNA. Because experimental detections of comprehensive lncRNA–RNA interactions are difficult, computational prediction of lncRNA–RNA interactions is an indispensable technique. However, the high computational costs of existing RNA–RNA interaction prediction tools prevent their application to large-scale lncRNA datasets.

Results

Here, we present ‘RIblast’, an ultrafast RNA–RNA interaction prediction method based on the seed-and-extension approach. RIblast discovers seed regions using suffix arrays and subsequently extends seed regions based on an RNA secondary structure energy model. Computational experiments indicate that RIblast achieves a level of prediction accuracy similar to those of existing programs, but at speeds over 64 times faster than existing programs.

Availability and implementation

The source code of RIblast is freely available at https://github.com/fukunagatsu/RIblast.

Supplementary information

Supplementary data are available at Bioinformatics online.

Function by Structure: Spotlights on Xist Long Non-coding RNA

Recent experimental evidence indicates that lncRNAs can act as regulatory molecules in the context of development and disease. Xist, the master regulator of X chromosome inactivation, is a classic example of how lncRNAs can exert multi-layered and fine-tuned regulatory functions, by acting as a molecular scaffold for recruitment of distinct protein factors. In this review, we discuss the methodologies employed to define Xist RNA structures and the tight interplay between structural clues and functionality of lncRNAs. This model of modular function dictated by structure, can be also generalized to other lncRNAs, beyond the field of X chromosome inactivation, to explain common features of similarly folded RNAs.

Comparative analysis of lincRNA in insect species

BACKGROUND

The ever increasing availability of genomes makes it possible to investigate and compare not only the genomic complements of genes and proteins, but also of RNAs. One class of RNAs, the long noncoding RNAs (lncRNAs) and, in particular, their subclass of long intergenic noncoding RNAs (lincRNAs) have recently gained much attention because of their roles in regulation of important biological processes such as immune response or cell differentiation and as possible evolutionary precursors for protein coding genes. lincRNAs seem to be poorly conserved at the sequence level but at least some lincRNAs have conserved structural elements and syntenic genomic positions. Previous studies showed that transposable elements are a main contribution to the evolution of lincRNAs in mammals. In contrast, plant lincRNA emergence and evolution has been linked with local duplication events. However, little is known about their evolutionary dynamics in general and in insect genomes in particular.

RESULTS

Here we compared lincRNAs between seven insect genomes and investigated possible evolutionary changes and functional roles. We find very low sequence conservation between different species and that similarities within a species are mostly due to their association with transposable elements (TE) and simple repeats. Furthermore, we find that TEs are less frequent in lincRNA exons than in their introns, indicating that TEs may have been removed by selection. When we analysed the predicted thermodynamic stabilities of lincRNAs we found that they are more stable than their randomized controls which might indicate some selection pressure to maintain certain structural elements. We list several of the most stable lincRNAs which could serve as prime candidates for future functional studies. We also discuss the possibility of de novo protein coding genes emerging from lincRNAs. This is because lincRNAs with high GC content and potentially with longer open reading frames (ORF) are candidate loci where de novo gene emergence might occur.

CONCLUSION

The processes responsible for the emergence and diversification of lincRNAs in insects remain unclear. Both duplication and transposable elements may be important for the creation of new lincRNAs in insects.