scholarly journals Estimating the power of sequence covariation for detecting conserved RNA structure

2019 ◽  
Author(s):  
Elena Rivas ◽  
Jody Clements ◽  
Sean R. Eddy
Keyword(s):  
Secondary Structure ◽  
Sequence Alignment ◽  
Rna Structure ◽  
Rna Secondary Structure ◽  
Noncoding Rnas ◽  
Long Noncoding Rnas ◽  
Detection Power

AbstractPairwise sequence covariations are a signal of conserved RNA secondary structure. We describe a method for distinguishing when lack of covariation signal can be taken as evidence against a conserved RNA structure, as opposed to when a sequence alignment merely has insufficient variation to detect covariations. We find that alignments for several long noncoding RNAs previously shown to lack covariation support do have adequate covariation detection power, providing additional evidence against their proposed conserved structures.

scholarly journals Estimating the power of sequence covariation for detecting conserved RNA structure

2020 ◽  
Vol 36 (10) ◽  
pp. 3072-3076 ◽  
Author(s):  
Elena Rivas ◽  
Jody Clements ◽  
Sean R Eddy
Keyword(s):  
Secondary Structure ◽  
Sequence Alignment ◽  
Rna Structure ◽  
Rna Secondary Structure ◽  
Source Code ◽  
Web Server ◽  
Supplementary Information ◽  
Supplementary Data ◽  
Detection Power ◽  
Non Coding Rnas

Abstract Pairwise sequence covariations are a signal of conserved RNA secondary structure. We describe a method for distinguishing when lack of covariation signal can be taken as evidence against a conserved RNA structure, as opposed to when a sequence alignment merely has insufficient variation to detect covariations. We find that alignments for several long non-coding RNAs previously shown to lack covariation support do have adequate covariation detection power, providing additional evidence against their proposed conserved structures. Availability and implementation The R-scape web server is at eddylab.org/R-scape, with a link to download the source code. Supplementary information Supplementary data are available at Bioinformatics online.

Predicting RNA Secondary Structure Using a Improved BPSO Based on Stem Combination

2013 ◽  
Vol 325-326 ◽  
pp. 1551-1554
Author(s):  
Yi Qi
Keyword(s):  
Secondary Structure ◽  
Sensitivity And Specificity ◽  
Rna Structure ◽  
Structure Prediction ◽  
Rna Secondary Structure ◽  
Experimental Results ◽  
New Method ◽  
Rna Structure Prediction ◽  
New Strategies

In this paper, we present an improved BPSO to predict RNA secondary structure to improve the performance with two new strategies. First one is to reduce the searching space of PSO through super stem set construction. Second is to modify the general BPSO updating process to settle stem permutation and combination problems. The experimental results show that the new method is effective for RNA structure prediction in terms of sensitivity and specificity by different sequence datasets including simple pseudoknot.

scholarly journals Bioinformatic and Physical Characterizations of Genome-Scale Ordered RNA Structure in Mammalian RNA Viruses

2008 ◽  
Vol 82 (23) ◽  
pp. 11824-11836 ◽  
Author(s):  
Matthew Davis ◽  
Selena M. Sagan ◽  
John P. Pezacki ◽  
David J. Evans ◽  
Peter Simmonds
Keyword(s):  
Secondary Structure ◽  
Rna Structure ◽  
Rna Secondary Structure ◽  
Large Scale ◽  
Rna Viruses ◽  
Plant Viruses ◽  
Large Set ◽  
Conserved Sequence ◽  
Evolutionarily Conserved ◽  
Genome Scale

ABSTRACT By the analysis of thermodynamic RNA secondary structure predictions, we previously obtained evidence for evolutionarily conserved large-scale ordering of RNA virus genomes (P. Simmonds, A. Tuplin, and D. J. Evans, RNA 10:1337-1351, 2004). Genome-scale ordered RNA structure (GORS) was widely distributed in many animal and plant viruses, much greater in extent than RNA structures required for viral translation or replication, but in mammalian viruses was associated with host persistence. To substantiate the existence of large-scale RNA structure differences between viruses, a large set of alignments of mammalian RNA viruses and rRNA sequences as controls were examined by thermodynamic methods (to calculate minimum free energy differences) and by algorithmically independent RNAz and Pfold methods. These methods produced generally concordant results and identified substantial differences in the degrees of evolutionarily conserved, sequence order-dependent RNA secondary structure between virus genera and groups. A probe hybridization accessibility assay was used to investigate the physical nature of GORS. Transcripts of hepatitis C virus (HCV), hepatitis G virus/GB virus-C (HGV/GBV-C), and murine norovirus, which are predicted to be structured, were largely inaccessible to hybridization in solution, in contrast to the almost universal binding of probes to a range of unstructured virus transcripts irrespective of G+C content. Using atomic force microscopy, HCV and HGV/GBV-C RNA was visualized as tightly compacted prolate spheroids, while under the same experimental conditions the predicted unstructured poliovirus and rubella virus RNA were pleomorphic and had extensively single-stranded RNA on deposition. Bioinformatic and physical characterization methods both identified fundamental differences in the configurations of viral genomic RNA that may modify their interactions with host cell defenses and their ability to persist.

scholarly journals Quantitative high-throughput tests of ubiquitous RNA secondary structure prediction algorithms via RNA/protein binding

2019 ◽  
Author(s):  
Winston R. Becker ◽  
Inga Jarmoskaite ◽  
Kalli Kappel ◽  
Pavanapuresan P. Vaidyanathan ◽  
Sarah K. Denny ◽  
...  
Keyword(s):  
Secondary Structure ◽  
Protein Binding ◽  
Rna Structure ◽  
Structure Prediction ◽  
Rna Secondary Structure ◽  
Nearest Neighbor ◽  
Secondary Structure Prediction ◽  
Structural Features ◽  
Vast Number ◽  
Prediction Algorithms

AbstractNearest-neighbor (NN) rules provide a simple and powerful quantitative framework for RNA structure prediction that is strongly supported for canonical Watson-Crick duplexes from a plethora of thermodynamic measurements. Predictions of RNA secondary structure based on nearest-neighbor (NN) rules are routinely used to understand biological function and to engineer and control new functions in biotechnology. However, NN applications to RNA structural features such as internal and terminal loops rely on approximations and assumptions, with sparse experimental coverage of the vast number of possible sequence and structural features. To test to what extent NN rules accurately predict thermodynamic stabilities across RNAs with non-WC features, we tested their predictions using a quantitative high-throughput assay platform, RNA-MaP. Using a thermodynamic assay with coupled protein binding, we carried out equilibrium measurements for over 1000 RNAs with a range of predicted secondary structure stabilities. Our results revealed substantial scatter and systematic deviations between NN predictions and observed stabilities. Solution salt effects and incorrect or omitted loop parameters contribute to these observed deviations. Our results demonstrate the need to independently and quantitatively test NN computational algorithms to identify their capabilities and limitations. RNA-MaP and related approaches can be used to test computational predictions and can be adapted to obtain experimental data to improve RNA secondary structure and other prediction algorithms.Significance statementRNA secondary structure prediction algorithms are routinely used to understand, predict and design functional RNA structures in biology and biotechnology. Given the vast number of RNA sequence and structural features, these predictions rely on a series of approximations, and independent tests are needed to quantitatively evaluate the accuracy of predicted RNA structural stabilities. Here we measure the stabilities of over 1000 RNA constructs by using a coupled protein binding assay. Our results reveal substantial deviations from the RNA stabilities predicted by popular algorithms, and identify factors contributing to the observed deviations. We demonstrate the importance of quantitative, experimental tests of computational RNA structure predictions and present an approach that can be used to routinely test and improve the prediction accuracy.

scholarly journals RNA Secondary Structurome Revealed Distinct Thermoregulation in Plasmodium falciparum

2022 ◽  
Vol 9 ◽  
Author(s):  
Yanwei Qi ◽  
Yuhong Zhang ◽  
Quankai Mu ◽  
Guixing Zheng ◽  
Mengxin Zhang ◽  
...  
Keyword(s):  
Plasmodium Falciparum ◽  
Secondary Structure ◽  
Rna Structure ◽  
Rna Secondary Structure ◽  
Structural Changes ◽  
Environmental Changes ◽  
Parasite Development ◽  
Temperature Dependent ◽  
Trophozoite Stage ◽  
Temperature Changes

The development of Plasmodium parasites, a causative agent of malaria, requests two hosts and the completion of 11 different parasite stages during development. Therefore, an efficient and fast response of parasites to various complex environmental changes, such as ambient temperature, pH, ions, and nutrients, is essential for parasite development and survival. Among many of these environmental changes, temperature is a decisive factor for parasite development and pathogenesis, including the thermoregulation of rRNA expression, gametogenesis, and parasite sequestration in cerebral malaria. However, the exact mechanism of how Plasmodium parasites rapidly respond and adapt to temperature change remains elusive. As a fundamental and pervasive regulator of gene expression, RNA structure can be a specific mechanism for fine tuning various biological processes. For example, dynamic and temperature-dependent changes in RNA secondary structures can control the expression of different gene programs, as shown by RNA thermometers. In this study, we applied the in vitro and in vivo transcriptomic-wide secondary structurome approach icSHAPE to measure parasite RNA structure changes with temperature alteration at single-nucleotide resolution for ring and trophozoite stage parasites. Among 3,000 probed structures at different temperatures, our data showed structural changes in the global transcriptome, such as S-type rRNA, HRPII gene, and the erythrocyte membrane protein family. When the temperature drops from 37°C to 26°C, most of the genes in the trophozoite stage cause significantly more changes to the RNA structure than the genes in the ring stage. A multi-omics analysis of transcriptome data from RNA-seq and RNA structure data from icSHAPE reveals that the specific RNA secondary structure plays a significant role in the regulation of transcript expression for parasites in response to temperature changes. In addition, we identified several RNA thermometers (RNATs) that responded quickly to temperature changes. The possible thermo-responsive RNAs in Plasmodium falciparum were further mapped. To this end, we identified dynamic and temperature-dependent RNA structural changes in the P. falciparum transcriptome and performed a comprehensive characterization of RNA secondary structures over the course of temperature stress in blood stage development. These findings not only contribute to a better understanding of the function of the RNA secondary structure but may also provide novel targets for efficient vaccines or drugs.

scholarly journals RNA secondary structure prediction using an ensemble of two-dimensional deep neural networks and transfer learning

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Jaswinder Singh ◽  
Jack Hanson ◽  
Kuldip Paliwal ◽  
Yaoqi Zhou
Keyword(s):  
Secondary Structure ◽  
Transfer Learning ◽  
Rna Structure ◽  
Structure Prediction ◽  
Secondary Structure Prediction ◽  
Noncoding Rnas ◽  
Rna Structures ◽  
Base Pairs ◽  
Functional Annotations ◽  
Model Training

AbstractThe majority of our human genome transcribes into noncoding RNAs with unknown structures and functions. Obtaining functional clues for noncoding RNAs requires accurate base-pairing or secondary-structure prediction. However, the performance of such predictions by current folding-based algorithms has been stagnated for more than a decade. Here, we propose the use of deep contextual learning for base-pair prediction including those noncanonical and non-nested (pseudoknot) base pairs stabilized by tertiary interactions. Since only $$<$$<250 nonredundant, high-resolution RNA structures are available for model training, we utilize transfer learning from a model initially trained with a recent high-quality bpRNA dataset of $$> $$>10,000 nonredundant RNAs made available through comparative analysis. The resulting method achieves large, statistically significant improvement in predicting all base pairs, noncanonical and non-nested base pairs in particular. The proposed method (SPOT-RNA), with a freely available server and standalone software, should be useful for improving RNA structure modeling, sequence alignment, and functional annotations.

scholarly journals Pervasive RNA Secondary Structure in the Genomes of SARS-CoV-2 and Other Coronaviruses

mBio ◽  
2020 ◽  
Vol 11 (6) ◽  
Author(s):  
P. Simmonds
Keyword(s):  
Secondary Structure ◽  
Rna Structure ◽  
Rna Secondary Structure ◽  
Large Scale ◽  
Rna Viruses ◽  
Shielding Effect ◽  
Cellular Interactions ◽  
Folding Energy ◽  
Base Pairing ◽  
Positive Strand Rna

ABSTRACT The ultimate outcome of the coronavirus disease 2019 (COVID-19) pandemic is unknown and is dependent on a complex interplay of its pathogenicity, transmissibility, and population immunity. In the current study, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was investigated for the presence of large-scale internal RNA base pairing in its genome. This property, termed genome-scale ordered RNA structure (GORS) has been previously associated with host persistence in other positive-strand RNA viruses, potentially through its shielding effect on viral RNA recognition in the cell. Genomes of SARS-CoV-2 were remarkably structured, with minimum folding energy differences (MFEDs) of 15%, substantially greater than previously examined viruses such as hepatitis C virus (HCV) (MFED of 7 to 9%). High MFED values were shared with all coronavirus genomes analyzed and created by several hundred consecutive energetically favored stem-loops throughout the genome. In contrast to replication-associated RNA structure, GORS was poorly conserved in the positions and identities of base pairing with other sarbecoviruses—even similarly positioned stem-loops in SARS-CoV-2 and SARS-CoV rarely shared homologous pairings, indicative of more rapid evolutionary change in RNA structure than in the underlying coding sequences. Sites predicted to be base paired in SARS-CoV-2 showed less sequence diversity than unpaired sites, suggesting that disruption of RNA structure by mutation imposes a fitness cost on the virus that is potentially restrictive to its longer evolution. Although functionally uncharacterized, GORS in SARS-CoV-2 and other coronaviruses represents important elements in their cellular interactions that may contribute to their persistence and transmissibility. IMPORTANCE The detection and characterization of large-scale RNA secondary structure in the genome of SARS-CoV-2 indicate an extraordinary and unsuspected degree of genome structural organization; this could be effectively visualized through a newly developed contour plotting method that displays positions, structural features, and conservation of RNA secondary structure between related viruses. Such RNA structure imposes a substantial evolutionary cost; paired sites showed greater restriction in diversity and represent a substantial additional constraint in reconstructing its molecular epidemiology. Its biological relevance arises from previously documented associations between possession of structured genomes and persistence, as documented for HCV and several other RNA viruses infecting humans and mammals. Shared properties potentially conferred by large-scale structure in SARS-CoV-2 include increasing evidence for prolonged infections and induced immune dysfunction that prevents development of protective immunity. The findings provide an additional element to cellular interactions that potentially influences the natural history of SARS-CoV-2, its pathogenicity, and its transmission.

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