incaRNAfbinv 2.0: a webserver and software with motif control for fragment-based design of RNAs

2020 ◽  
Vol 36 (9) ◽  
pp. 2920-2922
Author(s):  
Matan Drory Retwitzer ◽  
Vladimir Reinharz ◽  
Alexander Churkin ◽  
Yann Ponty ◽  
Jérôme Waldispühl ◽  
...  
Keyword(s):  
Secondary Structure ◽  
Rna Secondary Structure ◽  
Rna Folding ◽  
Practical Interest ◽  
Supplementary Information ◽  
Command Line ◽  
Design Tools ◽  
Additional Information ◽  
Non Coding Rna ◽  
Rna Design

Abstract Summary RNA design has conceptually evolved from the inverse RNA folding problem. In the classical inverse RNA problem, the user inputs an RNA secondary structure and receives an output RNA sequence that folds into it. Although modern RNA design methods are based on the same principle, a finer control over the resulting sequences is sought. As an important example, a substantial number of non-coding RNA families show high preservation in specific regions, while being more flexible in others and this information should be utilized in the design. By using the additional information, RNA design tools can help solve problems of practical interest in the growing fields of synthetic biology and nanotechnology. incaRNAfbinv 2.0 utilizes a fragment-based approach, enabling a control of specific RNA secondary structure motifs. The new version allows significantly more control over the general RNA shape, and also allows to express specific restrictions over each motif separately, in addition to other advanced features. Availability and implementation incaRNAfbinv 2.0 is available through a standalone package and a web-server at https://www.cs.bgu.ac.il/incaRNAfbinv. Source code, command-line and GUI wrappers can be found at https://github.com/matandro/RNAsfbinv. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Using small samples to estimate neutral component size and robustness in the genotype–phenotype map of RNA secondary structure

2020 ◽  
Vol 17 (166) ◽  
pp. 20190784 ◽  
Author(s):  
Marcel Weiß ◽  
Sebastian E. Ahnert
Keyword(s):  
Secondary Structure ◽  
Rna Secondary Structure ◽  
Point Mutations ◽  
Local Environment ◽  
Small Samples ◽  
Sequence Length ◽  
Rna Sequences ◽  
Component Size ◽  
Naturally Occurring ◽  
Non Coding Rna

In genotype–phenotype (GP) maps, the genotypes that map to the same phenotype are usually not randomly distributed across the space of genotypes, but instead are predominantly connected through one-point mutations, forming network components that are commonly referred to as neutral components (NCs). Because of their impact on evolutionary processes, the characteristics of these NCs, like their size or robustness, have been studied extensively. Here, we introduce a framework that allows the estimation of NC size and robustness in the GP map of RNA secondary structure. The advantage of this framework is that it only requires small samples of genotypes and their local environment, which also allows experimental realizations. We verify our framework by applying it to the exhaustively analysable GP map of RNA sequence length L = 15, and benchmark it against an existing method by applying it to longer, naturally occurring functional non-coding RNA sequences. Although it is specific to the RNA secondary structure GP map in the first place, our framework can probably be transferred and adapted to other sequence-to-structure GP maps.

New model for the secondary structure of the 5′ non-coding RNA of poliovirus is supported by biochemical and genetic data that also show that RNA secondary structure is important in neurovirulence

1989 ◽  
Vol 207 (2) ◽  
pp. 379-392 ◽  
Author(s):  
Michael A. Skinner ◽  
Vincent R. Racaniello ◽  
Glynnis Dunn ◽  
Julian Cooper ◽  
Phillip D. Minor ◽  
...  
Keyword(s):  
Secondary Structure ◽  
Rna Secondary Structure ◽  
Genetic Data ◽  
New Model ◽  
Non Coding Rna

A New Method to Predict RNA Secondary Structure Based on RNA Folding Simulation

2016 ◽  
Vol 13 (5) ◽  
pp. 990-995 ◽  
Author(s):  
Yuanning Liu ◽  
Qi Zhao ◽  
Hao Zhang ◽  
Rui Xu ◽  
Yang Li ◽  
...  
Keyword(s):  
Secondary Structure ◽  
Rna Secondary Structure ◽  
Rna Folding ◽  
New Method ◽  
Folding Simulation

scholarly journals PhyloFold: Precise and Swift Prediction of RNA Secondary Structures to Incorporate Phylogeny among Homologs

2020 ◽  
Author(s):  
Masaki Tagashira
Keyword(s):  
Secondary Structure ◽  
Rna Secondary Structure ◽  
Prediction Accuracy ◽  
Structural Alignment ◽  
Source Code ◽  
Secondary Structures ◽  
Supplementary Information ◽  
Supplementary Data ◽  
Link Type ◽  
Structural Alignments

AbstractMotivationThe simultaneous consideration of sequence alignment and RNA secondary structure, or structural alignment, is known to help predict more accurate secondary structures of homologs. However, the consideration is heavy and can be done only roughly to decompose structural alignments.ResultsThe PhyloFold method, which predicts secondary structures of homologs considering likely pairwise structural alignments, was developed in this study. The method shows the best prediction accuracy while demanding comparable running time compared to conventional methods.AvailabilityThe source code of the programs implemented in this study is available on “https://github.com/heartsh/phylofold” and “https://github.com/heartsh/phyloalifold“.Contact“[email protected]”.Supplementary informationSupplementary data are available.

Improved RNA secondary structure and tertiary base-pairing prediction using evolutionary profile, mutational coupling and two-dimensional transfer learning

2021 ◽  
Author(s):  
Jaswinder Singh ◽  
Kuldip Paliwal ◽  
Tongchuan Zhang ◽  
Jaspreet Singh ◽  
Thomas Litfin ◽  
...  
Keyword(s):  
High Resolution ◽  
Secondary Structure ◽  
Transfer Learning ◽  
Rna Secondary Structure ◽  
Computational Prediction ◽  
Supplementary Information ◽  
Base Pairing ◽  
Base Pairs ◽  
Homologous Sequences ◽  
Non Coding Rnas

Abstract Motivation The recent discovery of numerous non-coding RNAs (long non-coding RNAs, in particular) has transformed our perception about the roles of RNAs in living organisms. Our ability to understand them, however, is hampered by our inability to solve their secondary and tertiary structures in high resolution efficiently by existing experimental techniques. Computational prediction of RNA secondary structure, on the other hand, has received much-needed improvement, recently, through deep learning of a large approximate data, followed by transfer learning with gold-standard base-pairing structures from high-resolution 3-D structures. Here, we expand this single-sequence-based learning to the use of evolutionary profiles and mutational coupling. Results The new method allows large improvement not only in canonical base-pairs (RNA secondary structures) but more so in base-pairing associated with tertiary interactions such as pseudoknots, non-canonical and lone base-pairs. In particular, it is highly accurate for those RNAs of more than 1000 homologous sequences by achieving >0.8 F1-score (harmonic mean of sensitivity and precision) for 14/16 RNAs tested. The method can also significantly improve base-pairing prediction by incorporating artificial but functional homologous sequences generated from deep mutational scanning without any modification. The fully automatic method (publicly available as server and standalone software) should provide the scientific community a new powerful tool to capture not only the secondary structure but also tertiary base-pairing information for building three-dimensional models. It also highlights the future of accurately solving the base-pairing structure by using a large number of natural and/or artificial homologous sequences. Availability and implementation Standalone-version of SPOT-RNA2 is available at https://github.com/jaswindersingh2/SPOT-RNA2. Direct prediction can also be made at https://sparks-lab.org/server/spot-rna2/. The datasets used in this research can also be downloaded from the GITHUB and the webserver mentioned above. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Accurate prediction of genome-wide RNA secondary structure profile based on extreme gradient boosting

2020 ◽  
Vol 36 (17) ◽  
pp. 4576-4582
Author(s):  
Yaobin Ke ◽  
Jiahua Rao ◽  
Huiying Zhao ◽  
Yutong Lu ◽  
Nong Xiao ◽  
...  
Keyword(s):  
Secondary Structure ◽  
High Throughput ◽  
Rna Secondary Structure ◽  
High Throughput Sequencing ◽  
Supplementary Information ◽  
Gradient Boosting ◽  
Synonymous Mutations ◽  
Periodic Distribution ◽  
Genome Wide ◽  
Extreme Gradient Boosting

Abstract Motivation RNA secondary structure plays a vital role in fundamental cellular processes, and identification of RNA secondary structure is a key step to understand RNA functions. Recently, a few experimental methods were developed to profile genome-wide RNA secondary structure, i.e. the pairing probability of each nucleotide, through high-throughput sequencing techniques. However, these high-throughput methods have low precision and cannot cover all nucleotides due to limited sequencing coverage. Results Here, we have developed a new method for the prediction of genome-wide RNA secondary structure profile from RNA sequence based on the extreme gradient boosting technique. The method achieves predictions with areas under the receiver operating characteristic curve (AUC) >0.9 on three different datasets, and AUC of 0.888 by another independent test on the recently released Zika virus data. These AUCs are consistently >5% greater than those by the CROSS method recently developed based on a shallow neural network. Further analysis on the 1000 Genome Project data showed that our predicted unpaired probabilities are highly correlated (>0.8) with the minor allele frequencies at synonymous, non-synonymous mutations, and mutations in untranslated regions, which were higher than those generated by RNAplfold. Moreover, the prediction over all human mRNA indicated a consistent result with previous observation that there is a periodic distribution of unpaired probability on codons. The accurate predictions by our method indicate that such model trained on genome-wide experimental data might be an alternative for analytical methods. Availability and implementation The GRASP is available for academic use at https://github.com/sysu-yanglab/GRASP. Supplementary information Supplementary data are available online.

scholarly journals RAG-Web: RNA structure prediction/design using RNA-As-Graphs

2019 ◽  
Author(s):  
Grace Meng ◽  
Marva Tariq ◽  
Swati Jain ◽  
Shereef Elmetwaly ◽  
Tamar Schlick
Keyword(s):  
Secondary Structure ◽  
Rna Structure ◽  
Structure Prediction ◽  
Rna Secondary Structure ◽  
Three Dimensional ◽  
Coarse Grained ◽  
Supplementary Information ◽  
Tree Graph ◽  
Rna Structure Prediction ◽  
Tree Graphs

Abstract Summary We launch a webserver for RNA structure prediction and design corresponding to tools developed using our RNA-As-Graphs (RAG) approach. RAG uses coarse-grained tree graphs to represent RNA secondary structure, allowing the application of graph theory to analyze and advance RNA structure discovery. Our webserver consists of three modules: (a) RAG Sampler: samples tree graph topologies from an RNA secondary structure to predict corresponding tertiary topologies, (b) RAG Builder: builds three-dimensional atomic models from candidate graphs generated by RAG Sampler, and (c) RAG Designer: designs sequences that fold onto novel RNA motifs (described by tree graph topologies). Results analyses are performed for further assessment/selection. The Results page provides links to download results and indicates possible errors encountered. RAG-Web offers a user-friendly interface to utilize our RAG software suite to predict and design RNA structures and sequences. Availability and implementation The webserver is freely available online at: http://www.biomath.nyu.edu/ragtop/. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals RNA folding in Drosophila shows a distance effect for compensatory fitness interactions.

Genetics ◽  
1993 ◽  
Vol 135 (1) ◽  
pp. 97-103
Author(s):  
W Stephan ◽  
D A Kirby
Keyword(s):  
Linkage Disequilibrium ◽  
Comparative Analysis ◽  
Secondary Structure ◽  
Messenger Rna ◽  
Rna Secondary Structure ◽  
Rna Folding ◽  
Distance Effect ◽  
Physical Distance ◽  
Random Genetic Drift ◽  
Mutation Pressure

Abstract Phylogenetic-comparative analysis was used to construct a secondary structure of Adh precursor messenger RNA (pre-mRNA) in Drosophila. The analysis revealed that the rate of coevolution of base-pairing residues decreases with their physical distance. This result is in qualitative agreement with a model of compensatory fitness interactions which assumes that mutations are individually deleterious but become harmless (neutral) in appropriate combinations. This model predicts that coupled mutations can become fixed in a population under mutation pressure and random genetic drift, when the mutations are closely linked. However, the rate of joint fixation drops as distance between sites increases and recombination breaks up favorable combinations. RNA secondary structure was also used to interpret patterns of linkage disequilibrium at Adh.

Statistical Mechanical Prediction of Ligand Perturbation to RNA Secondary Structure and Application to the SAM-I Riboswitch

2018 ◽  
Author(s):  
Osama Alaidi ◽  
Fareed Aboul-ela
Keyword(s):  
Secondary Structure ◽  
Ligand Binding ◽  
Rna Structure ◽  
Structure Prediction ◽  
Rna Secondary Structure ◽  
Rna Folding ◽  
Tertiary Structure ◽  
Secondary Structure Prediction ◽  
Key Factor ◽  
Statistical Mechanical

ABSTRACTThe realization that non protein-coding RNA (ncRNA) is implicated in an increasing number of cellular processes, many related to human disease, makes it imperative to understand and predict RNA folding. RNA secondary structure prediction is more tractable than tertiary structure or protein structure. Yet insights into RNA structure-function relationships are complicated by coupling between RNA folding and ligand binding. Here, we introduce a simple statistical mechanical formalism to calculate perturbations to equilibrium secondary structure conformational distributions for RNA, in the presence of bound cognate ligands. For the first time, this formalism incorporates a key factor in coupling ligand binding to RNA conformation: the differential affinity of the ligand for a range of RNA-folding intermediates. We apply the approach to the SAM-I riboswitch, for which binding data is available for analogs of intermediate secondary structure conformers. Calculations of equilibrium secondary structure distributions during the transcriptional “decision window” predict subtle shifts due to the ligand, rather than an on/off switch. The results suggest how ligand perturbation can release a kinetic block to the formation of a terminator hairpin in the full-length riboswitch. Such predictions identify aspects of folding that are most affected by ligand binding, and can readily be compared with experiment.

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