scholarly journals LinearPartition: linear-time approximation of RNA folding partition function and base-pairing probabilities

2020 ◽  
Vol 36 (Supplement_1) ◽  
pp. i258-i267 ◽  
Author(s):  
He Zhang ◽  
Liang Zhang ◽  
David H Mathews ◽  
Liang Huang
Keyword(s):  
Free Energy ◽  
Partition Function ◽  
Structure Prediction ◽  
Linear Time ◽  
Minimum Free Energy ◽  
Supplementary Information ◽  
Sequence Length ◽  
Energy Structure ◽  
Base Pairing ◽  
Long Distance

Abstract Motivation RNA secondary structure prediction is widely used to understand RNA function. Recently, there has been a shift away from the classical minimum free energy methods to partition function-based methods that account for folding ensembles and can therefore estimate structure and base pair probabilities. However, the classical partition function algorithm scales cubically with sequence length, and is therefore prohibitively slow for long sequences. This slowness is even more severe than cubic-time free energy minimization due to a substantially larger constant factor in runtime. Results Inspired by the success of our recent LinearFold algorithm that predicts the approximate minimum free energy structure in linear time, we design a similar linear-time heuristic algorithm, LinearPartition, to approximate the partition function and base-pairing probabilities, which is shown to be orders of magnitude faster than Vienna RNAfold and CONTRAfold (e.g. 2.5 days versus 1.3 min on a sequence with length 32 753 nt). More interestingly, the resulting base-pairing probabilities are even better correlated with the ground-truth structures. LinearPartition also leads to a small accuracy improvement when used for downstream structure prediction on families with the longest length sequences (16S and 23S rRNAs), as well as a substantial improvement on long-distance base pairs (500+ nt apart). Availability and implementation Code: http://github.com/LinearFold/LinearPartition; Server: http://linearfold.org/partition. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals PRACTICALITY AND TIME COMPLEXITY OF A SPARSIFIED RNA FOLDING ALGORITHM

2012 ◽  
Vol 10 (02) ◽  
pp. 1241007 ◽  
Author(s):  
SLAVICA DIMITRIEVA ◽  
PHILIPP BUCHER
Keyword(s):  
Time Complexity ◽  
Rna Folding ◽  
Minimum Free Energy ◽  
Energy Structure ◽  
Base Pairing ◽  
Time Analysis ◽  
Folding Algorithm ◽  
Run Time Analysis ◽  
Run Time ◽  
Standard Energy

Commonly used RNA folding programs compute the minimum free energy structure of a sequence under the pseudoknot exclusion constraint. They are based on Zuker's algorithm which runs in time O(n3). Recently, it has been claimed that RNA folding can be achieved in average time O(n2) using a sparsification technique. A proof of quadratic time complexity was based on the assumption that computational RNA folding obeys the "polymer-zeta property". Several variants of sparse RNA folding algorithms were later developed. Here, we present our own version, which is readily applicable to existing RNA folding programs, as it is extremely simple and does not require any new data structure. We applied it to the widely used Vienna RNAfold program, to create sibRNAfold, the first public sparsified version of a standard RNA folding program. To gain a better understanding of the time complexity of sparsified RNA folding in general, we carried out a thorough run time analysis with synthetic random sequences, both in the context of energy minimization and base pairing maximization. Contrary to previous claims, the asymptotic time complexity of a sparsified RNA folding algorithm using standard energy parameters remains O(n3) under a wide variety of conditions. Consistent with our run-time analysis, we found that RNA folding does not obey the "polymer-zeta property" as claimed previously. Yet, a basic version of a sparsified RNA folding algorithm provides 15- to 50-fold speed gain. Surprisingly, the same sparsification technique has a different effect when applied to base pairing optimization. There, its asymptotic running time complexity appears to be either quadratic or cubic depending on the base composition. The code used in this work is available at: http://sibRNAfold.sourceforge.net/ .

scholarly journals LinearTurboFold: Linear-Time RNA Structural Alignment and Conserved Structure Prediction with Applications to Coronaviruses

2020 ◽  
Author(s):  
Sizhen Li ◽  
He Zhang ◽  
Liang Zhang ◽  
Kaibo Liu ◽  
Boxiang Liu ◽  
...  
Keyword(s):  
Structure Prediction ◽  
Linear Time ◽  
Average Length ◽  
Structural Alignment ◽  
Scale Up ◽  
Sequence Length ◽  
Multiple Sequence ◽  
Viral Genomes ◽  
Conserved Regions ◽  
Rna Structural Alignment

Many functional RNA structures are conserved across evolution, and such conserved structures provide critical targets for diagnostics and treatment. TurboFold II is a state-of-the-art software that can predict conserved structures and alignments given homologous sequences, but its cubic runtime and quadratic memory usage with sequence length prevent it from being applied to most full-length viral genomes. As the COVID-19 outbreak spreads, there is a growing need to have a fast and accurate tool to identify conserved regions of SARS-CoV-2. To address this issue, we present LinearTurboFold, which successfully accelerates TurboFold II without sacrificing accuracy on secondary structure and multiple sequence alignment prediction. LinearTurboFold is orders of magnitude faster than Turbo-Fold II, e.g., 372× faster (12 minutes vs. 3.1 days) on a group of five HIV-1 homologs with average length 9,686 nt. LinearTurboFold is able to scale up to the full sequence of SARS-CoV-2, and identifies conserved structures that have been supported by previous studies. Additionally, LinearTurboFold finds a list of novel conserved regions, including long-range base pairs, which may be useful for better understanding the virus.

scholarly journals LinearSampling: Linear-Time Stochastic Sampling of RNA Secondary Structure with Applications to SARS-CoV-2

2020 ◽  
Author(s):  
He Zhang ◽  
Liang Zhang ◽  
Sizhen Li ◽  
David Mathews ◽  
Liang Huang
Keyword(s):  
Partition Function ◽  
Linear Time ◽  
Reference Sequence ◽  
Sequence Length ◽  
Sampling Algorithm ◽  
Stochastic Sampling ◽  
Viral Genomes ◽  
Multiple Structures ◽  
Sampling Algorithms ◽  
Current Sampling

Many RNAs fold into multiple structures at equilibrium. The classical stochastic sampling algorithm can sample secondary structures according to their probabilities in the Boltzmann ensemble, and is widely used, e.g., for accessibility prediction. However, the current sampling algorithm, consisting of a bottom-up partition function phase followed by a top-down sampling phase, suffers from three limitations: (a) the formulation and implementation of the sampling phase are unnecessarily complicated; (b) much redundant work is repeatedly performed in the sampling phase; (c) the partition function runtime scales cubically with the sequence length. These issues prevent it from being used for full-length viral genomes such as SARS-CoV-2. To address these problems, we first present a hypergraph framework under which the sampling algorithm can be greatly simplified. We then present three sampling algorithms under this framework of which two eliminate redundant work in the sampling phase. Finally, we present LinearSampling, an end-to-end linear-time sampling algorithm that is orders of magnitude faster than the standard algorithm. For instance, LinearSampling is 111 times faster (48s vs. 1.5h) than Vienna RNAsubopt on the longest sequence in the RNAcentral dataset that RNAsubopt can run (15,780 nt). More importantly, LinearSampling is the first sampling algorithm to scale to the full genome of SARS-CoV-2, taking only 96 seconds on its reference sequence (29,903 nt). It finds 23 regions of 15 nt with high accessibilities, which can be potentially used for COVID-19 diagnostics and drug design.

scholarly journals Mathematical and Biological Modelling of RNA Secondary Structure and Its Effects on Gene Expression

2006 ◽  
Vol 7 (1) ◽  
pp. 37-43 ◽  
Author(s):  
T. A. Hughes ◽  
J. N. McElwaine
Keyword(s):  
Gene Expression ◽  
Free Energy ◽  
Secondary Structure ◽  
Structure Prediction ◽  
Secondary Structure Prediction ◽  
Secondary Structures ◽  
Minimum Free Energy ◽  
Messenger Rnas ◽  
Rna Sequences ◽  
Translational Machinery

Secondary structures within the 5′ untranslated regions of messenger RNAs can have profound effects on the efficiency of translation of their messages and thereby on gene expression. Consequently they can act as important regulatory motifs in both physiological and pathological settings. Current approaches to predicting the secondary structure of these RNA sequences find the structure with the global-minimum free energy. However, since RNA folds progressively from the 5′ end when synthesised or released from the translational machinery, this may not be the most probable structure. We discuss secondary structure prediction based on local-minimisation of free energy with thermodynamic fluctuations as nucleotides are added to the 3′ end and show that these can result in different secondary structures. We also discuss approaches for studying the extent of the translational inhibition specified by structures within the 5′ untranslated region.

scholarly journals Machine learning a model for RNA structure prediction

2020 ◽  
Vol 2 (4) ◽  
Author(s):  
Nicola Calonaci ◽  
Alisha Jones ◽  
Francesca Cuturello ◽  
Michael Sattler ◽  
Giovanni Bussi
Keyword(s):  
Free Energy ◽  
Rna Structure ◽  
Structure Prediction ◽  
Secondary Structure Prediction ◽  
Structural Information ◽  
Minimum Free Energy ◽  
Experimental Information ◽  
Rna Structures ◽  
Chemical Probing ◽  
Validation Set

Abstract RNA function crucially depends on its structure. Thermodynamic models currently used for secondary structure prediction rely on computing the partition function of folding ensembles, and can thus estimate minimum free-energy structures and ensemble populations. These models sometimes fail in identifying native structures unless complemented by auxiliary experimental data. Here, we build a set of models that combine thermodynamic parameters, chemical probing data (DMS and SHAPE) and co-evolutionary data (direct coupling analysis) through a network that outputs perturbations to the ensemble free energy. Perturbations are trained to increase the ensemble populations of a representative set of known native RNA structures. In the chemical probing nodes of the network, a convolutional window combines neighboring reactivities, enlightening their structural information content and the contribution of local conformational ensembles. Regularization is used to limit overfitting and improve transferability. The most transferable model is selected through a cross-validation strategy that estimates the performance of models on systems on which they are not trained. With the selected model we obtain increased ensemble populations for native structures and more accurate predictions in an independent validation set. The flexibility of the approach allows the model to be easily retrained and adapted to incorporate arbitrary experimental information.

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