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Table 1 In silico tools for predicting effects of synonymous variants on mRNA structure

From: In silico methods for predicting functional synonymous variants

Prediction Algorithms

Tool

Input

Output

Special features

Notes

URL

Ref

Free energy minimization

CoFold

Single nucleotide sequence: limit of 50 kb; 1500 nt sequence has a run-time of approximately ~ 15 s

Structure diagram + predicted MFE (visualized as an arc plot and supports many other output formats)

Two different thermodynamic parameter options + scaling choices

Algorithm considers co-transcriptional folding to improve accuracy of predicting structure of longer sequences

https://e-rna.org/cofold/

[74]

 

remuRNA

Wild-type and mutant sequence (no upper limit)

Structure diagram + predicted MFE; relative entropy plot

 

Algorithm incorporates relative entropy between Boltzmann ensembles of wild-type and mutant secondary structures

https://github.com/bgruening/galaxytools/tree/master/tools/rna_tools/remurna

[72]

 

RNAfold

Single nucleotide sequence: 7500 nt limit for partition function calculations; 10,000 nt limit for free energy minimization prediction

Interactive RNA secondary structure plot; mountain plot

Parameter options to avoid isolated base pairs, to use partition function, and/or exclude GU pairs at end of helices

Algorithm employs partition function calculations in addition to free energy minimization

http://rna.tbi.univie.ac.at/cgi-bin/RNAWebSuite/RNAfold.cgi

[75]

 

UNAFold

Single nucleotide sequence or multiple short sequences (no upper limit)

Predicted MFE; circular structure plot; energy dot plot

Parameter constraint options to optimize loop types, base numbering frequencies, regularization angles

Uses free energy minimization program with folding temperature fixed at 37 °C

http://www.unafold.org/

[71]

Pseudoknots

IPKnot

Single or multiple sequences (FASTA format or multiple sequence alignments)

2D diagram using VARNA program and structure as an arc plot

Options between multiple scoring models and prediction complexity levels

Uses integer programming to compute the maximum expected accuracy structure (MEA)

http://rtips.dna.bio.keio.ac.jp/ipknot/

[81]

 

Kinefold

Single sequence (no upper limit)

Lowest free energy structure diagram + predicted MFE; folding path movie; helix tracing graph

Stochastic simulation—co-transcriptional folding or renaturation folding

Stochastic folding simulations using folding dynamic algorithms [99] and physical constraint modeling for pseudoknot prediction

http://kinefold.curie.fr/

[73]

 

Knotty

Single sequence (no upper limit)

Structure diagram + predicted MFE; provides information on all candidate structures

 

Predicts complex pseudoknot structures with optimization of run-time through sparsification technique and a CCJ-type algorithm

https://github.com/HosnaJabbari/Knotty

[82]

 

LandscapeFold

A list of sequences (up to 2), option to consider intramolecular pseudoknots and define minimum number of nucleotides within each hairpin

Identifies all possible structures and provides indexing/sorting via MFE and equilibrium probabilities

Multiple sequence structural analysis for predicting base interactions with option to assess equilibrium concentrations

Polymer physical model based on entropy calculations of arbitrary pseudoknotted structures

https://github.com/ofer-kimchi/RNA-FE-Landscape

[83]

 

ProbKnot

Single sequence (no upper limit)

Base pair probability plot

Optimization of iterations and minimum helix length

Predicts for presence of pseudoknots in sequence

https://rna.urmc.rochester.edu/RNAstructureWeb/Servers/ProbKnot/ProbKnot.html

[80]

Noncanonical base pairings

CycleFold

Single or multiple sequences (no upper limit), can apply maximum expected accuracy (MEA) or ProbKnot to generate structures

Lowest MFE structure, matrix of pairing probabilities between each nucleotide sequence

TurboFold mode can be engaged to process multiple sequences, considers evolutionary conservation

Uses nucleotide cyclic motifs to predict noncanonical base pairings and minimizes free energy

http://rna.urmc.rochester.edu

[85]

 

MC-Fold-DP

No sequence limit, but runtime scales polynomially

Returns all structures within energy band above the ground state

Cannot currently consider pseudoknots

Prediction based on combining small nucleotide cyclic motifs

https://hackage.haskell.org/package/MC-Fold-DP

[84]

Machine-learning

DMfold

Single sequences (no upper limit)

Folded RNA structure and energy model

Folding parameters automatically determined based on deep learning

Deep-learning and improved base pair maximation principles; trained with 3948 known RNA primary sequences [100]

https://github.com/linyuwangPHD/RNA-Secondary-Structure-Database

[86]

 

SPOT-RNA

Single sequence (maximum—2000 nts); can run longer sequences or batch sequences locally

2D plots of structure through VARNA visualization tool, output of secondary structure motifs can be seen through Vienna format

 

Deep contextual neural network implemented with model training and transfer learning from high quality datasets of > 10,000 RNA structures; trained with bpRNA [101] and PDB [102] databases

https://sparks-lab.org/server/spot-rna/

[87, 88]