predictAlternativeSecondaryStructures: Predicts alternative secondary structures of a given RNA...

In LaraSellesVidal/ncRNAtools: An R toolkit for non-coding RNA

Description

Attempts to identify potential alternative secondary structures of the provided RNA sequence using the RintW method, based on the decomposition of the base-pairing probability matrix over the Hamming distance to a reference secondary structure. It should be noted that RintW runs can take considerable amounts of time.

Usage

predictAlternativeSecondaryStructures(sequence, gammaWeight=4, inferenceEngine="BL")

Arguments

sequence	string with an RNA sequence whose secondary structure should be predicted. Should contain only standard RNA symbols (i.e., "A", "U", "G" and "C").
gammaWeight	weight factor for predicted base pairs. It directly affects the number of predicted base pairs. A higher value leads to a higher number of base pairs predicted. It should be a positive number. In the default behavior, a value of 4 is used.
inferenceEngine	engine used to identify the optimal canonical secondary structure. Possible values are "BL", "Turner" and "CONTRAfold". In the first two cases, a McCaskill partition function is applied, using respectively the Boltzmann likelihood model or Turner's energy model. In the third case, the CONTRAfold engine, based on conditional log-linear models, is applied. In the default behavior, a McCaskill partition function with a Boltzmann likelihood model is used.

Value

A list of two-element lists, where each element of the upper level list represents a potential secondary structure. The first top-level element always represents the canonical secondary structure. If no alternative secondary structures are found, simply a list of two elements is returned, comprising the query sequence and the canonical secondary structure.

When alternative secondary structure elements are found, each top-level element comprises the following two elements:

sequence	Query RNA sequence
secondaryStructure	Predicted secondary structure

References

Andronescu M, Condon A, Hoos HH, Mathews DH, Murphy KP. Computational approaches for RNA energy parameter estimation. RNA. 2010;16(12):2304-2318. doi:10.1261/rna.1950510

Do CB, Woods DA, Batzoglou S. CONTRAfold: RNA secondary structure prediction without physics-based models. Bioinformatics. 2006;22(14):e90-e98. doi:10.1093/bioinformatics/btl246

Hagio T, Sakuraba S, Iwakiri J, Mori R, Asai K. Capturing alternative secondary structures of RNA by decomposition of base-pairing probabilities. BMC Bioinformatics. 2018;19(Suppl 1):38. Published 2018 Feb 19. doi:10.1186/s12859-018-2018-4

Hamada M, Ono Y, Kiryu H, et al. Rtools: a web server for various secondary structural analyses on single RNA sequences. Nucleic Acids Res. 2016;44(W1):W302-W307. doi:10.1093/nar/gkw337

Mathews DH, Sabina J, Zuker M, Turner DH. Expanded sequence dependence of thermodynamic parameters improves prediction of RNA secondary structure. J Mol Biol. 1999;288(5):911-940. doi:10.1006/jmbi.1999.2700

http://rtools.cbrc.jp/

Examples

# Predict alternative secondary structures of an RNA sequence:

alternativeStructures <- predictAlternativeSecondaryStructures("AAAGGGGUUUCCC")

# Count the number of potential alternative structures identified:

length(alternativeStructures)

LaraSellesVidal/ncRNAtools documentation built on Oct. 17, 2020, 6:03 a.m.