featureStructure: Extraction of the Secondary Structural Features of RNA and...

In HAN-Siyu/ncProR: Predicting Long non-coding RNA-Protein Interaction

Extraction of the Secondary Structural Features of RNA and Protein Sequences

Description

Basically a wrapper for computeStructure function. This function can extract secondary structure features of RNA and protein sequences at the same time and format the results as the dataset that can be used to build classifier. ViennaRNA package and Predator is required.

Usage

featureStructure(
  seqRNA,
  seqPro,
  label = NULL,
  parallel.cores = 2,
  cl = NULL,
  ...
)

Arguments

seqRNA	RNA sequences loaded by function read.fasta from seqinr-package. Or a list of RNA sequences. RNA sequences will be converted into lower case letters. Each sequence should be a vector of single characters.
seqPro	protein sequences loaded by function read.fasta from seqinr-package. Or a list of protein sequences. Protein sequences will be converted into upper case letters. Each sequence should be a vector of single characters.
label	optional. A string or a vector of strings or NULL. Indicates the class of the samples such as "Interact", "Non.Interact". Default: NULL.
parallel.cores	an integer that indicates the number of cores for parallel computation. Default: 2. Set parallel.cores = -1 to run with all the cores. parallel.cores should be == -1 or >= 1.
cl	parallel cores to be passed to this function.
...	arguments (structureRNA.num, structurePro, Fourier.len, workDir.Pro, path.RNAsubopt, path.Predator and path.stride passed to function computeStructure. See example below.

Details

see computeStructure.

Value

This function returns a data frame.

References

[1] Han S, Yang X, Sun H, et al. LION: an integrated R package for effective prediction of ncRNA–protein interaction. Briefings in Bioinformatics. 2022; 23(6):bbac420

[2] Chou PY, Fasman GD. Prediction of the secondary structure of proteins from their amino acid sequence. Adv. Enzymol. Relat. Areas Mol. Biol. 1978; 47:45-148

[3] Deleage G, Roux B. An algorithm for protein secondary structure prediction based on class prediction. Protein Eng. Des. Sel. 1987; 1:289-294

[4] Levitt M. Conformational preferences of amino acids in globular proteins. Biochemistry 1978; 17:4277-85

[5] Frishman D, Argos P. Incorporation of non-local interactions in protein secondary structure prediction from the amino acid sequence. Protein Eng. 1996; 9:133-42

[6] Lorenz R, Bernhart SH, Honer zu Siederdissen C, et al. ViennaRNA Package 2.0. Algorithms Mol. Biol. 2011; 6:26

[7] Lu Q, Ren S, Lu M, et al. Computational prediction of associations between long non-coding RNAs and proteins. BMC Genomics 2013; 14:651

See Also

runRNAsubopt, runPredator, computeStructure

Examples

data(demoNegativeSeq)
seqsRNA <- demoNegativeSeq$RNA.negative
seqsPro <- demoNegativeSeq$Pro.negative

# Use your own paths of the program Predator and file "stride.dat". For example:

path.Predator <- "/mnt/external_drive_1/hansy/predator/predator"
path.stride <- "/mnt/external_drive_1/hansy/predator/stride.dat"

# Pass "structureRNA.num", "structurePro", "Fourier.len", "workDir.Pro",
# "path.RNAsubopt", "path.Predator" and "path.stride" using "..." argument:

dataset <- featureStructure(seqRNA = seqsRNA, seqPro = seqsPro, label = "Non.Interact",
                            parallel.cores = 2, structureRNA.num = 6,
                            structurePro = c("ChouFasman", "DeleageRoux", "Levitt"),
                            Fourier.len = 10, workDir.Pro = "tmpDir",
                            path.RNAsubopt = "RNAsubopt", path.Predator = path.Predator,
                            path.stride = path.stride)

HAN-Siyu/ncProR documentation built on Nov. 3, 2023, 12:08 a.m.