KNN_RNA {ftrCOOL}

K-Nearest Neighbor_RNA (KNN_RNA)

Description

This function is like KNNPeptide with the difference that similarity score is computed by Needleman-Wunsch algorithm.

Usage

KNN_RNA(seqs, trainSeq, percent = 30, labeltr = c(), label = c())

Arguments

seqs

is a FASTA file containing ribonucleotide sequences. The sequences start with '>'. Also, seqs could be a string vector. Each element of the vector is a ribonucleotide sequence.

trainSeq

is a fasta file with ribonucleotide sequences. Each sequence starts with a '>' character. Also it could be a string vector such that each element is a ribonucleotide sequence. Eaxh sequence in the training set is associated with a label. The label is found in the parameret labeltr.

percent

determines the threshold which is used to identify sequences (in the training set) which are similar to the input sequence.

labeltr

This parameter is a vector whose length is equivalent to the number of sequences in the training set. It shows class of each sequence in the trainig set.

label

is an optional parameter. It is a vector whose length is equivalent to the number of sequences. It shows the class of each entry (i.e., sequence).

Value

This function returns a feature matrix such that number of columns is number of classes multiplied by percent and number of rows is equal to the number of the sequences.

References

Wei,L., Su,R., Luan,S., Liao,Z., Manavalan,B., Zou,Q. and Shi,X. Iterative feature representations improve N4-methylcytosine site prediction. Bioinformatics, (2019).

Examples

ptmSeqsADR<-system.file("extdata/",package="ftrCOOL")
posSeqs<-fa.read(file=paste0(ptmSeqsADR,"/pos2RNA51.txt"),alphabet="rna")
negSeqs<-fa.read(file=paste0(ptmSeqsADR,"/neg2RNA51.txt"),alphabet="rna")
seqs<-fa.read(file=paste0(ptmSeqsADR,"/testSeq2RNA51.txt"),alphabet="rna")


trainSeq<-c(posSeqs,negSeqs)


labelPos<-rep(1,length(posSeqs))
labelNeg<-rep(0,length(negSeqs))

labeltr<-c(labelPos,labelNeg)

KNN_RNA(seqs=seqs,trainSeq=trainSeq,percent=10,labeltr=labeltr)