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#' k riboNucleotide Composition (kNUComposition_RNA)
#'
#' This function calculates the frequency of all k-mers in the sequence.
#'
#' @param 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.
#'
#'
#' @param rng This parameter can be a number or a vector. Each entry of the vector holds the value of k in the k-mer composition.
#' For each k in the rng vector, a new vector (whose size is 4^k) is created which contains the frequency of kmers.
#'
#' @param upto It is a logical parameter. The default value is FALSE. If rng is a number and upto is set to TRUE, rng is converted
#' to a vector with values from 1 to rng.
#'
#' @param reverse It is a logical parameter which assumes the reverse complement of the sequence.
#'
#' @param normalized is a logical parameter. When it is FALSE, the return value of the function does not change. Otherwise, the return value is normalized using the length of the sequence.
#'
#' @param ORF (Open Reading Frame) is a logical parameter. If it is set to true, ORF region of each sequence is considered instead of the original sequence (i.e., 3-frame).
#'
#' @param reverseORF is a logical parameter. It is enabled only if ORF is true.
#' If reverseORF is true, ORF region will be searched in the sequence and also in the reverse complement of the sequence (i.e., 6-frame).
#'
#' @param 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).
#'
#' @return This function returns a feature matrix. The number of rows is equal to the number of sequences and
#' the number of columns depends on the rng vector. For each value k in the vector, (4)^k columns are created in the matrix.
#'
#' @export
#'
#' @examples
#'
#' fileLNC<-system.file("extdata/Carica_papaya101RNA.txt",package="ftrCOOL")
#' mat<-kNUComposition_RNA(seqs=fileLNC,rng=c(1,3))
#'
kNUComposition_RNA<-function(seqs,rng=3,reverse=FALSE,upto=FALSE,normalized=TRUE,ORF=FALSE,reverseORF=TRUE,label=c()){
if(length(seqs)==1&&file.exists(seqs)){
seqs<-fa.read(seqs,alphabet="rna")
seqs_Lab<-alphabetCheck(seqs,alphabet = "rna",label)
seqs<-seqs_Lab[[1]]
label<-seqs_Lab[[2]]
}
else if(is.vector(seqs)){
seqs<-sapply(seqs,toupper)
seqs_Lab<-alphabetCheck(seqs,alphabet = "rna",label)
seqs<-seqs_Lab[[1]]
label<-seqs_Lab[[2]]
}
else {
stop("ERROR: Input sequence is not in the correct format. It should be a FASTA file or a string vector.")
}
if(ORF==TRUE){
seqs=maxORF_RNA(seqs,reverse=reverseORF)
}
if(upto==TRUE && length(rng)==1){
l<-length(rng)
l<-rng[l]
rng<-1:l
}
rng <- sort(rng)
rng <- unique(rng)
len<-length(rng)
dict<-list("A"=1,"C"=2,"G"=3,"U"=4)
mergedMatrix<-vector(mode = "numeric")
numSeqs<-length(seqs)
for(l in rng){
featureMatrix<-matrix(0,ncol = (4^l),nrow = numSeqs)
namesKmer<-nameKmer(l,type = "rna")
colnames(featureMatrix)<-namesKmer
if (l%%2==1)
{
size <- (4^l)/2
} else{
size <- ((4^l)/2)+(4^(l/2)/2)
}
for(n in 1:numSeqs){
seq<-seqs[n]
seqChars<-unlist(strsplit(seq,split = ""))
lenSeq<-length(seqChars)
kmers<-""
#create all kmers occure in the seq
for (i in 0:(l-1)){
temp<-seqChars[(1+i):(lenSeq-(l-1-i))]
kmers<-paste(kmers,temp,sep = "")
}
# table kmers of the seq
tabKmers<-table(kmers)
# a vector with name for each kmer
tabNames<-names(tabKmers)
featureMatrix[n,tabNames]<-tabKmers
}
if(reverse==TRUE){
rev<-sapply(namesKmer, revComp)
smaller<-(namesKmer<rev)
for(i in 1:length(rev)){
if(smaller[i]){
featureMatrix[,i]<-featureMatrix[,i]+featureMatrix[,rev[i]]
}
}
smallerEqual<-(namesKmer<=rev)
featureMatrix<-featureMatrix[,smallerEqual]
}
mergedMatrix<-cbind(mergedMatrix,featureMatrix)
}
if(normalized==TRUE){
seqLen<-sapply(seqs, nchar)
mergedMatrix<-mergedMatrix/seqLen
}
if(length(label)==numSeqs){
mergedMatrix<-as.data.frame(mergedMatrix)
mergedMatrix<-cbind(mergedMatrix,label)
}
row.names(mergedMatrix)<-names(seqs)
return(mergedMatrix)
}
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