I have a bed file which contains DNA sequences information as follow:
track name="194" description="194 methylation (sites)" color=0,60,120 useScore=1 chr1 15864 15866 FALSE 894 + chr1 534241 534243 FALSE 921 - chr1 710096 710098 FALSE 729 + chr1 714176 714178 FALSE 12 - chr1 720864 720866 FALSE 988 -
I loaded the bed file in R and named the matrix DataSet. I used the follow code to get the sequences:
mydataSet_Test1<-dataSet[,1:3] library(BSgenome.Hsapiens.UCSC.hg19) genome <- BSgenome.Hsapiens.UCSC.hg19 chr<-as.matrix(as.character(mydataSet_Test1[,1])) #50 start<-as.matrix(as.integer(as.character(mydataSet_Test1[,2]))-50) end<-as.matrix(as.integer(as.character(mydataSet_Test1[,3]))+50) Seqs50_Test1<-getSeq(genome,chr,start=start,end=end)
I'm having DNA sequences and for each sequence, I have its Methylation Number (i.e. 800). I have split the data to hypomethylated (methylation < 200, class 0) and hypermethylated (methylation > 800, class 1) and I want to build a model that can predict if a sequence (or a group of sequences) is hypo or hyper - methylated.
I now have a matrix of 358.367 data and 2 columns. The first column is the DNA sequences and the second column is the class for each sequence (0 or 1).
I want to build a classification model in R, using SVM algorithm and 83 features (dinucleotides, trinucleotides, etc.).
Before calculating the features and building the model, are there any steps I should follow for my DNA sequence data (each row of the matrix is a sequence from the human genome), like filtering CpG islands or sequences, homolog reduction or overlapping sequences etc?
As you understand I have a lack in DNA analysis.