Body: <p>I have a bed file which contains DNA sequences information as follow:</p>
  
  <p>**</p>
  
  <pre><code>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 -
  </code></pre>
  
  <p>**</p>
  
  <p>I loaded the bed file in R and named the matrix <strong>DataSet</strong>.
  I used the follow code to get the sequences:</p>
  
  <pre><code>mydataSet_Test1&lt;-dataSet[,1:3]
  
  library(BSgenome.Hsapiens.UCSC.hg19)
  genome &lt;- BSgenome.Hsapiens.UCSC.hg19
  chr&lt;-as.matrix(as.character(mydataSet_Test1[,1]))
  
  #50
  start&lt;-as.matrix(as.integer(as.character(mydataSet_Test1[,2]))-50)
  end&lt;-as.matrix(as.integer(as.character(mydataSet_Test1[,3]))+50)
  Seqs50_Test1&lt;-getSeq(genome,chr,start=start,end=end)
  </code></pre>
  
  <p>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 &lt; 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.</p>
  
  <p>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).</p>
  
  <p>I want to build a classification model in R, using SVM algorithm and 83 features (dinucleotides, trinucleotides, etc.).</p>
  
  <p>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?</p>
  
  <p>As you understand I have a lack in DNA analysis.</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-06-03T14:11:13.063Z
Comments.Text: Hi Giannis, could you please describe the classification problem you are trying to solve?
Comments.UserDisplayName: Daniel Standage
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-06-04T05:43:46.687Z
Comments.Text: 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
Comments.UserDisplayName: Giannis Lazaridis
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-06-04T07:50:37.090Z
Comments.Text: @GiannisLazaridis Please [edit] the question to include the last comment, and include the code or guides you have tried
Comments.UserDisplayName: llrs
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-06-04T11:23:19.860Z
Comments.Text: I edited the question
Comments.UserDisplayName: Giannis Lazaridis
Tags: <r><sequence-analysis><phylogenetics>
Title: What steps should I follow for DNA analysis, for a classification problem?
ThreadText: # What steps should I follow for DNA analysis, for a classification problem?
  
  (Asked by: username_0 on 2020-06-03)
  
  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.
  
  **Tags:** r, sequence-analysis, phylogenetics
  
  > **Comments:**
  > **username_1**: Hi username_0, could you please describe the classification problem you are trying to solve?  
  > **username_0**: 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  
  > **username_2**: @username_0 Please [edit] the question to include the last comment, and include the code or guides you have tried  
  > **username_0**: I edited the question  
  
  ---
  
  ## 0 answers
  
  