<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>

CC BY-SA 4.0
2020-06-03T14:11:13.063Z
Hi Giannis, could you please describe the classification problem you are trying to solve?
Daniel Standage
CC BY-SA 4.0
2020-06-04T05:43:46.687Z
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
Giannis Lazaridis
CC BY-SA 4.0
2020-06-04T07:50:37.090Z
@GiannisLazaridis Please [edit] the question to include the last comment, and include the code or guides you have tried
llrs
CC BY-SA 4.0
2020-06-04T11:23:19.860Z
I edited the question
Giannis Lazaridis
<r><sequence-analysis><phylogenetics>
What steps should I follow for DNA analysis, for a classification problem?
# 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 Giannis, 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**: @GiannisLazaridis Please [edit] the question to include the last comment, and include the code or guides you have tried  
> **username_0**: I edited the question  

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## 0 answers

