<p>You can search databases like Cosmic(Cancer) with the gene names to get frequently occurring mutations in cancer. I'm not sure about any specific tool/pipeline available for this exact scenario.</p>

<p>As an alternative approach literature mining for gene associated mutation discovery, you can use <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4926749/" rel="nofollow noreferrer">text mining</a> with machine learning to automate the whole process.</p>

<p>With no specific context in mind, you will end with several combinations of mutation per gene. Then for each variation translate the DNA sequence and match the result with reference peptide sequence you have.</p>

CC BY-SA 3.0
2018-02-04T09:10:49.357Z
2018-02-04T09:10:49.357Z
arup
<p>I don't have a complete answer to your question, but as a primer I can tell you that you should look into the Bioconductor packages. The reason is that biological sequence data are deposited in online databases (like GenBank, and others), and exposed by these databases in such a way that you precisely don't need web scrapping techniques to retrieve the data you're interested in.</p>

<p><code>rvest</code> is a great package for web scrapping, but definitely not the appropriate tool to solve your problem.</p>

CC BY-SA 3.0
2018-02-05T23:45:24.137Z
2018-02-05T23:45:24.137Z
Guillaume
<p>I have ~130 gene names of different human proteins.
I'm looking for a convenient and systematic way to locate each gene's location in the genome, extract its CDS sequence and find whether there are SNPs in the CDS (in any context) and what's their position.
Moreover, I've chosen for each of those proteins, several amino acids for whom I'm trying to find if the SNP fall in their exact coding nucleotide region (the amino acid triad of nucleotides).</p>
<p>Alternatively, I'm looking for an algorithm that would take the gene name out of my list, find its location in the genome and look for SNPs in the exons - if it would found any SNPs, then it should translate the exon to an amino acid sequence and find whether one of my pre-defined amino acids from each protein is translated by the nucleotide site (triad of nucleotides) with the SNP.</p>
<p>I'm using R, and I thought to use web scraping (should I use the <code>rvest</code> package?) but I'm not quite familiar with the technique. Is it possible?
What websites should I use? Genome Browser? EMBOSS Transeq? Other websites?</p>
<p>Thank you</p>

CC BY-SA 3.0
2018-02-04T08:44:21.203Z
Why do you need to do web scraping instead of using `biomaRt` and the packages of Bioconductor to find and retrieve the information? Did you look up how do other papers obtain the sequence and positions? I think your attempt parsing webs is full of problems if not unfeasible.
llrs
CC BY-SA 3.0
2018-02-05T09:06:26.353Z
"convenient and **systematic** way to map genes..." we can only dream.
zx8754
<r><proteins><ngs><pdb><uniprot>
Web scraping in R
# Web scraping in R

(Asked by: username_0 on 2018-02-03)

I have ~130 gene names of different human proteins.
I'm looking for a convenient and systematic way to locate each gene's location in the genome, extract its CDS sequence and find whether there are SNPs in the CDS (in any context) and what's their position.
Moreover, I've chosen for each of those proteins, several amino acids for whom I'm trying to find if the SNP fall in their exact coding nucleotide region (the amino acid triad of nucleotides).

Alternatively, I'm looking for an algorithm that would take the gene name out of my list, find its location in the genome and look for SNPs in the exons - if it would found any SNPs, then it should translate the exon to an amino acid sequence and find whether one of my pre-defined amino acids from each protein is translated by the nucleotide site (triad of nucleotides) with the SNP.

I'm using R, and I thought to use web scraping (should I use the `rvest` package?) but I'm not quite familiar with the technique. Is it possible?
What websites should I use? Genome Browser? EMBOSS Transeq? Other websites?

Thank you

**Tags:** r, proteins, ngs, pdb, uniprot
> **Comments:**
> **username_1**: Why do you need to do web scraping instead of using `biomaRt` and the packages of Bioconductor to find and retrieve the information? Did you look up how do other papers obtain the sequence and positions? I think your attempt parsing webs is full of problems if not unfeasible.  
> **username_2**: "convenient and **systematic** way to map genes..." we can only dream.  

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## 2 answers

### Answer by username_3 on 2018-02-04. Score: 1

You can search databases like Cosmic(Cancer) with the gene names to get frequently occurring mutations in cancer. I'm not sure about any specific tool/pipeline available for this exact scenario.

As an alternative approach literature mining for gene associated mutation discovery, you can use [text mining](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4926749/) with machine learning to automate the whole process.

With no specific context in mind, you will end with several combinations of mutation per gene. Then for each variation translate the DNA sequence and match the result with reference peptide sequence you have.


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### Answer by username_4 on 2018-02-05. Score: 1

I don't have a complete answer to your question, but as a primer I can tell you that you should look into the Bioconductor packages. The reason is that biological sequence data are deposited in online databases (like GenBank, and others), and exposed by these databases in such a way that you precisely don't need web scrapping techniques to retrieve the data you're interested in.

`rvest` is a great package for web scrapping, but definitely not the appropriate tool to solve your problem.


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