Answers.Body: <p>I am not actively following this field, so take my answer with a grain of salt.</p>
  <p>With bulk data, you can't easily tell how subclones differ from each other or characterize the mutation rates in subclones. It is also challenging to get accurate somatic mutation calls especially when you don't have the matched normal.</p>
  <p>With single-cell data, you usually get more accurate point mutation calls in 20-80% of the genome, depending on the amplification method. You can estimate the mutation rate and call large CNVs more accurately in each cell. Sometimes you can even reconstruct the evolution of subclones. I would say single-cell data is better than bulk data all around. The only problem is that the single-cell approach is more costly as you need to sequence many cells to see the pattern.</p>
  <p>On a side note, MDA is not that accurate for calling point mutations. This may not be a problem if the mutation rate is much higher than the error rate.</p>
  
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Body: <p>I would like to identify the somatic mutations present in a cell line and characterise the genes that are potentially affected by those mutations. For example, are there oncogenes mutated in a subpopulation in the cell line?</p>
  <p>I have currently access to single cell WGS data and sequencing data of the bulk sample derived from the same clone. The data was generate after MDA amplification of the single cell's genome so I am currently evaluating SCCaller and Prosolo for variant calling. If I correctly understand single cell variant calling, mutations called in each cell are called relatively to the bulk background sample. So each cell separately doesn't provide much information on the cell line itself.</p>
  <p>My questions are:</p>
  <ul>
  <li>Do i need single cell WGS to quantify the overall 'genomic health' of a cell line or can I just use deep sequencing followed by somatic variant calling against a reference genome.</li>
  <li>What added value can single cell WGS provide here and how can I extract that information?</li>
  </ul>
  <p>Links to papers, packages or statistical models more than welcome.</p>
  <p>Thanks!</p>
  
Tags: <single-cell><genomics><variant-calling><mutations><variants>
Title: Identifying somatic mutations in cell lines
ThreadText: # Identifying somatic mutations in cell lines
  
  (Asked by: username_0 on 2023-03-10)
  
  I would like to identify the somatic mutations present in a cell line and characterise the genes that are potentially affected by those mutations. For example, are there oncogenes mutated in a subpopulation in the cell line?
  
  I have currently access to single cell WGS data and sequencing data of the bulk sample derived from the same clone. The data was generate after MDA amplification of the single cell's genome so I am currently evaluating SCCaller and Prosolo for variant calling. If I correctly understand single cell variant calling, mutations called in each cell are called relatively to the bulk background sample. So each cell separately doesn't provide much information on the cell line itself.
  
  My questions are:
  
  * Do i need single cell WGS to quantify the overall 'genomic health' of a cell line or can I just use deep sequencing followed by somatic variant calling against a reference genome.
  * What added value can single cell WGS provide here and how can I extract that information?
  
  Links to papers, packages or statistical models more than welcome.
  
  Thanks!
  
  **Tags:** single-cell, genomics, variant-calling, mutations, variants
  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_1 on 2023-03-12. Score: 0
  
  I am not actively following this field, so take my answer with a grain of salt.
  
  With bulk data, you can't easily tell how subclones differ from each other or characterize the mutation rates in subclones. It is also challenging to get accurate somatic mutation calls especially when you don't have the matched normal.
  
  With single-cell data, you usually get more accurate point mutation calls in 20-80% of the genome, depending on the amplification method. You can estimate the mutation rate and call large CNVs more accurately in each cell. Sometimes you can even reconstruct the evolution of subclones. I would say single-cell data is better than bulk data all around. The only problem is that the single-cell approach is more costly as you need to sequence many cells to see the pattern.
  
  On a side note, MDA is not that accurate for calling point mutations. This may not be a problem if the mutation rate is much higher than the error rate.
  
  
  ---
  
  