text: user2141: I am putting this question because I did not find any useful information from internet because of limited access. My question is related to control (or normal) data that we use for somatic mutation detection (with the help of mutect or SomaticSnipper etc) with tumor data. 
  I don't have control data and my aim is to find somatic mutations in Multiple myeloma WES caner data.
  Here are the questions:
  Can we use Hg19 as a control data? If not, what are the technical reasons of not using hg19 as a control data because it hg19 is consensus call.
  Is there any way to infer (or predict) somatic mutations (either statistically or by something else) out of all possible mutations? For this reason, I want to know what information is given in control data.
  
  user581: Without control data from your subjects, I don't think there's really no way to distinguish somatic mutations from germ-line mutations. The best you can do is to screen out common variants, which are germ-line mutations that are shared by large numbers of individuals using the population frequencies from something like the  Exome Aggregation Consortium:
  annotate_variation.pl -downdb -webfrom annovar -build hg19 exac03 humandb/
  annotate_variation.pl -filter -build hg19 -dbtype exac03 example/ex1.avinput humandb/
  
  
original_text: 2141: <p>I am putting this question because I did not find any useful information from internet because of limited access. My question is related to control (or normal) data that we use for somatic mutation detection (with the help of mutect or SomaticSnipper etc) with tumor data. </p>
  
  <p>I don't have control data and my aim is to find somatic mutations in Multiple myeloma WES caner data.</p>
  
  <p>Here are the questions:</p>
  
  <p>Can we use Hg19 as a control data? If not, what are the technical reasons of not using hg19 as a control data because it hg19 is consensus call.</p>
  
  <p>Is there any way to infer (or predict) somatic mutations (either statistically or by something else) out of all possible mutations? For this reason, I want to know what information is given in control data.</p>
   581: <p>Without control data from your subjects, I don't think there's really no way to distinguish somatic mutations from germ-line mutations. The best you can do is to screen out common variants, which are germ-line mutations that are shared by large numbers of individuals using the population frequencies from something like the  <a href="http://annovar.openbioinformatics.org/en/latest/user-guide/filter/#exac-annotations" rel="nofollow noreferrer">Exome Aggregation Consortium</a>:</p>
  
  <pre><code>annotate_variation.pl -downdb -webfrom annovar -build hg19 exac03 humandb/
  annotate_variation.pl -filter -build hg19 -dbtype exac03 example/ex1.avinput humandb/
  </code></pre>
  