<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>

CC BY-SA 3.0
2018-04-17T05:21:46.993Z
@heatthobrien: Thanks for the reply. What about hg19 reference genome as a control data. Can I use it as a control data for all patients?
Lot_to_learn
CC BY-SA 3.0
2018-04-17T05:37:45.780Z
hg19 is a composite of a small number of individuals, but it doesn't capture any of the variation between individuals. You can use it as a reference to identify variants, but most of the variants you find will have been inherited from you're subjects' parents.
heathobrien
CC BY-SA 3.0
2018-04-17T07:21:30.743Z
My idea was to use probability mapping and frequency mapping from a consensus call to indentify somatic mutations with the help of motif visualization tools. It may be little bit difficult, time taking or prone to error method. But I have here no choice because I don't have the control data. I also don't know if it is right way. (But sounds practical because we can statistically infer that most probabely it can be somatic mutation.
Lot_to_learn
CC BY-SA 3.0
2018-04-17T07:23:26.903Z
One more suggestion can you tell me is there any way to use Genome Aggregation Database in ANNOVAR because I have checked the ANNOVAR doccumentation and I don't find any way to use this database.
Lot_to_learn
CC BY-SA 3.0
2018-04-17T08:57:43.703Z
Without control data any method you try will be a waste of time.
Devon Ryan
CC BY-SA 3.0
2018-04-17T09:46:47.993Z
It doesn't look like it, but you can use `ExAC`, which is the predecessor to  `gnomAD`. See my updated answer.
heathobrien
CC BY-SA 3.0
2018-04-16T10:28:14.327Z
2018-04-17T09:44:25.207Z
2018-04-17T09:44:25.207Z
heathobrien
<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>

CC BY-SA 3.0
2018-04-06T07:18:48.763Z
Could you explain what articles or what research have you done to answer this questions. Also this is a list of questions, could you focus on one and open another post with others (ie question 5 is unrelated to question 1-4), so ask it in other posts (but be aware that you should show your research in each question you post)
llrs
CC BY-SA 3.0
2018-04-06T09:22:49.483Z
@Llopis: Thanks for suggestion. I tried to add more descriptions and make it more valuable.
Lot_to_learn
<database><cancer><mutations><somatic>
Information about control data
# Information about control data

(Asked by: username_0 on 2018-04-06)

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.

**Tags:** database, cancer, mutations, somatic
> **Comments:**
> **username_1**: Could you explain what articles or what research have you done to answer this questions. Also this is a list of questions, could you focus on one and open another post with others (ie question 5 is unrelated to question 1-4), so ask it in other posts (but be aware that you should show your research in each question you post)  
> **username_0**: @Llopis: Thanks for suggestion. I tried to add more descriptions and make it more valuable.  

---

## 1 answers

### Answer by username_2 on 2018-04-16. Score: 2

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/

> **Comments:**
> **username_0**: @heatthobrien: Thanks for the reply. What about hg19 reference genome as a control data. Can I use it as a control data for all patients?  
> **username_2**: hg19 is a composite of a small number of individuals, but it doesn't capture any of the variation between individuals. You can use it as a reference to identify variants, but most of the variants you find will have been inherited from you're subjects' parents.  
> **username_0**: My idea was to use probability mapping and frequency mapping from a consensus call to indentify somatic mutations with the help of motif visualization tools. It may be little bit difficult, time taking or prone to error method. But I have here no choice because I don't have the control data. I also don't know if it is right way. (But sounds practical because we can statistically infer that most probabely it can be somatic mutation.  
> **username_0**: One more suggestion can you tell me is there any way to use Genome Aggregation Database in ANNOVAR because I have checked the ANNOVAR doccumentation and I don't find any way to use this database.  
> **username_3**: Without control data any method you try will be a waste of time.  
> **username_2**: It doesn't look like it, but you can use `ExAC`, which is the predecessor to  `gnomAD`. See my updated answer.  

---

