<p>I'm wondering which tool or program is:</p>
<ul>
<li>Most updated; doesn't use a superseded algorithm</li>
<li>Runs very quickly (ideally, under 30s per VCF file)</li>
<li>Able to be automated in some way for a large number of VCFs</li>
<li>I need to at least have these groups: Africans, East Asians, Europeans, and South Asians. More specific groups are fine.</li>
<li>Able to be run locally, as I have a lot of data</li>
</ul>
<p>I am not looking to visualize the data or graph a PCA. My goal is to get a percentage match, or any closeness measure, for each group for each VCF file (assuming one sample per file). All VCFs are generated from microarray data.</p>
<p>For example, Eurogenes k13 and MDLP World-22, especially with oracle, have sufficient results but are only available as a web app.</p>
<p>To clarify, I am asking if there exists <em>any</em> such local program that exists that is peer-reviewed or validated, fast, and automate-able.</p>

CC BY-SA 4.0
2023-05-23T08:30:55.167Z
Solution: use https://github.com/stevenliuyi/admix
BigMistake
CC BY-SA 4.0
2023-05-23T14:10:03.700Z
Please add that as an answer.
Ram RS
CC BY-SA 4.0
2023-05-23T15:55:28.773Z
I'm not sure that it's a good solution enough to post it because as far as I can tell, they're non-validated algorithms aimed at consumers, so it doesn't address the first bullet point.
BigMistake
CC BY-SA 4.0
2023-05-23T20:03:07.017Z
Please mention / discuss your actual problem that you are trying to solve. General "what is the best" questions don't work well for Stack Exchange questions because there's no right answer that works in all cases.
gringer
CC BY-SA 4.0
2023-05-23T20:04:19.657Z
If you're writing it in the comments as a solution to your question, it's good enough as the start of an answer. It can always be updated / improved at a later date.
gringer
CC BY-SA 4.0
2023-05-24T09:01:54.693Z
'Runs very quickly (ideally, under 30s per VCF file)' - whats your sample size. One sample per vcf? #
user438383
CC BY-SA 4.0
2023-05-29T17:54:12.607Z
Ah, good point. I really mean "per sample" instead of per VCF file. I was assuming one sample per vcf, yes.
BigMistake
CC BY-SA 4.0
2023-05-29T18:19:35.100Z
Human genetics ain't my thing but I'd use a published machine learning or deep learning solution with an accuracy of >99%. This will be quick and accurate. It an obvious and easy supervised classification problem which means a large number of groups will already have done it.
M__
<vcf><genome><admixture>
What is the current state-of-the-art for classifying the ancestry groups of VCF files?
# What is the current state-of-the-art for classifying the ancestry groups of VCF files?

(Asked by: username_0 on 2023-05-23)

I'm wondering which tool or program is:

* Most updated; doesn't use a superseded algorithm
* Runs very quickly (ideally, under 30s per VCF file)
* Able to be automated in some way for a large number of VCFs
* I need to at least have these groups: Africans, East Asians, Europeans, and South Asians. More specific groups are fine.
* Able to be run locally, as I have a lot of data

I am not looking to visualize the data or graph a PCA. My goal is to get a percentage match, or any closeness measure, for each group for each VCF file (assuming one sample per file). All VCFs are generated from microarray data.

For example, Eurogenes k13 and MDLP World-22, especially with oracle, have sufficient results but are only available as a web app.

To clarify, I am asking if there exists *any* such local program that exists that is peer-reviewed or validated, fast, and automate-able.

**Tags:** vcf, genome, admixture
> **Comments:**
> **username_0**: Solution: use https://github.com/stevenliuyi/admix  
> **username_1**: Please add that as an answer.  
> **username_0**: I'm not sure that it's a good solution enough to post it because as far as I can tell, they're non-validated algorithms aimed at consumers, so it doesn't address the first bullet point.  
> **username_2**: Please mention / discuss your actual problem that you are trying to solve. General "what is the best" questions don't work well for Stack Exchange questions because there's no right answer that works in all cases.  
> **username_2**: If you're writing it in the comments as a solution to your question, it's good enough as the start of an answer. It can always be updated / improved at a later date.  
> **username_3**: 'Runs very quickly (ideally, under 30s per VCF file)' - whats your sample size. One sample per vcf? #  
> **username_0**: Ah, good point. I really mean "per sample" instead of per VCF file. I was assuming one sample per vcf, yes.  
> **username_4**: Human genetics ain't my thing but I'd use a published machine learning or deep learning solution with an accuracy of >99%. This will be quick and accurate. It an obvious and easy supervised classification problem which means a large number of groups will already have done it.  

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## 0 answers

