Closed bjmain closed 3 years ago
Hello @bjmain,
yes, this would be easy. The problem is that the mitochondrial data doesn't have AF%AF_afr%AF_eas%AF_fin%AF_nfe%AF_asj%AF_oth%AF_popmax
columns, but
AF_hom, AF_het, hap_AF_hom, hap_AF_het, hapmax_AF_hom, hapmax_AF_het, pop_AF_hom, pop_AF_het.
Which columns would you need for your analysis? I would have to extend the database with these columns.
Hi Kalin, I would think we would want these columns to be in the database (at minimum it would be pop_AF_hom and pop_AF_het pop_AN):
count (number of individuals in which the variant was filtered out)">
(number of samples with non-missing genotype)">
restricted to variants with a heteroplasmy level >= 0.95">
restricted to variants with a heteroplasmy level >= 0.10 and < 0.95">
individuals for the site">
mapping quality) across individuals with a variant for the site">
likelihood ratio score of variant existing versus not existing) across individuals with a variant for the site">
number for each population, population order: ['afr', 'ami', 'amr', 'asj', 'eas', 'fin', 'nfe', 'oth', 'sas', 'mid']">
each population, population order: ['afr', 'ami', 'amr', 'asj', 'eas', 'fin', 'nfe', 'oth', 'sas', 'mid']">
each population, population order: ['afr', 'ami', 'amr', 'asj', 'eas', 'fin', 'nfe', 'oth', 'sas', 'mid']">
each population, population order: ['afr', 'ami', 'amr', 'asj', 'eas', 'fin', 'nfe', 'oth', 'sas', 'mid']">
each population, population order: ['afr', 'ami', 'amr', 'asj', 'eas', 'fin', 'nfe', 'oth', 'sas', 'mid']">
Best, Brad
On Tue, Jun 29, 2021 at 10:12 PM Kalin Nonchev @.***> wrote:
Hello @bjmain https://github.com/bjmain,
yes, this would be easy. The problem is that the mitochondrial data doesn't have AF%AF_afr%AF_eas%AF_fin%AF_nfe%AF_asj%AF_oth%AF_popmax columns, but AF_hom, AF_het, hap_AF_hom, hap_AF_het, hapmax_AF_hom, hapmax_AF_het, pop_AF_hom, pop_AF_het. Which columns would you need for your analysis? I would have to extend the database with these columns.
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Can you give me more information about your use case and what you want to do? Do you use only the mitochondrial data? This would really help me to understand your problem and how we can solve it most efficiently and at a reasonable time.
This would solve your problem I think.
You can run bcftools
from the command line as
bcftools query -f "%CHROM\t%POS\t%REF\t%ALT\t%AF_hom\t%AF_het\t%excluded_AC\t%AN\t%AC_hom\t%AC_het\t%dp_mean\t%mq_mean\t%tlod_mean\t%pop_AN\t%pop_AC_het\t%pop_AC_hom\t%pop_AF_hom\t%pop_AF_het\n" gnomad.genomes.v3.1.sites.chrM.vcf.bgz | gzip > gnomad.genomes.v3.1.sites.chrM.tsv.gz
and then you can load the table as
import pandas as pd
chrMgnomad = pd.read_csv("../test_dir/gnomad.genomes.v3.1.sites.chrM.tsv.gz", sep="\t", header=None)
chrMgnomad.columns = ["CHROM", "POS", "REF", "ALT", "AF_hom", "AF_het", "excluded_AC", "AN", "AC_hom", "AC_het", "dp_mean", "mq_mean", "tlod_mean", "pop_AN", "pop_AC_het", "pop_AC_hom", "pop_AF_hom", "pop_AF_het"]
chrMgnomad.head()
After that, you can merge on chrome, pos, ref and alt on your dataset. This would be super fast as you are loading everything in memory. The table contains also only 18 164 variants.
Let me know if this works for you or what would the problem be in your use case.
Best,
I am closing this issue as it is not active. Please feel free to reopen it, if the issue is not solved.
Any chance you can update it to handle the mitochondrial data? https://gnomad.broadinstitute.org/downloads#v3-mitochondrial-dna