gnomAD_DB

NEW version (December 2021)

• more available variant features present, check here
• get_maf_from_df renamed to get_info_from_df
• get_maf_from_str renamed to get_info_from_str
• genome=["Grch37"|"Grch38"] argument have to be specified, when initializing the database

The Genome Aggregation Database (gnomAD) is a resource developed by an international coalition of investigators, with the goal of aggregating and harmonizing both exome and genome sequencing data from a wide variety of large-scale sequencing projects, and making summary data available for the wider scientific community.

This package scales the huge gnomAD files (on average ~120G/chrom) to a SQLite database with a size of 34G for WGS v2.1.1 (261.942.336 variants) and 99G for WGS v3.1.1 (about 759.302.267 variants), and allows scientists to look for various variant annotations present in gnomAD (i.e. Allele Count, Depth, Minor Allele Frequency, etc. - here you can find all selected features given the genome version). (A query containing 300.000 variants takes ~40s.)

It extracts from a gnomAD vcf about 23 variant annotations. You can find further infromation about the exact fields here.

The package works for all currently available gnomAD releases.(January 2022)

1. Download SQLite preprocessed files

I have preprocessed and created sqlite3 files for gnomAD v2.1.1 and 3.1.1 for you, which can be easily downloaded from here. They contain all variants on the 24 standard chromosomes.

gnomAD v3.1.1 (hg38, 759'302'267 variants) 46.9G zipped, 99G in total - https://zenodo.org/record/5758663/files/gnomad_db_v3.1.1.sqlite3.gz?download=1
gnomAD v2.1.1 (hg19, 261'942'336 variants) 16.1G zipped, 48G in total - https://zenodo.org/record/5770384/files/gnomad_db_v2.1.1.sqlite3.gz?download=1

You can download it as:

from gnomad_db.database import gnomAD_DB
download_link = "https://zenodo.org/record/5770384/files/gnomad_db_v2.1.1.sqlite3.gz?download=1"
output_dir = "test_dir" # database_location
gnomAD_DB.download_and_unzip(download_link, output_dir)

NB this would take ~30min (network speed 10mb/s)

or you can create the database by yourself. However, I recommend to use the preprocessed files to save ressources and time. If you do so, you can go to 2. API usage and explore the package and its great features!

1.1 Data preprocessing and SQL database creation

Start by downloading the vcf files from gnomAD in a single directory:

wget -c link_to_gnomAD.vcf.bgz

After that specify the arguments in the script_config.yaml.

database_location: "test_out" # where to create the database, make sure you have space on your device.
gnomad_vcf_location: "data" # where are your *.vcf.bgz located
tables_location: "test_out" # where to store the preprocessed intermediate files, you can leave it like this 
script_locations: "test_out" # where to store the scripts, where you can check the progress of your jobs, you can leave it like this
genome: "Grch37" # genome version of the gnomAD vcf file (2.1.1 = Grch37, 3.1.1 = Grch38)

Once this is done, run

conda env create -f environment.yaml
conda activate gnomad_db
python -m ipykernel install --user --name gnomad_db --display-name "gnomad_db"

to prepare your conda environment

Finally, you can trigger the snakemake pipeline which will create the SQL database

snakemake --cores 12

2. API usage

Congratulations, your database is set up! Now it is time to learn how to use it.

First, you can install the package in the gnomad_db env or in the one which you are going to use for your downstream analysis

pip install gnomad_db

You can use the package like

1. import modules

import pandas as pd
from gnomad_db.database import gnomAD_DB

2. Initialize database connection

# pass dir
database_location = "test_dir"
db = gnomAD_DB(database_location, genome="Grch38")

3. Insert some test variants to run the examples below
   If you have downloaded the preprocessed sqlite3 files, you can skip this step as you already have variants, make sure to have the correct genome version!

# get some variants
var_df = pd.read_csv("data/test_vcf_gnomad_chr21_10000.tsv.gz", sep="\t", names=db.columns, index_col=False)
# IMPORTANT: The database removes internally chr prefix (chr1->1)
# insert these variants
db.insert_variants(var_df)

4. Query variant minor allele frequency
   These example variants are assembled to hg38!

# query some MAF scores
dummy_var_df = pd.DataFrame({
    "chrom": ["1", "21"], 
    "pos": [21, 9825790], 
    "ref": ["T", "C"], 
    "alt": ["G", "T"]})

# query from dataframe AF column
db.get_info_from_df(dummy_var_df, "AF")

# query from dataframe AF and AF_popmax columns
db.get_info_from_df(dummy_var_df, "AF, AF_popmax")

# query from dataframe all columns
db.get_info_from_df(dummy_var_df, "*")

# query from string
db.get_info_from_str("21:9825790:C>T", "AF")

5. You can query also intervals of minor allele frequencies

db.get_info_for_interval(chrom=21, interval_start=9825780, interval_end=9825799, query="AF")

For more information on how to use the package, look into GettingStartedwithGnomAD_DB.ipynb notebook!

NB: The package is under development and any use cases suggestions/extensions and feedback are welcome.