Metadata-Version: 2.4
Name: gnomad_db
Version: 0.1.6
Summary: This package scales the huge gnomAD files to a SQLite database, which is easy and fast to query. It extracts from a gnomAD vcf the minor allele frequency for each variant.
Home-page: https://github.com/KalinNonchev/gnomAD_DB
Author: KalinNonchev
Author-email: boo@foo.com
License: MIT License
Requires-Python: >=3.8
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: pandas
Requires-Dist: numpy
Requires-Dist: joblib
Requires-Dist: tqdm
Requires-Dist: pyyaml
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# gnomAD_DB

#### Changelog

#### NEW version (April 2024)
 - release gnomAD WGS v4.1 and WES v4.1 
   - More information [here](https://gnomad.broadinstitute.org/news/2024-04-gnomad-v4-1/).

#### version (November 2023)
 - release gnomAD WGS v4.0 and WES v4.0
 - `gnomad_version`=["v2"|"v3"|"v4"] argument has to be specified when initializing the database
 - minor fixes

#### version (July 2022)
- release gnomAD WGS v3.1.2
- minor bug fixes

#### version (December 2021)
- more available variant features present, check [here](https://github.com/KalinNonchev/gnomAD_DB/blob/master/gnomad_db/pkgdata/gnomad_columns.yaml)
- `get_maf_from_df` renamed to `get_info_from_df`
- `get_maf_from_str` renamed to `get_info_from_str`
- [DEPRECATED 11.2023]`genome`=["Grch37"|"Grch38"] argument has to be specified when initializing the database

## Why and What

[The Genome Aggregation Database (gnomAD)](https://gnomad.broadinstitute.org) 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 <100G and allows scientists to look for various variant annotations present in gnomAD (i.e. Allele Count, Depth, Minor Allele Frequency, etc. - [here](https://github.com/KalinNonchev/gnomAD_DB/blob/master/gnomad_db/pkgdata/gnomad_columns.yaml) 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 information about the exact fields [here](https://github.com/KalinNonchev/gnomAD_DB/blob/master/gnomad_db/pkgdata/gnomad_columns.yaml). 

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

## 1. Download SQLite preprocessed files

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

- WGS gnomAD v4.1 (hg38, **759'336'320** variants) 44.3G zipped, 91G in total - https://zenodo.org/records/11077663/files/gnomad_db.sqlite3.gz?download=1
- WES gnomAD v4.1 (hg38, **183'558'769** variants) 8.3G zipped, 19G in total - https://zenodo.org/records/11076395/files/gnomad_db.sqlite3.gz?download=1
- WGS gnomAD v4.0 (hg38, **759'336'320** variants) 44.3G zipped, 91G in total - https://zenodo.org/records/10453271/files/gnomad_db_wgs_v4.0.sqlite3.gz?download=1
- WES gnomAD v4.0 (hg38, **183'558'769** variants) 8.3G zipped, 19G in total - https://zenodo.org/records/10455901/files/gnomad_db_wes_v4.0.sqlite3.gz?download=1
- WGS gnomAD v3.1.2 (hg38, **759'302'267** variants) 46.2G zipped, 98G in total - https://zenodo.org/record/6818606/files/gnomad_db_v3.1.2.sqlite3.gz?download=1
- WGS 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:

```python
from gnomad_db.database import gnomAD_DB
download_link = "https://zenodo.org/record/6818606/files/gnomad_db_v3.1.2.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 using the preprocessed files to save resources and time**. If you do so, you can go to **2. API usage** and explore the package and its great features!


## 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
```bash
pip install gnomad_db
```

You can use the package like

1. import modules
```python
import pandas as pd
from gnomad_db.database import gnomAD_DB
```

2. Initialize database connection \
**Make sure to have the correct gnomad version!**
```python
# pass dir
database_location = "test_dir"
db = gnomAD_DB(database_location, gnomad_version="v3")
```

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!**
```python
# 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!**
```python
# 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
```python
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!

## Citation

In case you found our work useful, please consider citing us:

```
@misc{gnomad_db,
  author       = {Kalin Nonchev},
  title        = {gnomAD_DB: Scalable SQLite Database for gnomAD VCF Files},
  year         = {2021},
  publisher    = {GitHub},
  journal      = {GitHub repository},
  howpublished = {\url{https://github.com/KalinNonchev/gnomAD_DB}},
  note         = {Accessed: 2025-05-27}
}
```

## Contact

In case, you have questions, please get in touch with [Kalin Nonchev](https://bmi.inf.ethz.ch/people/person/kalin-nonchev).

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