# omilayers
[](https://pip.pypa.io/en/stable/?badge=stable) [](https://pepy.tech/project/omilayers)
``omilayers`` is a Python data management library. It is suitable for multi-omic data analysis, hence the `omi` prefix, that involves the handling of diverse datasets usually referred to as omic layers. `omilayers` wraps the APIs of `SQLite` and `DuckDB` and provides a high-level interface for frequent and repetitive tasks that involve fast storage, processing and retrieval of data without the need to constantly write SQL queries.
The rationale behind `omilayers` is the following:
* User stores **layers** of omic data (tables in SQL lingo).
* User creates new layers by processing and restructuring existing layers.
* User can group layers using **tags**.
* User can store a brief description for each layer.
## Why omilayers?
Although SQL is a straightfoward language, it can become quite tedious task if it needs to be repeated multiple times. Since data analysis involves highly repetitive procedures, a user would need to create functions as a means to abstract the process of writing SQL queries. The aim of `omilayers` is to provide this level of abstaction to facilitate bioinformatic data analysis. The `omilayers` API resembles the `pandas` API and the user needs to write the following code to parse a column named `foo` from a layer called `omicdata`:
with DuckDB (default database)
```python
from omilayers import Omilayers
omi = Omilayers("dbname.duckdb")
result = omi.layers['omicdata']['foo']
```
with SQLite
```python
from omilayers import Omilayers
omi = Omilayers("dbname.sqlite", engine="sqlite")
result = omi.layers['omicdata']['foo']
```
## Installation
```
pip install omilayers
```
## Perform unittests
The directory `testing` includes predefined unittests for SQLite and DuckDB.
To test the functionality of `omilayers` with SQLite:
```bash
python -m unittests -v tests_sqlite.py
```
To test the functionality of `omilayers` with DuckDB:
```bash
python -m unittests -v tests_duckdb.py
```
## Testing with synthetic omic data
The directory `synthetic_data` includes two jupyter notebooks (one for SQLite and one for DuckDB) for testing `omilayers` using synthetic multi-omic data. It also includes the Python script `create_synthetic_vcf/synthesize_vcf.py` that was used to create the synthetic VCF that is hosted in Zenodo [](https://doi.org/10.5281/zenodo.12790872).
The recreation of the synthetic VCF can be done as following:
```bash
for i in {1..22} {X,Y,M};do python synthesize_vcf.py $i;done
```
To join the generated VCFs into a single VCF:
```bash
for i in {1..22} {X,Y,M};do cat chr${i}.vcf >> simulated.vcf;done
```
## Documentation
You can read the full documentation here: [https://omilayers.readthedocs.io](https://omilayers.readthedocs.io/en/latest/)
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"description": "\n# omilayers\n\n[](https://pip.pypa.io/en/stable/?badge=stable) [](https://pepy.tech/project/omilayers)\n\n``omilayers`` is a Python data management library. It is suitable for multi-omic data analysis, hence the `omi` prefix, that involves the handling of diverse datasets usually referred to as omic layers. `omilayers` wraps the APIs of `SQLite` and `DuckDB` and provides a high-level interface for frequent and repetitive tasks that involve fast storage, processing and retrieval of data without the need to constantly write SQL queries.\n\nThe rationale behind `omilayers` is the following:\n\n* User stores **layers** of omic data (tables in SQL lingo).\n* User creates new layers by processing and restructuring existing layers.\n* User can group layers using **tags**.\n* User can store a brief description for each layer.\n\n\n## Why omilayers?\n\nAlthough SQL is a straightfoward language, it can become quite tedious task if it needs to be repeated multiple times. Since data analysis involves highly repetitive procedures, a user would need to create functions as a means to abstract the process of writing SQL queries. The aim of `omilayers` is to provide this level of abstaction to facilitate bioinformatic data analysis. The `omilayers` API resembles the `pandas` API and the user needs to write the following code to parse a column named `foo` from a layer called `omicdata`:\n\nwith DuckDB (default database)\n```python\nfrom omilayers import Omilayers\n\nomi = Omilayers(\"dbname.duckdb\")\nresult = omi.layers['omicdata']['foo']\n```\n\nwith SQLite\n```python\nfrom omilayers import Omilayers\n\nomi = Omilayers(\"dbname.sqlite\", engine=\"sqlite\")\nresult = omi.layers['omicdata']['foo']\n```\n\n\n## Installation\n\n```\npip install omilayers\n```\n\n## Perform unittests\nThe directory `testing` includes predefined unittests for SQLite and DuckDB. \n\nTo test the functionality of `omilayers` with SQLite:\n```bash\npython -m unittests -v tests_sqlite.py\n```\n\nTo test the functionality of `omilayers` with DuckDB:\n```bash\npython -m unittests -v tests_duckdb.py\n```\n\n\n## Testing with synthetic omic data\n\nThe directory `synthetic_data` includes two jupyter notebooks (one for SQLite and one for DuckDB) for testing `omilayers` using synthetic multi-omic data. It also includes the Python script `create_synthetic_vcf/synthesize_vcf.py` that was used to create the synthetic VCF that is hosted in Zenodo [](https://doi.org/10.5281/zenodo.12790872).\n\nThe recreation of the synthetic VCF can be done as following:\n```bash\nfor i in {1..22} {X,Y,M};do python synthesize_vcf.py $i;done\n```\n\nTo join the generated VCFs into a single VCF:\n```bash\nfor i in {1..22} {X,Y,M};do cat chr${i}.vcf >> simulated.vcf;done\n```\n\n\n## Documentation\n\nYou can read the full documentation here: [https://omilayers.readthedocs.io](https://omilayers.readthedocs.io/en/latest/)\n\n",
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