https://github.com/bio-ontology-research-group/embedpvp
EmbedPVP: Embedding-based Phenotype Variant Predictor
https://github.com/bio-ontology-research-group/embedpvp
bioinformatics biomedical-ontologies genomic-variants knowledge-enhanced-learning neuro-symbolic prioritization-algorithm variant-analysis
Last synced: 6 months ago
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EmbedPVP: Embedding-based Phenotype Variant Predictor
- Host: GitHub
- URL: https://github.com/bio-ontology-research-group/embedpvp
- Owner: bio-ontology-research-group
- License: gpl-3.0
- Created: 2021-06-04T13:59:35.000Z (about 5 years ago)
- Default Branch: main
- Last Pushed: 2024-04-04T17:23:46.000Z (over 2 years ago)
- Last Synced: 2025-10-27T10:49:44.542Z (9 months ago)
- Topics: bioinformatics, biomedical-ontologies, genomic-variants, knowledge-enhanced-learning, neuro-symbolic, prioritization-algorithm, variant-analysis
- Language: Python
- Homepage: https://www.biorxiv.org/content/10.1101/2023.11.08.566179v1
- Size: 176 KB
- Stars: 5
- Watchers: 5
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# EmbedPVP: Embedding-based Phenotype Variant Predictor
Prioritizing genomic variants through neuro-symbolic, knowledge-enhanced learning.
## Annotation data sources (integrated in the candidate SNP prediction workflow)
We integrated the annotations from different sources:
- Gene ontology ([GO](http://geneontology.org/docs/download-go-annotations/))
- Mammalian Phenotype ontology ([MP](http://www.informatics.jax.org/vocab/mp_ontology))
- Human Phenotype Ontology ([HPO](https://hpo.jax.org/app/download/annotation))
- Uber-anatomy ontology ([UBERON](https://www.ebi.ac.uk/ols/ontologies/uberon))
## Dependencies
- The code was developed and tested using Python 3.9.6
> You need to use any version of Python > 3.9 and < 3.10
- We used ([mOWL](https://github.com/bio-ontology-research-group/mowl)) library to process the input dataset as well as generate the embedding representation using different
embedding-based methods.
> You need to have JAVA and JDK installed in your machine.
## Get the data
1. Download all the files from [data](https://drive.google.com/file/d/1QQVG_hzYl1X-rO64zgOX0xvoxv1Ux9of/view?usp=drive_link) and place the uncompressed the file in the folder named `/data`.
2. Download the required database using [CADD](https://cadd.gs.washington.edu/score) and follow the [instructions](https://github.com/kircherlab/CADD-scripts) to generate the TSV file with CADD scores for the input VCF file.
## Use the tool
You can install the tool either from source or PyPi as follows:
### Create a virtual environment
```
python3 -m venv embedpvp_env
source ./embedpvp_env/bin/activate
```
### :ballot_box_with_check: Install from source
```
git clone https://github.com/bio-ontology-research-group/EmbedPVP.git
cd EmbedPVP/
python setup.py install
mkdir output
embedpvp [args]
```
### :ballot_box_with_check: Install from PyPi
```
pip install embedpvp
mkdir output
embedpvp [args]
```
- Run the command `embedpvp --help` to display help and parameters:
```
Initializing the package
Usage: embedpvp [OPTIONS]
Options:
-d, --data-root TEXT Data root folder [required]
-i, --in_file TEXT Annotated Input file [required]
-p, --pathogenicity TEXT Path to the pathogenicity prediction file (CADD)
[required]
-hpo, --hpo TEXT List of phenotype codes separated by commas
[required]
-m, --model_type TEXT Ontology model, one of the following (go , mp ,
hp, uberon, union)
-e, --embedding TEXT Preferred embedding model (e.g. TransD, TransE,
TranR, ConvE ,DistMult, DL2vec, OWL2vc)
[required]
-dir, --outdir TEXT Path to the output directory
-o, --outfile TEXT Path to the results output file
--help Show this message and exit.
```
- **Example:**
`embedpvp -d data/ -i example_annotation.vcf.hg38_multianno.txt -p example_cadd.tsv.gz -hpo HP:0004791,HP:0002020,HP:0100580,HP:0001428,HP:0011459 -m hp -e TransE -dir output/ -o example_output1.tsv`
### Output:
The script will output a ranking a score for the candidate caustive list of variants.
## Reference
For further details or if you used EmbedPVP in your work, please refer to [this article](https://www.biorxiv.org/content/10.1101/2023.11.08.566179v1):
```bibtex
@article{althagafi2023prioritizing,
title={Prioritizing genomic variants through neuro-symbolic, knowledge-enhanced learning},
author={Althagafi, Azza and Zhapa-Camacho, Fernando and Hoehndorf, Robert},
journal={bioRxiv},
pages={2023--11},
year={2023},
publisher={Cold Spring Harbor Laboratory}
}
```
## Note
For any questions or comments please contact azza.althagafi@kaust.edu.sa