{"id":41922463,"url":"https://github.com/bio-ontology-research-group/embedpvp","last_synced_at":"2026-01-25T17:03:03.668Z","repository":{"id":203356555,"uuid":"373859347","full_name":"bio-ontology-research-group/EmbedPVP","owner":"bio-ontology-research-group","description":"EmbedPVP: Embedding-based Phenotype Variant 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unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["bioinformatics","biomedical-ontologies","genomic-variants","knowledge-enhanced-learning","neuro-symbolic","prioritization-algorithm","variant-analysis"],"created_at":"2026-01-25T17:03:02.899Z","updated_at":"2026-01-25T17:03:03.658Z","avatar_url":"https://github.com/bio-ontology-research-group.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# EmbedPVP: Embedding-based Phenotype Variant Predictor \nPrioritizing genomic variants through neuro-symbolic, knowledge-enhanced learning.\n\n## Annotation data sources (integrated in the candidate SNP prediction workflow)\nWe integrated the annotations from different sources:\n- Gene ontology ([GO](http://geneontology.org/docs/download-go-annotations/))\n- Mammalian Phenotype ontology ([MP](http://www.informatics.jax.org/vocab/mp_ontology))\n- Human Phenotype Ontology ([HPO](https://hpo.jax.org/app/download/annotation))\n- Uber-anatomy ontology ([UBERON](https://www.ebi.ac.uk/ols/ontologies/uberon))\n\n## Dependencies\n\n- The code was developed and tested using Python 3.9.6\n\t\u003e  You need to use any version of Python \u003e 3.9 and \u003c 3.10\n\n\u003cdiv align=\"left\"\u003e\n\u003cimg src=\"https://raw.githubusercontent.com/bio-ontology-research-group/mowl/main/docs/source/mowl_black_background_colors_2048x2048px.png\" alt=\"mOWL library\" align=\"right\" width=\"110\" height=\"110\"\u003e\n\n- 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 \nembedding-based methods.\n    \u003e  You need to have JAVA and JDK installed in your machine.\n\n\u003c/div\u003e\n\n\n## Get the data\n\n1. 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`.\n2. 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.\n\n## Use the tool\n\nYou can install the tool either from source or PyPi as follows:\n\n### Create a virtual environment\n```\npython3 -m venv embedpvp_env\nsource ./embedpvp_env/bin/activate\n```\n\n### :ballot_box_with_check: Install from source\n```\ngit clone https://github.com/bio-ontology-research-group/EmbedPVP.git\ncd EmbedPVP/\npython setup.py install \nmkdir output\nembedpvp [args]\n```\n\n### :ballot_box_with_check: Install from PyPi\n```\npip install embedpvp\nmkdir output\nembedpvp [args]\n```\n- Run the command `embedpvp --help` to display help and parameters:\n```\nInitializing the package\nUsage: embedpvp [OPTIONS]\n\nOptions:\n  -d, --data-root TEXT      Data root folder  [required]\n  -i, --in_file TEXT        Annotated Input file  [required]\n  -p, --pathogenicity TEXT  Path to the pathogenicity prediction file (CADD)\n                            [required]\n  -hpo, --hpo TEXT          List of phenotype codes separated by commas\n                            [required]\n  -m, --model_type TEXT     Ontology model, one of the following (go , mp ,\n                            hp, uberon, union)\n  -e, --embedding TEXT      Preferred embedding model (e.g. TransD, TransE,\n                            TranR, ConvE ,DistMult, DL2vec, OWL2vc)\n                            [required]\n  -dir, --outdir TEXT       Path to the output directory\n  -o, --outfile TEXT        Path to the results output file\n  --help                    Show this message and exit.\n```\n\n- **Example:**\n`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`\n\n\n### Output:\nThe script will output a ranking a score for the candidate caustive list of variants. \n\n\n## Reference\nFor 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):\n\n```bibtex\n@article{althagafi2023prioritizing,\n  title={Prioritizing genomic variants through neuro-symbolic, knowledge-enhanced learning},\n  author={Althagafi, Azza and Zhapa-Camacho, Fernando and Hoehndorf, Robert},\n  journal={bioRxiv},\n  pages={2023--11},\n  year={2023},\n  publisher={Cold Spring Harbor Laboratory}\n}\n```\n\n## Note\nFor any questions or comments please contact azza.althagafi@kaust.edu.sa\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbio-ontology-research-group%2Fembedpvp","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fbio-ontology-research-group%2Fembedpvp","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbio-ontology-research-group%2Fembedpvp/lists"}