{"id":19714792,"url":"https://github.com/baranzinilab/vane","last_synced_at":"2025-07-16T21:39:26.870Z","repository":{"id":162210642,"uuid":"618547337","full_name":"BaranziniLab/VANE","owner":"BaranziniLab","description":"Variant Annotation with Network-enhanced Epigenetic data","archived":false,"fork":false,"pushed_at":"2023-05-05T18:29:44.000Z","size":286,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":3,"default_branch":"main","last_synced_at":"2025-02-27T21:49:16.806Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/BaranziniLab.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2023-03-24T17:52:57.000Z","updated_at":"2023-03-28T23:00:48.000Z","dependencies_parsed_at":null,"dependency_job_id":"dd2165a1-516e-44a2-85cb-42523ea55aa5","html_url":"https://github.com/BaranziniLab/VANE","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/BaranziniLab/VANE","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/BaranziniLab%2FVANE","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/BaranziniLab%2FVANE/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/BaranziniLab%2FVANE/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/BaranziniLab%2FVANE/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/BaranziniLab","download_url":"https://codeload.github.com/BaranziniLab/VANE/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/BaranziniLab%2FVANE/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":265542439,"owners_count":23785226,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","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":[],"created_at":"2024-11-11T22:35:55.685Z","updated_at":"2025-07-16T21:39:26.824Z","avatar_url":"https://github.com/BaranziniLab.png","language":"Jupyter Notebook","readme":"# VANE\n![Python](https://img.shields.io/badge/python-3670A0?style=for-the-badge\u0026logo=python\u0026logoColor=ffdd54)\n\n![VANE-2](https://user-images.githubusercontent.com/75185329/227608114-a2cb2ba8-6245-4a26-a64e-7866ed66fa2e.png)\n\n## Welcome to VANE!\n\nVANE, is a python package that process cellular (and tissue) level epigenomic data, and creates celullar-specific protein-protein interaction networks to model omnigenics pathways of disease. \n\nVANE is able to process either a list of Tagging SNPs, or a list of fine-mapped variants. \n\n# Installation\n\n### 1. Install dependencies either using PIP or Conda.\n\n#### Installation using PIP:\n\n```\n$ git clone https://github.com/BaranziniLab/VANE.git\n$ cd VANE/envs/\n$ pip install -r requirements_pip.txt\n```\n\n#### Installation using Conda: \n\n```\n$ git clone https://github.com/BaranziniLab/VANE.git\n$ cd VANE/envs/\n$ conda env create -f environment-vane.yml\n$ conda activate vane\n```\n\n### 2. Download base files\n\nVANE needs multiple cleaned and processed files to work. You can download the base file folder from: (link)\n\n#### Linux:\n\n```\n$ cd VANE\n$ wget https://ucsf.box.com/shared/static/b5hbzpl84v73lz0d2tt72gsi2vvftpz3.zip\n$ unzip b5hbzpl84v73lz0d2tt72gsi2vvftpz3.zip\n```\n#### Mac:\n\n```\n$ cd VANE\n$ curl https://ucsf.box.com/shared/static/b5hbzpl84v73lz0d2tt72gsi2vvftpz3.zip\n$ unzip b5hbzpl84v73lz0d2tt72gsi2vvftpz3.zip\n```\n\n\n\u003e **A:** Your VANE folder and files, should look exactly like *this*.\n\n    VANE\n    ├── ...\n    ├── base_files                   \n    │   ├── cell_networks          # Folders with all the cell networks\n    │   ├── clean_snp2gene.db         # SNP2 Gene SQL db from OpenTargets\n    │   └── gene_protein_ensembl_map_table.tsv                # Gene-protein-ENSG_id mapping tables\n    ├── VANE.py\n    ├── snp_parser.py\n    ├── regulome_db_parsing.py\n    └── ld_parser.py\n\n# Quick Start\n\n### Complete example of creating cellular-level network for a subset of autoimmune-related tagging variants\n\n#### Epigenomic Analysis\n```python\nfrom VANE import VANE\n\nms_variants = ['rs35486093','rs11256593', 'rs112344141', 'rs1323292', 'rs72928038', 'rs78727559', 'rs1800693', 'rs10801908', 'rs6670198',\n'rs62420820', 'rs1738074', 'rs4939490', 'rs9843355', 'rs11809700', 'rs35540610', 'rs1026916', 'rs1014486', 'rs6589706', 'rs11079784',\n'rs11749040', 'rs631204', 'rs4808760', 'rs12478539', 'rs7977720', 'rs3809627', 'rs12365699', 'rs6032662', 'rs60600003', 'rs2546890', 'rs1465697']\n\nMS_variant = VANE(list_of_variants = ms_variants)\ndf_with_positions = MS_variant.process_tag_variants(ld=0.7, population = \"1000GENOMES:phase_3:CEU\", verbose=False)\n\nsnp_2_gene = MS_variant.query_snp_opentargets(df_with_positions, \"base_files/clean_snp2gene.db\")\ngene_to_rsid = MS_variant.merge_and_clean(snp_2_gene, df_with_positions)\nMS_variant.regulomedb_scoring(save=True, verbose=True)\n\n\ncell_list = ['B', 'T', 'mono', 'astrocyte', 'neutrophil', 'keratinocyte', 'melanocyte']\norgans_list = ['brain','spleen','thymus', 'lymphoid tissue', 'lymph node', 'skin']\n\nMS_variant.process_cells(cell_list)\nMS_variant.plot_cells()\n\nMS_variant.process_organs(organs_list)\nMS_variant.plot_organs()\n\n```\n#### Network Analysis\n\nCalculate P value and CI of the cellular-network of the epigenomic-relevant target genes:\n\n```python\nfrom VANE import VANE_network\n\nT_cell_network = VANE_network(network_folder = 'base_files/cell_networks/', df_for_network=MS_variant.df_for_network)\nT_cell_network.create_cellular_network(cell = 'T', epigenome_threshold = 0)\nCI, pval = T_cell_network.calculate_p_value()\nT_cell_network.plot_bootstrap()\n\n```\n\n\n\n\n","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbaranzinilab%2Fvane","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fbaranzinilab%2Fvane","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbaranzinilab%2Fvane/lists"}