{"id":38676312,"url":"https://github.com/wglab/rankvar","last_synced_at":"2026-01-17T10:01:06.985Z","repository":{"id":279683021,"uuid":"939049916","full_name":"WGLab/RankVar","owner":"WGLab","description":null,"archived":false,"fork":false,"pushed_at":"2025-08-26T17:40:03.000Z","size":56679,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-08-27T00:15:27.784Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/WGLab.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"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,"zenodo":null}},"created_at":"2025-02-25T23:01:11.000Z","updated_at":"2025-08-26T17:40:07.000Z","dependencies_parsed_at":"2025-08-26T19:37:11.292Z","dependency_job_id":null,"html_url":"https://github.com/WGLab/RankVar","commit_stats":null,"previous_names":["wglab/rankvar"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/WGLab/RankVar","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/WGLab%2FRankVar","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/WGLab%2FRankVar/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/WGLab%2FRankVar/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/WGLab%2FRankVar/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/WGLab","download_url":"https://codeload.github.com/WGLab/RankVar/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/WGLab%2FRankVar/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":28505570,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-01-17T06:57:29.758Z","status":"ssl_error","status_checked_at":"2026-01-17T06:56:03.931Z","response_time":85,"last_error":"SSL_read: 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":[],"created_at":"2026-01-17T10:00:43.995Z","updated_at":"2026-01-17T10:01:06.954Z","avatar_url":"https://github.com/WGLab.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# RankVar: Machine Learning-Based Variant Ranking and Reinterpretation for Rare Genetic Diseases\nRankVar is an AI-driven pipeline that integrates phenotype data and sequencing profiles to prioritize disease-causing genes and variants.\n\n# Installation\n\nWe recommend using Conda to set up the environment. If Conda is not installed, run the following commands in Linux to install it.\n\n```bash\ncurl -O https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh\nbash Miniconda3-latest-Linux-x86_64.sh\n```\nAfter conda is installed successfully, RankVar sources can be downloaded:\n\n```bash\ngit clone https://github.com/WGLab/RankVar.git\ncd RankVar\nconda create -n rankvar python=3.10\nconda activate rankvar\npip install numpy==1.25 pandas joblib scikit-learn==1.3 torch\npython RankVar.py --help\n```\n\n# Inference\n### Step 1: Install and run ANNOVAR\nANNOVAR is an efficient software tool to utilize update-to-date information to functionally annotate genetic variants detected from diverse genomes.\n\n#### a) Install ANNOVAR\n\nTypically you will go to the [ANNOVAR website](https://annovar.openbioinformatics.org/en/latest/), fill in a registration form, and download the package there. When you have requested the ANNOVAR from the website and downloaded it, you will receive a compressed file ```annovar.latest.tar.gz```, you will need to unzip it. Then follow the user guide to install ANNOVAR. \n\n#### b) Run ANNOVAR\n\nInput files to ANNOVAR refer to VCF file (example.vcf)\n\n```bash\nperl table_annovar.pl example.vcf humandb/ -buildver hg38 -out myanno -remove -protocol refGene,cytoBand,exac03,avsnp147,dbnsfp47a,gnomad41_exome,gnomad41_genome,clinvar_20240917,GTEx_v8_eQTL,GTEx_v8_sQTL -operation gx,r,f,f,f,f,f,f,f,f -nastring . -vcfinput -polish\n```\nAfter that, you will find the result files ```myanno.hg38_multianno.txt``` and ```myanno.hg38_multianno.vcf```.\n\n### Step 2: Install and run Phen2Gene\nPhen2Gene is a phenotype-driven gene prioritization tool, that takes HPO (Human Phenotype Ontology) IDs as inputs, searches and prioritizes candidate causal disease genes.\n\n#### a) Install Phen2gene\nPlease follow [Phen2gene](https://github.com/WGLab/Phen2Gene) repository for instructions on how to install Phen2gene.\n\n#### b) Run Phen2Gene\n\nInput files to Phen2Gene should contain HPO IDs, separated by UNIX-recognized new line characters (i.e., \\n). Alternatively you can use a space separated list of HPO IDs on the command line.\n\nHere is an example file called ```hpo_list.txt```\n```bash\nHP:0000358\nHP:0000039\nHP:0008438\nHP:0000891\nHP:0000252\n```\nsimply run:\n```bash\npython3 phen2gene.py -f hpo_list.txt -out phen2gene_out\n```\nAfter that, you will find the result files ```phen2gene_out/output_file.associated_gene_list```\n\n### Step 3: Run RanVar\n\nInput files to RankVar are annotated VCF file (```myanno.hg38_multianno.txt```) and HPO terms (```hpo_list.txt```) and related Phen2gene score file (```phen2gene_out/output_file.associated_gene_list```)\n\nType ```python RankVar/RankVar.py -help``` to see all options.\n\n```bash\nusage: RankVar.py [-h] --annovar ANNOVAR --output OUTPUT --hpo_ids HPO_IDS --phen2gene PHEN2GENE [--gq GQ] [--ad AD]\n                  [--gnomad GNOMAD]\n\nParse arguments for genetic analysis software.\n\noptions:\n  -h, --help            show this help message and exit\n  --annovar ANNOVAR     Path to the annotated VCF file (default: None)\n  --output OUTPUT       Path to the output folder (default: None)\n  --hpo_ids HPO_IDS     Path to the HPO id file (default: None)\n  --phen2gene PHEN2GENE\n                        Path to the Phen2Gene score file (default: None)\n  --gq GQ               Genotype Quality threshold (float) (default: 20)\n  --ad AD               Allelic Depth threshold (float) (default: 15)\n  --gnomad GNOMAD       GnomAD frequency threshold (float) (default: 0.0001)\n```\n\n#### Example\n\nDownload the example VCF file:\n\n```bash\nwget https://pmc.ncbi.nlm.nih.gov/articles/instance/5111005/bin/supp_mcs.a001131_Supp_File_2_KBG_family_Utah_VCF_files.zip\nunzip supp_mcs.a001131_Supp_File_2_KBG_family_Utah_VCF_files.zip\n```\n\nAfter that, you will find the ```proband.vcf```\n\nIf the input VCF is in hg19, you need to convert it to hg38 using GATK:\n\n```bash\ngatk --java-options \"-Xmx16g\" LiftoverVcf -I proband.vcf -O proband.hg38.vcf -CHAIN hg19ToHg38.over.chain.gz -REJECT unmapped_variants.vcf -R Homo_sapiens_assembly38.fasta\n```\nAfter that, you will find the result file ```proband.hg38.vcf```\n\nThen, run annovar on ```proband.hg38.vcf``` and Phen2gene on ```hpo_list.txt``` to generate the files ```myanno.proband.hg38_multianno.txt``` and ```phen2gene_out/output_file.associated_gene_list```\n\nrun RankVar:\n```bash\npython RankVar.py --annovar myanno.proband.hg38_multianno.txt --phen2gene phen2gene_out/output_file.associated_gene_list  --hpo_ids hpo_list.txt --output output/\n```\nRankVar will write output in `output/rank_var.tsv` that will look like:\n```\nChr    Start      End        Ref  Alt  Func.refGene  Gene.refGene  ExonicFunc.refGene    gnomad41_exome_AF_grpmax  phen2gene_score  pathogenecity_score  rank\nchr16  89280526   89280526   -    T    exonic        ANKRD11       frameshift insertion  0.0                       1.0              1.0                  1.0\nchr19  53889950   53889950   -    G    exonic        PRKCG         frameshift insertion  0.0                       0.158115         0.72                 2.0\nchr2   202555679  202555679  -    A    exonic        BMPR2         frameshift insertion  0.0                       0.108457         0.63                 3.0\nchr19  13298600   13298600   G    -    exonic        CACNA1A       frameshift deletion   0.0                       0.085824         0.54                 4.0\nchr3   171644932  171644932  C    -    exonic        PLD1          frameshift deletion   0.0                       0.12026          0.49                 5.0\n\n...\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fwglab%2Frankvar","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fwglab%2Frankvar","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fwglab%2Frankvar/lists"}