{"id":26359487,"url":"https://github.com/karchinlab/2020plus","last_synced_at":"2025-03-16T15:59:54.138Z","repository":{"id":69669006,"uuid":"57407955","full_name":"KarchinLab/2020plus","owner":"KarchinLab","description":"Classifies genes as an oncogene, tumor suppressor gene, or as a non-driver gene by using Random Forests","archived":false,"fork":false,"pushed_at":"2024-08-03T23:39:13.000Z","size":31143,"stargazers_count":48,"open_issues_count":12,"forks_count":17,"subscribers_count":13,"default_branch":"master","last_synced_at":"2024-08-04T00:23:52.700Z","etag":null,"topics":["bioinformatics","cancer","driver-genes","random-forest","somatic-variants"],"latest_commit_sha":null,"homepage":"http://2020plus.readthedocs.org","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"apache-2.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/KarchinLab.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}},"created_at":"2016-04-29T19:26:49.000Z","updated_at":"2024-08-03T23:39:15.000Z","dependencies_parsed_at":null,"dependency_job_id":"69b3ff72-e316-45c6-ae81-3c79e4a412c7","html_url":"https://github.com/KarchinLab/2020plus","commit_stats":null,"previous_names":[],"tags_count":13,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/KarchinLab%2F2020plus","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/KarchinLab%2F2020plus/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/KarchinLab%2F2020plus/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/KarchinLab%2F2020plus/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/KarchinLab","download_url":"https://codeload.github.com/KarchinLab/2020plus/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":243893829,"owners_count":20364916,"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":["bioinformatics","cancer","driver-genes","random-forest","somatic-variants"],"created_at":"2025-03-16T15:59:54.062Z","updated_at":"2025-03-16T15:59:54.129Z","avatar_url":"https://github.com/KarchinLab.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# 20/20+\n\n## About\n\nNext-generation DNA sequencing of the exome has detected hundreds of thousands of small somatic variants (SSV) in cancer. However, distinguishing genes containing driving mutations rather than simply passenger SSVs from a cohort sequenced cancer samples requires sophisticated computational approaches.\n20/20+ integrates many features indicative of positive selection to predict oncogenes and tumor suppressor genes from small somatic variants. \nThe features capture mutational clustering, conservation, mutation *in silico* pathogenicity scores, mutation consequence types, protein interaction network connectivity, and other covariates (e.g. replication timing).\nContrary to methods based on mutation rate, 20/20+ uses ratiometric features of mutations by normalizing for the total number of mutations in a gene. This decouples the genes from gene-level differences in background mutation rate. 20/20+ uses monte carlo simulations to evaluate the significance of random forest scores based on an estimated p-value from an empirical null distribution.\n\n## Documentation\n\n[![Documentation Status](http://readthedocs.org/projects/2020plus/badge/?version=latest)](http://2020plus.readthedocs.io/en/latest/?badge=latest)\n\nPlease see the [documentation](http://2020plus.readthedocs.io/) on readthedocs.\n\n## Releases\n\nYou can download [releases](https://github.com/KarchinLab/2020plus/releases) on github.\n\n## Installation\n\n[![Build Status](https://travis-ci.org/KarchinLab/2020plus.svg?branch=master)](https://travis-ci.org/KarchinLab/2020plus)\n\n20/20+ is designed to run on *linux* operating systems.\n\nWe recommend that you install the dependencies for 20/20+ through [conda](https://conda.io/miniconda.html). Once conda is installed, setting up the environment is done as follows:\n\n```bash\n$ conda env create -f environment_python.yml  # install dependencies for python\n$ source activate 2020plus  # activate the 20/20+ conda environment\n$ conda install r r-randomForest rpy2  # install the R related dependencies\n```\n\nEvery time you wish to run 20/20+, you will then need to activate the \"2020plus\" conda environment.\n\n```bash\n$ source activate 2020plus\n```\n\nThe 20/20+ conda environment can also be deactivated.\n\n```bash\n$ source deactivate 2020plus\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fkarchinlab%2F2020plus","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fkarchinlab%2F2020plus","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fkarchinlab%2F2020plus/lists"}