{"title":"AnnoMate：通过定制的交互式可视化方式探索和注释集成的分子数据","authors":"Claudia Chu, Conor Messer, Samantha Van Seters, Mendy Miller, Kristy Schlueter-Kuck, Gad Getz","doi":"10.1016/j.patter.2024.101060","DOIUrl":null,"url":null,"abstract":"<p>Manual review is an integral part of any study. As the cost of data generation continues to decrease, the rapid rise in large-scale multi-omic studies calls for a modular, flexible framework to perform what is currently a tedious, error-prone process. We developed <em>AnnoMate</em>, a Python-based package built with Plotly Dash that creates interactive, highly customizable dashboards for reviewing and annotating data. Its object-oriented framework enables easy development and modification of custom dashboards for specific manual review tasks. We utilized this framework to implement “reviewer” dashboards for various tasks often performed in cancer genome sequencing studies.</p>","PeriodicalId":36242,"journal":{"name":"Patterns","volume":null,"pages":null},"PeriodicalIF":6.7000,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"AnnoMate: Exploring and annotating integrated molecular data through custom interactive visualizations\",\"authors\":\"Claudia Chu, Conor Messer, Samantha Van Seters, Mendy Miller, Kristy Schlueter-Kuck, Gad Getz\",\"doi\":\"10.1016/j.patter.2024.101060\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Manual review is an integral part of any study. As the cost of data generation continues to decrease, the rapid rise in large-scale multi-omic studies calls for a modular, flexible framework to perform what is currently a tedious, error-prone process. We developed <em>AnnoMate</em>, a Python-based package built with Plotly Dash that creates interactive, highly customizable dashboards for reviewing and annotating data. Its object-oriented framework enables easy development and modification of custom dashboards for specific manual review tasks. We utilized this framework to implement “reviewer” dashboards for various tasks often performed in cancer genome sequencing studies.</p>\",\"PeriodicalId\":36242,\"journal\":{\"name\":\"Patterns\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":6.7000,\"publicationDate\":\"2024-09-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Patterns\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1016/j.patter.2024.101060\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Patterns","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1016/j.patter.2024.101060","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
AnnoMate: Exploring and annotating integrated molecular data through custom interactive visualizations
Manual review is an integral part of any study. As the cost of data generation continues to decrease, the rapid rise in large-scale multi-omic studies calls for a modular, flexible framework to perform what is currently a tedious, error-prone process. We developed AnnoMate, a Python-based package built with Plotly Dash that creates interactive, highly customizable dashboards for reviewing and annotating data. Its object-oriented framework enables easy development and modification of custom dashboards for specific manual review tasks. We utilized this framework to implement “reviewer” dashboards for various tasks often performed in cancer genome sequencing studies.
京公网安备 11010802042870号
京ICP备2023020795号-1
分享
求助内容：
应助结果提醒方式：
扫码关注我们
