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OpenCRAVAT: Informatics Tools for High-Throughput Analysis of Cancer Mutations

OpenCRAVAT：用于癌症突变高通量分析的信息学工具

基本信息

批准号：
10617371
负责人：
Rachel Karchin
金额：
$ 65.31万
依托单位：
JOHNS HOPKINS UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-05-15 至 2027-04-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10617371
关键词：
Address Adopted Adoption Architecture Bioinformatics Cancer Patient Characteristics Clinical Cloud Computing Cloud Service Collaborations Collection Communities Complex Computer Analysis Computer software Custom DNA Sequence DNA Sequence Alteration DNA sequencing Data Data Commons Data Set Databases Disease Educational workshop Ensure Environment Epitopes Evaluation Feedback Frequencies Friends Gene Frequency Genes Genomics Growth Haplotypes Human Individual Investments Language Literature Malignant Neoplasms Measures Molecular Mutation Occupations Online Systems Pathogenicity Population Prognosis Property Recurrence Research Personnel Resources Running Sampling Selection for Treatments Services Side Software Tools Source Source Code Subgroup System Techniques Time Training Tumor Tissue Untranslated RNA Update Variant Visualization Visualization software Work anticancer research base cancer genomics cancer subtypes cloud platform cohort computerized tools data privacy design driver mutation flexibility genetic variant genomics cloud graphical user interface hackathon high throughput analysis improved in silico informatics tool interoperability meetings open source outreach parallel processing patient subsets protein structure recruit skills social media success symposium task analysis tool tumor tumorigenesis usability web server web-based tool

项目摘要

PROJECT SUMMARY Cancer sequencing projects have identified a very large number of DNA mutations whose importance in cancer is not yet understood. To better understand the impact of these mutations, our team has produced a software tool for computational analysis of cancer mutations that can analyze millions of mutations at one time. This tool works as a funnel to help researchers to find the small number of mutations that are most likely to be informative from the very large number of mutations discovered in a sequencing project. The software allows users to design ways to combine multiple mutation evaluation metrics, and generate a prioritized list of mutations that are more likely to be biologically important. These evaluation metrics include the molecular consequence, bioinformatic scores to identify pathogenic and driver mutations, frequency of the mutation in human populations, previous occurrence in tumor tissue types, pointers to literature, and visualization of annotated protein structures and networks. A web-based version of the pipeline - Cancer Related Analysis of Variants Toolkit (CRAVAT) has been widely adopted (3000+ jobs submitted/month on average in 2020). We have attracted a user community that spans both basic and clinical cancer researchers, all of whom rely on high-throughput tumor sequencing in their work. In 2019, we introduced OpenCRAVAT, which is distinguished by an open source codebase and an open app store of tools and resources that can be used to better understand the importance and impact of mutations. The app store is driven by the user community; new apps are prioritized based upon user requests and the app store includes many apps that were contributed directly by outside tool developers. The app store currently aggregates tools from over 70 organizations, and these tools can be combined to identify mutations whose molecular impact contributes to tumorigenesis, prognosis and treatment selection. Initial adoption of our OpenCRAVAT tool is encouraging, with over 10,000 local package downloads in the first two years. We expect that OpenCRAVAT will be adopted by a much larger community, given the increasing importance of DNA sequencing data in cancer research. We will continue to ensure that our tools are interoperable with other informatics tools and services, and can be run in different computational environments such as cloud computing and local installation to maintain data privacy.

项目总结