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：
10418133
负责人：
Rachel Karchin
金额：
$ 67.97万
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/10418133
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 Gene Frequency Genes Genomics Growth Haplotypes Human Individual Internet 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 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

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 Gene Frequency Genes Genomics Growth Haplotypes Human Individual Internet 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 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

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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.

项目摘要癌症测序项目已经确定了大量的DNA突变，其在癌症中的重要性尚未理解。为了更好地了解这些突变的影响，我们的团队生产了一个软件用于一次计算分析癌症突变的工具，该工具一次可以分析数百万突变。这个工具作为渠道，可帮助研究人员找到最有可能提供信息的少量突变从测序项目中发现的大量突变中。该软件允许用户设计结合多个突变评估指标的方法，并生成优先的突变列表可能在生物学上很重要。这些评估指标包括分子后果，生物信息学得分以识别病原体和驱动器突变，人群中突变的频率，以前在肿瘤组织类型，文学指针以及注释的蛋白质结构和可视化中发生网络。基于Web的管道版本 - 变体工具包（CRAVAT）的癌症相关分析已是广泛采用（2020年平均每月提交3000多个工作）。我们吸引了一个用户社区跨越碱性和临床癌症研究人员，所有这些研究人员都依赖于高通量的肿瘤测序工作。在2019年，我们介绍了OpenCravat，该OpenCravat以开源代码库和开放式的方式区分应用程序存储的工具和资源可以用来更好地理解突变的重要性和影响。 App Store由用户社区驱动；根据用户请求和应用程序优先考虑新应用商店包含许多直接由外部工具开发人员贡献的应用程序。当前的应用商店来自70多个组织的汇总工具，可以将这些工具组合起来，以识别突变分子影响有助于肿瘤发生，预后和治疗选择。最初采用我们的 OpenCravat工具令人鼓舞，在头两年中，有10,000多个本地套餐下载。我们期望鉴于DNA的重要性越来越高癌症研究中的数据测序。我们将继续确保我们的工具与其他工具可互操作信息学工具和服务，可以在不同的计算环境中运行，例如云计算和本地安装以维护数据隐私。