Addressing Sparsity in Metabolomics Data Analysis

解决代谢组学数据分析中的稀疏性

• 批准号: 10396831
• 负责人: Debashis Ghosh
• 金额: $ 9.64万
• 依托单位: UNIVERSITY OF COLORADO DENVER
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2022-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10396831
• 关键词: Address; Adoption; Area; Award; Bibliometrics; Biological; Characteristics; Classification; Communities; Computer software; Data; Data Analyses; Data Set; Databases; Decision Making; Dependence; Diagnosis; Disease; Etiology; Feedback; Funding; Goals; Guidelines; Individual; Literature; Manuals; Mass Spectrum Analysis; Measurement; Metabolic Pathway; Methods; Multivariate Analysis; Natural Language Processing; Operating System; Parents; Pathway Analysis; Procedures; Process; Programming Languages; Recommendation; Reproducibility; Request for Applications; Research; Research Personnel; Sampling; Scanning; Science; Semantics; Software Engineering; Software Tools; Statistical Data Interpretation; Statistical Methods; Suggestion; Surveys; Testing; Text; United States National Institutes of Health; Update; analysis pipeline; analytical tool; base; bioinformatics tool; data analysis pipeline; data format; data reuse; design; drug development; expiration; file format; improved; innovation; interoperability; member; metabolome; metabolomics; novel; open source; open source tool; programs; small molecule; software development; syntax; text searching; tool; tool development; usability; user-friendly; working group

Comprehensive profiling of the small molecule repertoire in a sample is referred to as metabolomics and it is being used to address a variety of scientific questions in biomedical studies. Recent technological advances in mass spectrometry-based metabolomics have allowed for more comprehensive and sensitive measurements of metabolites. Despite the technological advances, the bottleneck for taking full advantage of metabolomics data is often the availability and usability of analysis tools. The goal of the parent award (U01CA235488) is to develop novel statistical methods and software for the research community to improve the utilization of metabolomics data, which will help maximize the potential of metabolomics to provide new discoveries in disease etiology, diagnosis, and drug development. Software tools specifically designed for metabolomics data, like those proposed in the parent U01 award and attendant RFA (NIH RFA-RM-17-012), are being developed at an increasing rate. Many of these tools are open-source and freely available, but they are very diverse with respect to programming language, data formats, and stage in the metabolomics pipeline. Several of the challenges recognized in the NIH Common Fund Metabolomics Program are to “meet increasing demand for user-friendly, open-source, bioinformatics tools for data analysis and interpretation” and “coordinate community-wide identification and adoption of best practices for rigor, reproducibility and data reuse.” To mitigate these challenges and further the consortium’s goals, we have built the MSCAT database (https://mscat.metabolomicsworkbench.org) of metabolomics software tools that can be sustainably and continually updated (U01CA235488-02S1). The database provides a survey of the landscape of available tools and can assist researchers in the selection of data analysis workflows according to their specific needs. This supplement proposal aims to extend this database project by further mining the literature to characterize tool interoperability as outlined by their use in metabolomics studies and by analyzing the collected data about software tools to extract factors contributing to tool adoption, usability, and utility. In Aim 1, we will develop a text-mining process where the full text and co-citations of metabolomics studies are mined to identify which combinations of tools were used in past studies to validate the set of tools suggested by our database. In Aim 2, we assess the metabolomics software landscape for tool redundancy (based on functionality) and correlate software characteristics with tool adoption and interoperability.

样品中小分子谱的综合分析称为代谢组学，它是 用于解决生物医学研究中的各种科学问题。 基于质谱的代谢组学可以实现更全面、更灵敏的测量 尽管技术取得了进步，但充分利用代谢组学仍存在瓶颈。 数据往往是分析工具的可用性和可用性。母奖（U01CA235488）的目标是 为研究界开发新颖的统计方法和软件，以提高数据的利用率 代谢组学数据，这将有助于最大限度地发挥代谢组学的潜力，为以下领域提供新发现 专为代谢组学设计的疾病病因学、诊断和药物开发软件工具。 数据，如母 U01 奖项和随之而来的 RFA (NIH RFA-RM-17-012) 中提出的数据，正在 这些工具的开发速度越来越快，其中许多工具都是开源的并且可以免费使用，但它们非常有用。 在编程语言、数据格式和代谢组学流程中的阶段方面存在差异。 NIH 共同基金代谢组学计划承认的挑战之一是“满足日益增长的需求” 对用于数据分析和解释的用户友好型开源生物信息学工具的需求”以及 “协调社区范围内的最佳实践的识别和采用，以保证严谨性、可重复性和数据 为了缓解这些挑战并进一步实现联盟的目标，我们构建了 MSCAT 数据库 (https://mscat.metabolomicsworkbench.org) 可持续且可持续的代谢组学软件工具 不断更新（U01CA235488-02S1）该数据库提供了可用工具的概况调查。 并可以帮助研究人员根据自己的具体需求选择数据分析工作流程。 补充提案旨在通过进一步挖掘文献来描述工具来扩展该数据库项目 通过它们在代谢组学研究中的使用以及通过分析收集到的数据概述了互操作性 软件工具来提取有助于工具采用、可用性和实用性的因素。在目标 1 中，我们将开发一个工具。 文本挖掘过程，其中挖掘代谢组学研究的全文和共引，以确定哪些内容 过去的研究中使用了工具组合来验证我们的数据库建议的工具集。 2，我们评估代谢组学软件环境的工具冗余（基于功能）并关联 具有工具采用和互操作性的软件特征。