U2C Computational Core

U2C计算核心

• 批准号: 9981748
• 负责人: Shuzhao Li
• 金额: $ 15.21万
• 依托单位: EMORY UNIVERSITY
• 依托单位国家: 美国
• 资助国家: 美国
• 起止时间: 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/9981748
• 关键词: Automation; Biological Availability; Chemicals; Computer software; Data; Data Science Core; Data Set; Database Management Systems; Databases; Detection; Development; Dissociation; Environmental Health; Feedback; Funding; Goals; Human; Informatics; Ions; Leadership; Machine Learning; Maps; Mass Spectrum Analysis; Metabolic Biotransformation; Metabolic Pathway; National Institute of Environmental Health Sciences; Parents; Pattern; Population; Process; Resolution; Resources; Sampling; Structure; Systems Biology; Time; Universities; Visual; Work; Xenobiotic Metabolism; Xenobiotics; adduct; base; biological systems; cloud based; computational pipelines; computerized data processing; computerized tools; cost effective; data warehouse; design; improved; in silico; instrument; metabolomics; multiple omics; open source; platform-independent; prediction algorithm; programs; robotic system; support tools; synergism; tool; ultra high resolution

Computational Core –Project Summary The Computational Core will develop tools supporting the Experimental Core and creation of an informatic compound identification resource that greatly expands available biological and analytical characterization of xenobiotic compounds. This includes the development of computational tools that maximize information capture from the Experimental Core and uses this information to develop informatic compound identification resources for high-resolution mass spectrometry (HRMS) platforms. The Core is structured to deliver key analytics for mega-scale mass spectral data processing and improved workflows for chemical identification using high-throughput arrays. The team has extensive expertise in systems biology, computational metabolomics, multiomic integration, database management and HRMS spectral processing, and will leverage informatic and machine learning expertise in the NIEHS-funded HERCULES Environmental Health Data Sciences Core (EHDSC) at Emory University. The Computational Core will provide sustained impact for the Metabolomic Consortium through development of an open-source, platform independent software pipeline and cloud-based xenobiotic databases. Throughout the pipeline creation and implementation process we will work closely with the Metabolomics Consortium Stakeholder Engagement and Program Coordination Center (SEPCC) to provide consistent identification metrics and annotation best practices, in addition to eliciting feedback from the National Metabolomics Data Repository for maximizing synergy metabolomic datasets. Because of the unique needs of this project to develop improved algorithms for prediction of in silico biotransformation products and ion dissociation patterns and processing tools for the large amount of metabolite data generated by the Experimental Core, we have identified key milestones and deliverables to meet ECIDC objectives. These will be accomplished through aims designed to process MS/MS spectra for thousands to hundreds of thousands of metabolites generated by the Experimental Core in a time and cost- effective manner by developing a semi-automated workflow that combines visual scripting, computational prediction of enzymatic biotransformation products and MS/MS spectral deconvolution that utilizes correlation across samples to isolate high-purity dissociation patterns. We will build upon the mega-biotransformation- identification pipeline to 1) calibrate and enhance in silico prediction of biotransformation products using parent compounds, 2) calibrate and enhance in silico prediction MS/MS dissociation patterns, 3) LC retention time and adduct prediction tools for reducing false matches, 4) a combined cloud-based database containing experimental and predicted MS/MS spectral patterns for xenobiotics and metabolites, and 5) exposome-based metabolic pathway maps to rapidly assess xenobiotic exposure enrichment in human populations using untargeted, HRMS profiling data. These tools will be scalable to different instruments and number of samples to support the goal to provide mega-scale identification of xenobiotic metabolites.

计算核心 - 项目摘要 计算核心将开发支持实验核心和创建信息丰富的工具 复合识别资源大大扩展了可用的生物学和分析表征 异生物化合物。这包括开发最大化信息的计算工具 从实验核心捕获并使用此信息来开发信息丰富的复合识别 高分辨率质谱（HRMS）平台的资源。核心的结构是提供密钥 大型质谱数据处理和改进的化学识别工作流程的分析 使用高通量阵列。该团队在系统生物学，计算方面拥有广泛的专业知识 代谢组学，多组分集成，数据库管理和HRMS光谱处理，并将利用 NIEHS资助的大力神环境健康数据的信息和机器学习专业知识 埃默里大学的科学核心（EHDSC）。计算核心将为 通过开发开源，平台独立软件管道和 基于云的异种生物数据库。通过管道创建和实施过程，我们将工作 与代谢组学财团的互动和计划协调中心紧密 （SEPCC）除了引起 来自国家代谢组学数据存储库的反馈，以最大化协同代谢组合数据集。 由于该项目的独特需求，需要开发改进的算法以预测计算机 生物转化产品和离子解离模式和加工工具 实验核心生成的代谢物数据，我们已经确定了关键里程碑和可交付成果 达到ECIDC目标。这些将通过旨在处理MS/MS光谱的目标来完成 实验核心在一段时间内产生的成千上万到成千上万的代谢产物和成本 - 通过开发结合视觉脚本，计算的半自动化工作流程来有效的方式 使用相关性的酶生物转化产物和MS/MS光谱反卷积的预测 跨样品分离出高纯度解离模式。我们将建立在大型建筑的基础上 - 识别管道至1）使用父级校准和增强生物转化产物的硅化预测 化合物，2）校准和增强硅预测MS/MS解离模式，3）LC保留时间 和用于减少错误匹配的加合物预测工具，4）包含基于云的组合数据库 用于异种生物和代谢物的实验和预测的MS/MS光谱模式，以及5）基于剥离体的 代谢途径图以快速评估人类种群中的异种暴露富集 未靶向的HRMS分析数据。这些工具将可扩展到不同的仪器和样品数量 支持提供异种生物代谢物的大规模鉴定的目标。