U2C Computational Core

计算核心-项目摘要 计算核心将开发支持实验核心的工具， 化合物鉴定资源，大大扩展了现有的生物和分析表征， 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化合物识别 高分辨率质谱（HRMS）平台的资源。核心的结构是提供关键的 大规模质谱数据处理的分析和改进的化学鉴定工作流程 使用高通量阵列。该团队在系统生物学、计算生物学和生物学领域拥有广泛的专业知识。 代谢组学、多组学整合、数据库管理和HRMS光谱处理，并将利用 NIEHS资助的HERCULES环境健康数据的信息学和机器学习专业知识 科学核心（EHDSC）在埃默里大学。计算核心将为 代谢组学联盟通过开发开源、平台独立的软件管道， 基于云的异生物数据库。在整个管道创建和实施过程中，我们将努力 与代谢组学联盟利益相关者参与和计划协调中心密切合作 （SEPCC）提供一致的标识指标和注释最佳实践， 来自国家代谢组学数据库的反馈，用于最大化协同代谢组学数据集。 由于该项目的独特需求，开发用于预测计算机模拟的改进算法， 生物转化产物和离子解离模式和处理工具， 实验核心产生的代谢物数据，我们已经确定了关键的里程碑和可交付成果， 达到ECIDC的目标。这些将通过设计用于处理MS/MS光谱的目标来实现， 实验核心在一段时间内产生数千到数十万种代谢物， 通过开发一个半自动化的工作流程，结合可视化脚本，计算 预测酶促生物转化产物和利用相关性的MS/MS光谱解卷积 分离出高纯度的解离模式我们将建立在巨大的生物转化基础上- 识别管道：1）使用母体校准和增强生物转化产物的计算机预测 化合物，2）校准和增强计算机预测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）组合的基于云的数据库， 外源性物质和代谢物的实验和预测MS/MS光谱模式，以及5）基于代谢物组的 使用代谢途径图快速评估人群中的外源性暴露富集 非目标的HRMS分析数据。这些工具将可扩展到不同的仪器和样本数量 以支持大规模鉴定异生质代谢物的目标。

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.