U2C Computational Core

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学的工具 化合物鉴定资源，极大地扩展了可用的生物学和分析表征 异种生物化合物。这包括开发使信息最大化的计算工具 从实验核心中捕获并使用这些信息来开发信息化合物识别 高分辨率质谱仪(HRMS)平台的资源。核心的结构旨在提供密钥 用于大规模质谱学数据处理的分析和改进的化学鉴定工作流程 使用高吞吐量阵列。该团队在系统生物学、计算领域拥有广泛的专业知识 代谢组学、多组学集成、数据库管理和HRMS光谱处理，并将利用 NIEHS资助的Hercules环境健康数据中的信息学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 通过开发开放源码的、独立于平台的软件管道和 基于云的异种生物数据库。在整个管道创建和实施过程中，我们将努力 与新陈代谢联盟利益相关者参与和计划协调中心密切合作 (SEPCC)，以提供一致的识别指标和注释最佳做法，除了引出 来自国家代谢组学数据库的反馈，以最大限度地提高协同作用代谢数据集。 由于本项目的独特需要，需要开发改进的算法来预测硅酸盐指数 生物转化产物、离子解离规律及加工工具 由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交付成果，以 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以千计到数十万的代谢物- 通过开发结合了可视化脚本编写、计算 利用相关性预测酶生物转化产物和MS/MS光谱去卷积 以分离出高纯度的解离模式。我们将在巨型生物改造的基础上- 用于1)利用亲本校准和增强生物转化产物的电子预测的鉴定流水线 化合物，2)校正和增强计算机预测的MS/MS解离模式，3)LC保留时间 和添加预测工具以减少误匹配，4)基于云的组合数据库，包括 外源化合物和代谢物的实验和预测的MS/MS谱型，以及5)基于曝光组的 代谢途径图用于快速评估人类人群中外源生物暴露的浓缩 无针对性的人力资源管理系统概要数据。这些工具将可扩展到不同的仪器和样本数量 支持大规模鉴定异种代谢物的目标。

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.