Integrating metagenomics data into accurate mass stool metabolite identifications

项目摘要 教授奥利弗菲恩将与他的关键人员，统计学家克里斯托弗Brydges博士，生物信息学专家 博士Yuanyue Li和程序员Gert Wohlgemuth（都是加州大学戴维斯分校的人）创建了新的管道， 微生物宏基因组学数据和粪便质谱数据，以更好地将代谢物与疾病相关联 炎症性肠病的进展。 我们将与Clary Clish博士（布罗德研究所）协商，他生成数据并将其保存到 NIH共同基金代谢组学和iHMP整合人类微生物组数据。我们将 优先考虑使用纵向疾病的80，000多个尚未识别的粪便代谢信号的巨大集合 与健康受试者相比，炎症性肠病受试者在1年内的进展。为此 有限的不超过1,000种代谢物，将显示出与健康结果的显着关联，我们将 使用所有可用的准确质量MS/MS数据和所有粪便微生物组数据获得代谢物类别信息 以及可能的代谢物结构或亚结构。克利什博士将审查我们的结果，并分享新的注释， 他的组织就会释放 为此，我们将开发由KBase协作建立的代谢组预测工具 过去10年的研究联盟。KBase使用微生物基因组序列（甚至转录组学 数据），以通过酶预测和间隙填充自动构建代谢途径。KBase也 使微生物群落的利用，建模的进口和出口的代谢物，其他微生物 可以作为碳源。咨询KBase的Chris亨利博士（阿贡国家实验室） 然后，我们将在KBase环境中构建管道，以包括质谱分析工具， Fiehn实验室通过其过去的NIH资助，特别是公式预测和子结构， 预测（在MS-FINDER中）、保留时间预测（在Retip.app中）、混合移位MS/MS相似性匹配（在 NIST搜索）和熵相似性MS/MS匹配（在MassBank.us中）。 这一特定项目将对其他类似的微生物组/代谢组项目产生重大影响， 上传到NIH共同基金数据库。该项目解决了粪便中的巨大复杂性， 宏基因组学和粪便代谢组学数据，并提供关键管道（在KBase中称为“叙述”）， 供广大研究界使用。

Project Summary Prof. Oliver Fiehn will work with his key persons, statistician Dr. Christopher Brydges, bioinformatics specialist Dr. Yuanyue Li and programmer Gert Wohlgemuth (all UC Davis) to generate new pipelines that integrate stool microbial metagenomics data and stool mass spectrometry data to better associate metabolites with disease progression in inflammatory bowel disease. We will work in consultation with Dr. Clary Clish (Broad Institute) who generated and deposited the data to the NIH Common Funds MetabolomicsWorkbench and the iHMP integrated human microbiome data. We will prioritize the enormous set of more than 80,000 yet unidentified stool metabolic signals using longitudinal disease progression over 1 year in subjects with inflammatory bowel disease, in comparison to healthy subjects. For this limited set of not more than 1,000 metabolites that will show significant association with health outcomes, we will use all available accurate mass MS/MS data and all stool microbiome data to obtain metabolite class information and likely metabolite structures or substructures. Dr. Clish will review our results and share new annotations that his group will release. To this end, we will develop the tools for metabolome predictions that have been built by the KBase collaborative research consortium over the past 10 years. KBase uses microbial genomic sequences (or even transcriptomics data) to automatically build metabolic pathways through enzyme predictions and gap filling. KBase also empowers utilization of microbial communities, modeling import and export of metabolites that other microbes can use as carbon sources. In consultation with Dr. Chris Henry (Argonne National Lab) from the KBase consortium, we will then build pipelines within the KBase environment to include mass spectrometry tools that the Fiehn laboratory has built through its past NIH funding, specifically formula predictions and substructure predictions (in MS-FINDER), retention time predictions (in Retip.app), hybrid-shift MS/MS similarity matching (in NIST search), and entropy similarity MS/MS matching (in MassBank.us). This specific project will have large impact on other, similar microbiome/metabolome projects that will be uploaded to the NIH Common Funds databases in the future. The project addresses the huge complexity in stool metagenomics and stool metabolomics data, and delivers key pipelines (called ‘narratives’ in KBase) that can be used by the research community at large.