Integrating metagenomics data into accurate mass stool metabolite identifications

项目摘要 Oliver Fiehn教授将与他的关键人员、统计学家Christopher Brydges博士、生物信息学专家 李元月博士和程序员Gert Wohlgeuth(均为加州大学戴维斯分校)生成整合粪便的新管道 微生物元基因组学数据和粪便质谱学数据，更好地将代谢物与疾病联系起来 炎症性肠病的进展。 我们将与Clary Clish博士(博德研究所)协商，他生成了数据并将其存储到 NIH共同基金代谢学工作台和iHMP整合了人类微生物组数据。我们会 使用纵向疾病对8万多个尚未识别的大便代谢信号进行优先排序 与健康受试者相比，炎症性肠病受试者的进展超过1年。为了这个 有限的一套不超过1,000种与健康结果显著相关的代谢物，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来获取代谢物类别信息 以及可能的代谢物结构或亚结构。Clish博士将审查我们的结果并分享新的注释 他的团队将被释放。 为此，我们将开发用于代谢组预测的工具，这些工具已经由KBase Collaborative构建 过去10年的研究联盟。Kbase使用微生物基因组序列(甚至是转录组 数据)，以通过酶预测和缺口填充自动建立代谢途径。KBase还 支持微生物群落的利用，模拟其他微生物的代谢物的进出口 可以用作碳源。与KBase的克里斯·亨利博士(阿贡国家实验室)协商 财团，然后我们将在KBase环境中建立管道，以包括 费恩实验室通过其过去的NIH资金，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MS/MS匹配(在MassBank.us中)。 这一具体项目将对其他类似的微生物组/代谢组项目产生重大影响，这些项目将 在未来上传到NIH共同基金数据库。该项目解决了凳子上的巨大复杂性 元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“叙述性”)，可以 被广大的研究界所使用。

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.