Subtractive assembly approaches for inferring disease-associated microbial genes and pathways from microbiome sequencing data

从微生物组测序数据推断疾病相关微生物基因和途径的减法组装方法

• 批准号: 10053318
• 负责人: Yuzhen Ye
• 金额: $ 27.03万
• 依托单位: TRUSTEES OF INDIANA UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-11-05 至 2022-10-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10053318
• 关键词: Algorithms; Biological; Case Study; Chemotherapy-Oncologic Procedure; Communities; Data; Data Analyses; Data Set; Development; Disease; Disease Marker; Disease Progression; Environment; Genes; Genome; Health; Human; Immunotherapy; Inflammatory Bowel Diseases; Link; Liver Cirrhosis; Malignant neoplasm of liver; Metagenomics; Methods; Molecular; Non-Insulin-Dependent Diabetes Mellitus; Pathway interactions; Patients; Phase; Probiotics; Research Personnel; Sampling; Software Tools; Time; Treatment Efficacy; United States National Institutes of Health; Work; analysis pipeline; base; cancer immunotherapy; comparative; computerized tools; differential expression; disease diagnosis; disorder prevention; improved; metagenome; metaproteomics; metatranscriptomics; microbial; microbial community; microbiome; microbiome analysis; microbiome research; microbiome sequencing; microbiota; non-alcoholic fatty liver disease; novel strategies; outcome prediction; predict responsiveness; predictive modeling; responders and non-responders; success; therapy outcome; tool; treatment responders

Summary Successful translational applications of microbiome research rely on computational tools that can effectively detect microbial markers that are associated with diseases, and provide explanations to the associations. We propose to develop subtractive assembly approaches to microbiome sequencing data analysis, aiming to identity microbial genes and pathways that are associated with diseases. The advantages of using subtractive assembly approaches include: 1) they significantly reduce the complexity of the fragment assembly problem by focusing only on the potential difference (genes and genomes) at the initial (instead of the final) stage of the comparative analysis pipeline, and 2) they improve the assemblies of differential genes, which are important inputs for building predictive models for disease diagnosis and characterization of treatment efficacy. We will apply our new tools to analyzing disease-associated microbiomes including those associated with type II diabetes, liver cancer, inflammatory bowel disease (IBD) and those known to be related to the efficacy of cancer immunotherapy.

摘要 微生物组研究的成功翻译应用依赖于计算工具 可以有效地检测与疾病相关的微生物标记物，以及 向协会提供解释。我们建议开发减法汇编 微生物组测序数据分析的方法，旨在识别微生物基因 以及与疾病相关的途径。使用减法的优点 组装方法包括：1)它们显著降低了片段的复杂性 只关注潜在差异(基因和基因组)的组装问题 比较分析管道的初始阶段(而不是最后阶段)，以及2)它们 改善差异基因的组装，这是构建 疾病诊断和治疗效果表征的预测模型。我们 将应用我们的新工具来分析与疾病相关的微生物群，包括 与II型糖尿病、肝癌、炎症性肠病(IBD)和 那些已知与癌症免疫疗法的疗效有关的药物。