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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

基本信息

批准号：
10307128
负责人：
Yuzhen Ye
金额：
$ 30.96万
依托单位：
TRUSTEES OF INDIANA UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2018
资助国家：
美国
起止时间：
2018-11-05 至 2024-10-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10307128
关键词：
Algorithms Biological Case Study Chemotherapy-Oncologic Procedure Collaborations 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 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 translational applications treatment responders

项目摘要

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.

总结微生物组研究的成功转化应用依赖于计算工具可以有效检测与疾病相关的微生物标志物，向协会作出解释。我们建议发展减法装配微生物组测序数据分析方法，旨在识别微生物基因和与疾病相关的途径。使用减法的优点组装方法包括：1）显着降低片段的复杂性组装问题，只关注潜在的差异（基因和基因组），比较分析管道的初始（而不是最终）阶段，以及2）它们改善差异基因的组装，这是构建用于疾病诊断和治疗功效表征的预测模型。我们将应用我们的新工具来分析疾病相关微生物组，包括与II型糖尿病、肝癌、炎症性肠病（IBD）和这些已知与癌症免疫疗法的功效有关。