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

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）它们显著降低片段的复杂性 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。

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.