Univ Rennes, Inria, CNRS, IRISA, Rennes, France.
Université de Nantes, LS2N, CNRS, Nantes, France.
Bioinformatics. 2018 Sep 1;34(17):i934-i943. doi: 10.1093/bioinformatics/bty588.
The selection of species exhibiting metabolic behaviors of interest is a challenging step when switching from the investigation of a large microbiota to the study of functions effectiveness. Approaches based on a compartmentalized framework are not scalable. The output of scalable approaches based on a non-compartmentalized modeling may be so large that it has neither been explored nor handled so far.
We present the Miscoto tool to facilitate the selection of a community optimizing a desired function in a microbiome by reporting several possibilities which can be then sorted according to biological criteria. Communities are exhaustively identified using logical programming and by combining the non-compartmentalized and the compartmentalized frameworks. The benchmarking of 4.9 million metabolic functions associated with the Human Microbiome Project, shows that Miscoto is suited to screen and classify metabolic producibility in terms of feasibility, functional redundancy and cooperation processes involved. As an illustration of a host-microbial system, screening the Recon 2.2 human metabolism highlights the role of different consortia within a family of 773 intestinal bacteria.
Miscoto source code, instructions for use and examples are available at: https://github.com/cfrioux/miscoto.
当从研究大型微生物群落转向研究功能有效性时，选择表现出感兴趣代谢行为的物种是一个具有挑战性的步骤。基于分隔框架的方法不可扩展。基于非分隔建模的可扩展方法的输出可能非常大，以至于迄今为止尚未得到探索或处理。
我们介绍了 Miscoto 工具，通过报告几种可能性来简化选择微生物组中优化期望功能的群落的过程，然后可以根据生物学标准对其进行排序。使用逻辑编程并结合非分隔和分隔框架来彻底识别群落。对与人类微生物组计划相关的 490 万个代谢功能进行基准测试表明，Miscoto 适合根据可行性、功能冗余和涉及的合作过程来筛选和分类代谢产物的生产能力。作为宿主-微生物系统的一个例证，筛选 Recon 2.2 人类新陈代谢突出了 773 种肠道细菌家族中不同菌群的作用。
Miscoto 的源代码、使用说明和示例可在以下网址获得：https://github.com/cfrioux/miscoto。
Zotero 插件
