<br>MiMiC：一种从宏基因组生成合成群落的生物信息学方法,Microbial Biotechnology

当前位置： X-MOL 学术 › Microb. Biotechnol. › 论文详情

Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)

MiMiC：一种从宏基因组生成合成群落的生物信息学方法
Microbial Biotechnology ( IF 4.8 ) Pub Date : 2021-06-03 , DOI: 10.1111/1751-7915.13845
Neeraj Kumar _{1,

2} , Thomas C A Hitch ₁ , Dirk Haller _{2,

3} , Ilias Lagkouvardos _{2,

4} , Thomas Clavel ₁

Affiliation

环境和宿主相关的微生物群落是复杂的生态系统，其中许多成员仍然未知。因此，研究群落动态具有挑战性，并且创建模仿主要群落功能的降低复杂性的模型系统很重要。因此，我们开发了 MiMiC，一种通过鸟枪法宏基因组进行数据驱动设计简化群落的计算方法。我们首先建立了物种级细菌和古菌基因组的综合数据库 ( n = 22 627)，由蛋白质家族的二元（存在/不存在）载体 (Pfam = 17 929) 组成。 MiMiC 使用基于该数据库与任何输入宏基因组的 Pfam 二元向量之间的最大匹配与不匹配比的迭代评分系统来预测最小联合体的组成。 Pfam 矢量化保留了足够的分辨率来区分六个环境和宿主衍生的微生物群落之间的宏基因组图谱 ( n = 937)。计算得出的每个最小群落的物种数量在 5 到 11 之间，MiMiC 选择的群落比随机选择的物种更好地概括了原始样本的功能库。推断的最小群落保留了栖息地特定的特征，并且与由最丰富的成员组成的群落有很大不同。使用已知微生物的混合物揭示了从整个基因组数据库中选择 25 个目标物种中的 23 个的能力。 MiMiC 是开源的，可从 https://github.com/ClavelLab/MiMiC 获取。

"点击查看英文标题和摘要"

MiMiC: a bioinformatic approach for generation of synthetic communities from metagenomes

Environmental and host-associated microbial communities are complex ecosystems, of which many members are still unknown. Hence, it is challenging to study community dynamics and important to create model systems of reduced complexity that mimic major community functions. Therefore, we developed MiMiC, a computational approach for data-driven design of simplified communities from shotgun metagenomes. We first built a comprehensive database of species-level bacterial and archaeal genomes (n = 22 627) consisting of binary (presence/absence) vectors of protein families (Pfam = 17 929). MiMiC predicts the composition of minimal consortia using an iterative scoring system based on maximal match-to-mismatch ratios between this database and the Pfam binary vector of any input metagenome. Pfam vectorization retained enough resolution to distinguish metagenomic profiles between six environmental and host-derived microbial communities (n = 937). The calculated number of species per minimal community ranged between 5 and 11, with MiMiC selected communities better recapitulating the functional repertoire of the original samples than randomly selected species. The inferred minimal communities retained habitat-specific features and were substantially different from communities consisting of most abundant members. The use of a mixture of known microbes revealed the ability to select 23 of 25 target species from the entire genome database. MiMiC is open source and available at https://github.com/ClavelLab/MiMiC.

更新日期：2021-07-27

点击分享查看原文

点击收藏

公开下载

阅读更多本刊新发论文本刊介绍/投稿指南