Suppr超能文献

对一个独特的玄武岩土壤森林中微生物磷循环基因的计算预测功能宏基因组与微阵列分析的比较。

A comparison of computationally predicted functional metagenomes and microarray analysis for microbial P cycle genes in a unique basalt-soil forest.

作者信息

LeBrun Erick S, Kang Sanghoon

机构信息

Center for Reservoir and Aquatic Systems Research, Department of Biology, Baylor University, Waco, TX, 76798-7388, USA.

出版信息

F1000Res. 2018 Feb 12;7:179. doi: 10.12688/f1000research.13841.1. eCollection 2018.

Abstract

Here we compared microbial results for the same Phosphorus (P) biogeochemical cycle genes from a GeoChip microarray and PICRUSt functional predictions from 16S rRNA data for 20 samples in the four spatially separated Gotjawal forests on Jeju Island in South Korea. The high homogeneity of microbial communities detected at each site allows sites to act as environmental replicates for comparing the two different functional analysis methods. We found that while both methods capture the homogeneity of the system, both differed greatly in the total abundance of genes detected, as well as the diversity of taxa detected. Additionally, we introduce a more comprehensive functional assay that again captures the homogeneity of the system but also captures more extensive community gene and taxonomic information and depth. While both methods have their advantages and limitations, PICRUSt appears better suited to asking questions specifically related to microbial community P as we did here. This comparison of methods makes important distinctions between both the results and the capabilities of each method and can help select the best tool for answering different scientific questions.

摘要

在此,我们比较了来自韩国济州岛四个空间分离的乔戈瓦尔森林中20个样本的GeoChip微阵列中相同磷(P)生物地球化学循环基因的微生物结果,以及基于16S rRNA数据的PICRUSt功能预测结果。在每个位点检测到的微生物群落的高度同质性使得这些位点能够作为环境重复样本,用于比较两种不同的功能分析方法。我们发现,虽然两种方法都能捕捉到系统的同质性,但在检测到的基因总丰度以及检测到的分类单元多样性方面,两者存在很大差异。此外,我们引入了一种更全面的功能分析方法,该方法不仅再次捕捉到了系统的同质性,还捕捉到了更广泛的群落基因和分类信息以及深度。虽然两种方法都有其优点和局限性,但PICRUSt似乎更适合像我们在此所做的那样,提出与微生物群落磷具体相关的问题。这种方法比较在每种方法的结果和能力之间做出了重要区分,并有助于选择最佳工具来回答不同的科学问题。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0021/6051228/e5829893fdf6/f1000research-7-15044-g0000.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验