Condensing the omics fog of microbial communities

被引:54
作者
Muller, Emilie E. L. [1 ]
Glaab, Enrico [1 ]
May, Patrick [1 ]
Vlassis, Nikos [1 ]
Wilmes, Paul [1 ]
机构
[1] Univ Luxembourg, Luxembourg Ctr Syst Biomed, L-4362 Esch Sur Alzette, Luxembourg
关键词
Eco-Systems Biology; function prediction; integrated omics; machine learning; microbial communities; systematic measurements; MINIMUM INFORMATION; DIMENSIONALITY REDUCTION; FEATURE-SELECTION; PROTEOMICS; FRAMEWORK; GENOMES; RECONSTRUCTION; ASSEMBLAGES; NETWORK; SERVER;
D O I
10.1016/j.tim.2013.04.009
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
Natural microbial communities are ubiquitous, complex, heterogeneous, and dynamic. Here, we argue that the future standard for their study will require systematic omic measurements of spatially and temporally resolved unique samples in line with a discovery-driven planning approach. Resulting datasets will allow the generation of solid hypotheses about causal relationships and, thereby, will facilitate the discovery of previously unknown traits of specific microbial community members. However, to achieve this, solid wet lab, bioinformatic and statistical methodologies are required to have the promises of the emerging field of Eco-Systems Biology come to fruition.
引用
收藏
页码:325 / 333
页数:9
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