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. 2018 Mar 1;10(3):707-715.
doi: 10.1093/gbe/evy031.

Microbial Dark Matter Investigations: How Microbial Studies Transform Biological Knowledge and Empirically Sketch a Logic of Scientific Discovery

Affiliations

Microbial Dark Matter Investigations: How Microbial Studies Transform Biological Knowledge and Empirically Sketch a Logic of Scientific Discovery

Guillaume Bernard et al. Genome Biol Evol. .

Abstract

Microbes are the oldest and most widespread, phylogenetically and metabolically diverse life forms on Earth. However, they have been discovered only 334 years ago, and their diversity started to become seriously investigated even later. For these reasons, microbial studies that unveil novel microbial lineages and processes affecting or involving microbes deeply (and repeatedly) transform knowledge in biology. Considering the quantitative prevalence of taxonomically and functionally unassigned sequences in environmental genomics data sets, and that of uncultured microbes on the planet, we propose that unraveling the microbial dark matter should be identified as a central priority for biologists. Based on former empirical findings of microbial studies, we sketch a logic of discovery with the potential to further highlight the microbial unknowns.

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Figures

<sc>Fig</sc>. 1.—
Fig. 1.—
Four types of environmental sequences. Environmental sequences can be classified based on their taxonomical annotation (horizontal line) and their functional annotation (vertical column), which defines four categories. The cells in purple and black correspond to categories that are not readily explained based on current biological knowledge.
<sc>Fig</sc>. 2.—
Fig. 2.—
Microbial dark matter across a diversity of environmental samples. Proteins inferred (with FragGeneScan; Rho et al. 2010) based on Metagenomic sequences from (Fondi et al. 2016), clustered based on their taxonomy (using MEGAN 6; Huson et al. 2016) and functional (using EggNOG-mapper; Huerta-Cepas et al. 2017) annotation. The pie charts represent the proportion of proteins from each type of environment. The taxonomy annotation was performed using three minimum percentage of identity: 50% (panels A and B), 85% (panels C and D), and 95% (panels E and F). In panels A, C, and E, the proteins were clustered based on their functional annotation including the category S (“Function unknown”). Panels B, D, and F were clustered with the exclusion of the category S.

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