4/10/2023 0 Comments Conet cytoscape* Arctic freshwater systems (EBI Study Accession: PRJEB15630, ID Qiita 1883) * Gut bacteria of Peruvian rainforest ants (EBI Study Accession: PRJEB15630, ID Qiita 10343) * Honeybees from Puerto Rico (EBI Study Accession: PRJEB14927, ID Qiita 1064) * Soil from California vineyards (EBI Study Accession: PRJEB15630, ID Qiita 10082) * The Global Sponge Microbiome (DOI: 10.1038/ncomms11870, ID Qiita 1740) * Tree leaves (DOI: 10.1111/j., ID Qiita 396) * HMP healthy human (DOI: 10.1038/nature11234, ID Qiita 1928) * TARA Ocean Project (DOI: 10.1126/science.1261359). All data are described in Table 2, Part 5 of S1 Appendix. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.ĭata Availability: All microbiota data used in the article are available in the Qiita database ( ) and on the TARA Ocean project web page ( ). Received: JAccepted: FebruPublished: March 15, 2019Ĭopyright: © 2019 Cougoul et al. Different possibilities for improving the analysis of associations within microbiota are discussed.Ĭitation: Cougoul A, Bailly X, Vourc’h G, Gasqui P (2019) Rarity of microbial species: In search of reliable associations. This trimming strategy could significantly reduce the computational time needed to infer networks and network inference quality. Identifying testable associations could serve as an objective method for filtering datasets in lieu of current empirical approaches. This constraint could hamper the identification of candidate biological agents that could be used to control rare pathogens. We found that a large proportion of pairwise associations, especially negative associations, cannot be reliably tested. We explored the utility of common statistics for testing associations the sensitivity of alternative association measures and the performance of network inference tools. Our goal was to understand the impact of OTU rarity on the detection of associations. The performance of association detection tools is impaired when there is a high proportion of zeros in OTU tables. This feature of community structure can lead to methodological difficulties: simulations have shown that methods for detecting pairwise associations between OTUs, which presumably reflect interactions, yield problematic results. Microbiota contain hundreds to thousands of operational taxonomic units (OTUs), most of them rare. The role of microbial interactions in defining the properties of microbiota is a topic of key interest in microbial ecology.
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