Mining relationships between microbes and the environment they live in are crucial to understand the intrinsic mechanisms that govern cycles of carbon, nitrogen and energy in a microbial community. Building upon next-...Mining relationships between microbes and the environment they live in are crucial to understand the intrinsic mechanisms that govern cycles of carbon, nitrogen and energy in a microbial community. Building upon next- generation sequencing technology, the selective capture of 16S rRNA genes has enabled the study of co-occurrence patterns of microbial species from the viewpoint of complex networks, yielding successful descriptions of phenomena exhibited in a microbial community. However, since the effects of such environmental factors as temperature or soil conditions on microbes are complex, reliance on the analysis of co-occurrence networks alone cannot elucidate such complicated effects underlying microbial communities. In this study, we apply a statistical method, which is called Boolean implications for metagenomic studies （BIMS） for extracting Boolean implications （IF-THEN relationships） to capture the effects of environmental factors on microbial species based on 16S rRNA sequencing data. We first demonstrate the power and effectiveness of BIMS through comprehensive simulation studies and then apply it to a 16S rRNA sequencing dataset of real marine microbes. Based on a total of 6,514 pairwise relationships identified at a low false discovery rate （FDR） of 0.01, we construct a Boolean implication network between operational taxonomic units （OTUs） and environmental factors. Relationships in this network are supported by literature, and, most importantly, they bring biological insights into the effects of environmental factors on microbes. We next apply BIMS to detect three-way relationships and show the possibility of using this strategy to explain more complex relationships within a microbial community.展开更多
The authors introduce the notions of （∈, ∈ ∨q）-fuzzy Boolean （implicative, positive implicative, and fantastic） filters in BL-algebras, present some characterizations on these generalized fuzzy filters, and des...The authors introduce the notions of （∈, ∈ ∨q）-fuzzy Boolean （implicative, positive implicative, and fantastic） filters in BL-algebras, present some characterizations on these generalized fuzzy filters, and describe the relations among these generalized fuzzy filters. It is proved that an （∈, ∈ ∨q）fuzzy filter in a BL-algebra is Boolean （implicative） if and only if it is both positive implicative and fantastic.展开更多
文摘Mining relationships between microbes and the environment they live in are crucial to understand the intrinsic mechanisms that govern cycles of carbon, nitrogen and energy in a microbial community. Building upon next- generation sequencing technology, the selective capture of 16S rRNA genes has enabled the study of co-occurrence patterns of microbial species from the viewpoint of complex networks, yielding successful descriptions of phenomena exhibited in a microbial community. However, since the effects of such environmental factors as temperature or soil conditions on microbes are complex, reliance on the analysis of co-occurrence networks alone cannot elucidate such complicated effects underlying microbial communities. In this study, we apply a statistical method, which is called Boolean implications for metagenomic studies （BIMS） for extracting Boolean implications （IF-THEN relationships） to capture the effects of environmental factors on microbial species based on 16S rRNA sequencing data. We first demonstrate the power and effectiveness of BIMS through comprehensive simulation studies and then apply it to a 16S rRNA sequencing dataset of real marine microbes. Based on a total of 6,514 pairwise relationships identified at a low false discovery rate （FDR） of 0.01, we construct a Boolean implication network between operational taxonomic units （OTUs） and environmental factors. Relationships in this network are supported by literature, and, most importantly, they bring biological insights into the effects of environmental factors on microbes. We next apply BIMS to detect three-way relationships and show the possibility of using this strategy to explain more complex relationships within a microbial community.
基金the Key Science Foundation of Education Committee of Hubei Province,China,under Grant No.D200729003
文摘The authors introduce the notions of （∈, ∈ ∨q）-fuzzy Boolean （implicative, positive implicative, and fantastic） filters in BL-algebras, present some characterizations on these generalized fuzzy filters, and describe the relations among these generalized fuzzy filters. It is proved that an （∈, ∈ ∨q）fuzzy filter in a BL-algebra is Boolean （implicative） if and only if it is both positive implicative and fantastic.
