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A Brief Review of Earth Microbiomes and Applications
Published in Nwadiuto (Diuto) Esiobu, James Chukwuma Ogbonna, Charles Oluwaseun Adetunji, Olawole O. Obembe, Ifeoma Maureen Ezeonu, Abdulrazak B. Ibrahim, Benjamin Ewa Ubi, Microbiomes and Emerging Applications, 2022
Toochukwu E. Ogbulie, Nwadiuto (Diuto) Esiobu, Ifeoma Enweani-Nwokelo
For earth microbiome project, identity of species can be retrieved at 97% lower limit from clustered OTUs using the UCLUST algorithm. Novel sequences discovered in certain situation and which cannot be mapped to existing taxonomy can deduce its taxa from the new sequences and collections of closely related known sequences by creating a phylogenetic tree. Alpha and beta diversity analysis in this case can be done using varying phylogenetic software packages such as Phyloseq package, PHYLIP, phyloT, etc. To obtain comparative gene families, functional prediction profiling of microbiome from the earth using 16S rRNA datasets is carried out using the PICRUSt and KEGG (Kyoto Encyclopaedia of Genes and Genomes) software.
Advances in Microbial Molecular Biology
Published in Gustavo Molina, Zeba Usmani, Minaxi Sharma, Abdelaziz Yasri, Vijai Kumar Gupta, Microbes in Agri-Forestry Biotechnology, 2023
Deborah Catharine de Assis Leite, Naiana Cristine Gabiatti
Additionally, Langille et al. (2013) developed the PICRUSt—Phylogenetic Investigation of Communities by Reconstruction of Unobserved States—a method to predict metagenomic functional profiles from phylogenetic (16S rRNA gene) data using previous genomic knowledge about related taxa. By this “predictive metagenomic” approach, they reinforced that phylogeny and function are sufficiently attached and also provide useful information about uncultivated microbiomes for which just phylogenetic marker genes are available. Montanari-Coelho et al. (2018) used PICRUSt to verify the impact of genetically modified (GM) soybean plants on the predictive functionality of associated microbial communities.
Unraveling seaweeds bacteriomes
Published in Bénédicte Charrier, Thomas Wichard, C.R.K. Reddy, Protocols for Macroalgae Research, 2018
Tânia Aires, Gerard Muyzer, Ester A. Serrão, Aschwin H. Engelen
Metagenomics and metatranscriptomics are still expensive and time-consuming techniques. Some tools have been developed to help giving the first insight into the bacterial community potential function. Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt) is a software program that allows one to predict the functional composition of a metagenome using 16S rRNA gene data and a known database of reference genomes (Langille et al. 2013). Similar to QIIME, PICRUSt uses Python coding, and its installation process can be consulted in the following website: https://picrust.github.io/picrust/install.html#install.
New insights into the impact of nZVI on soil microbial biodiversity and functionality
Published in Journal of Environmental Science and Health, Part A, 2019
Carmen Fajardo, Jesús García-Cantalejo, Pedro Botías, Gonzalo Costa, Mar Nande, Margarita Martin
Thus, profiling phylogenetic marker genes (i.e., the 16S rRNA gene) is a key tool for studying microbial communities but does not provide direct evidence of a community’s functional capabilities. In the last few years, Langille et al.[13] suggested a way to overcome such a limitation. The authors developed the software PICRUSt (Phylogenetic Investigation of Communities by Reconstruction of Unobserved States) to predict the occurrence of functions within microbial communities solely based on bacterial 16S rRNA genes sequences. The overall correlation between phylogeny and functional attributes could predict the functions encoded in an organism’s genome based on functions encoded in closely related genomes. Previous studies performed in our laboratory demonstrated that nZVI caused a disturbance in specific functions of soil bacteria, as revealed by the upregulation or downregulation of some genes and/or proteins after nZVI treatment.[11] However, the establishment of linkages between soil microbial diversity and functionality shifts following nZVI exposure is still far from being developed.
Key Microbes and Metabolic Potentials Contributing to Cyanide Biodegradation in Stirred-Tank Bioreactors Treating Gold Mining Effluent
Published in Mineral Processing and Extractive Metallurgy Review, 2020
Doyun Shin, Jeonghyun Park, Hyunsik Park, Jae-Chun Lee, Min-Seuk Kim, Jaeheon Lee
The recently developed software package PiCRUSt can delineate the metabolic potential of microorganisms represented by their 16S rRNA sequences, based on the phylogenetic placement of the 16S rRNA sequences within a phylogeny of sequenced genomes (Langille et al. 2013). To generate a synthetic metagenome, the observed 16S rDNA sequences were clustered into a collection of OTUs in the Mothur package. The resultant biom-formatted OTU table was uploaded to the online Galaxy terminal (http://huttenhower.sph.harvard.edu/galaxy/) for processing. The biom-formatted OTU table was first normalized with respect to the inferred 16S rRNA gene copy numbers and then used to predict the metagenomic functional content with the software package PiCRUSt (Langille et al. 2013). This computational approach exploits the relationship between phylogeny and function by combining 16S rRNA gene data with a database of reference genomes (Greengenes) to predict the presence of gene families. This was done by first screening OTUs against the Greengenes database on 13 August 2013. Functional predictions were exported as Kyoto Encyclopedia of Genes and Genomes (KEGG) orthologues. From the PiCRUSt data, the relative abundance of gene function is calculated from the estimate of the genes presents divided by the total genes in the synthetic metagenome of the sample. The abundance of each OTU is divided by its predicted 16S rRNA copy number, and then multiplied by the copy number of the gene family to get the contribution of each OTU to the overall gene content of the sample. The individual contributions are summed to produce an estimate of the genes presents in the total metagenome of the sample.
Shift of microbial diversity and function in high-efficiency performance biotrickling filter for gaseous xylene treatment
Published in Journal of the Air & Waste Management Association, 2019
Mingxue Li, Yantao Shi, Yixuan Li, Yizhe Sun, Chunhui Song, Zhiyong Huang, Zongzheng Yang, Yifan Han
The Greengenes database was used to determine bacterial species diversity in the samples. The community composition of each sample was counted at the kingdom, phylum, class, order, family, and genus levels. The relative abundance of different bacterial genera in the samples was analyzed. Combined with principal coordinate analysis (PCoA; performed using R, Package Vegan) and SPSS one-way analysis of variance (ANOVA) analysis (version 19; IBM, Armonk, NY, USA), the similarities and differences between the top, middle, and bottom microbial communities were determined. Furthermore, microbial community function was predicted using PICRUSt (Langille et al. 2013).