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Poultry and Eggs
Published in Christopher Cumo, Ancestral Diets and Nutrition, 2020
This book seeks precision by specifying animals, plants, fungi, bacteria, and protists’ scientific names. These names are part of the International Code of Phylogenic Nomenclature and are meant to standardize biology’s language. The taxonomic rank class includes Aves, whose members are birds. These vertebrates lay eggs with hard shells, have wings and feathers, and are warm blooded.
Taxonomy and Grouping
Published in Paul Pumpens, Single-Stranded RNA Phages, 2020
The 2018b release introduced a set of novel taxonomic levels, such as “Realm,” “Subrealm,” “Kingdom,” “Subkingdom,” “Phylum,” “Subphylum,” “Class,” and “Subclass,” which are followed now by the formerly higher “Order” level. The “Realm” is defined now as the highest taxonomic rank into which virus species can be classified. The only currently defined realm Riboviria covers RNA viruses: altogether 1 phylum, 3 orders, 40 families, and 8 genera. The Leviviridae family is included therefore into the huge Riboviria realm.
Classification and Systematics
Published in Jacques Derek Charlwood, The Ecology of Malaria Vectors, 2019
Taxonomy is the arrangement of similar entities (objects) in a hierarchical series of nested classes, in which each, more inclusive, higher-level class is subdivided comprehensively into less inclusive classes at the next lower level. These classes (groups) are known as taxa (singular: taxon). The level of a taxon in a hierarchical classification is referred to as a taxonomic rank or category.
A transcriptomic insight into the human sperm microbiome through next-generation sequencing
Published in Systems Biology in Reproductive Medicine, 2023
Celia Corral-Vazquez, Joan Blanco, Riccardo Aiese Cigliano, Sarrate Zaida, Francesca Vidal, Ester Anton
The quality of the raw reads was assessed with FASTQC v.0.11.8, and then trimming and clipping were performed using BBDuk by setting a minimum base quality of 1 and a minimum read length of 35 bp. The obtained reads were aligned to the microbiome genome and transcriptome and analyzed using the GAIA software (www.metagenomics.cloud) (Paytuví et al. 2019). For this purpose, several databases were employed: Metatranscriptomics (16S, 18S and Internal Transcribed Spacer); Prokaryotes (Whole Genome Sequencing [WGS] and Whole Transcriptome Sequencing [WTS], release 2020); and Viruses (WGS and WTS, release 2020). The Human WGS and WTS databases (release 2020) were used to filter out human-specific reads. Species were identified and classified in any of the non-eukaryote databases according to the alignment analysis. When this alignment did not allow identification of an organism to the taxonomic rank of species, the detected features were classified into less specific ranks: genus, families, orders, or domains. The RNA abundance levels of each microbiome element were expressed in percentage of Operational Taxonomic Unit (OTU). The percentages of OTU were calculated by dividing the quantified reads of the microbiome element by the total number of reads in its taxonomic rank. Therefore, the different identified species were comparable with each other, and also the different genus, families, etc.
Evaluating live microbiota biobanking using an ex vivo microbiome assay and metaproteomics
Published in Gut Microbes, 2022
Xu Zhang, Krystal Walker, Janice Mayne, Leyuan Li, Zhibin Ning, Alain Stintzi, Daniel Figeys
All MS raw files were subjected to data processing using MetaLab (version 1.2), a bioinformatic tool for automated and comprehensive metaproteomic data analysis.39 Briefly, peptides and proteins were identified and quantified using the MetaPro-IQ workflow.40 To generate a reduced database, redundant spectra were removed using a spectral clustering strategy and the resulting clustered spectra were then searched against the human gut microbial gene catalog database (containing 9.9 million microbial genes).41 All matched proteins were then extracted, and their sequences were compiled as a database for a second step target-decoy database search with a strict filtering of the peptide-spectrum matches (PSMs) based on a false discovery rate (FDR) of 0.01. Relative abundances of identified protein groups were quantified using label-free quantification (LFQ) with maxLFQ algorithm.42 Taxonomic annotation of all identified peptide sequences was performed using a built-in pep2tax database as described previously.39 The identified taxa were then quantified using the intensities of corresponding distinctive peptides and were analyzed at different taxonomic rank levels separately. Identified protein groups were then subjected for functional annotation using eggNOG-mapper.43 In this study, Clusters of Orthologous Groups of proteins (COGs) and COG category were used for functional analyses. Relative abundances of a COG or COG category were derived by summing the LFQ intensities of all protein groups that were annotated as that COG or COG category.
Characterization of the oral microbiome of children with type 1 diabetes in the acute and chronic phases
Published in Journal of Oral Microbiology, 2022
Xiaoxiao Yuan, Jin Wu, Ruimin Chen, Zhihong Chen, Zhe Su, Jinwen Ni, Miaoying Zhang, Chengjun Sun, Fengwei Zhang, Yefei Liu, Junlin He, Lei Zhang, Feihong Luo, Ruirui Wang
Next, we applied the LEfSe method to analyze specific differences between the compositions of microbiotas and to search for potential biomarkers among these three groups. The cladogram represents significantly different taxa among the three groups according to a hierarchy that reflects the taxonomic rank from phylum to genus (Supplementary Figure S5), which was consistent with the whole oral microbiota structure (Figure 3). Leptotrichia, a health-associated genus; TM7x, which virulently kills host bacteria; and Prevotella, an acetate-producing bacterium; were significantly enriched in the CON group. Potential periodontal pathogens, such as Capnocytophaga and Granulicatella, and genera harboring many opportunistic pathogens, such as Streptococcus, Staphylococcus, and Enterobacterales were significantly enriched in the NT1D group. Fusobacterium, Leptotrichia, and Eubacterium were found to be significantly enriched in the CT1D group. Thus, these bacteria could be considered hyperglycemia-associated taxa and potential biomarkers for T1D.