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Human Gut Microbiota–Transplanted Gn Pig Models for HRV Infection
Published in Lijuan Yuan, Vaccine Efficacy Evaluation, 2022
Sequencing reads were processed with Quantitative Insights into Microbial Ecology (QIIME) (Caporaso et al., 2010). High-quality reads with Phred quality score ≥20 (corresponding to a sequencing error rate ≤0.01) were clustered into operational taxonomic units (OTUs) with the program UCLUST (Edgar, 2010). Chimeric sequences were identified with CHIMERASLAYER (Haas et al., 2011) and removed from further analysis. Bacterial taxonomy was assigned by using a naïve Bayes classifier (Wang et al., 2007b) against reference databases and bacterial taxonomy maps at Greengenes (McDonald et al., 2012). A phylogenetic tree was constructed (Price et al., 2010) from PyNAST-aligned sequences representing each OTU. Principle coordinate analysis on stool samples was based on UniFrac distances (Lozupone and Knight, 2005). Distance-based redundancy analysis for the effect of HRV on community structures was performed with the Vegan package (Vegan: Community Ecology Package, 2013). Shannon and Simpson diversity indices and a rank abundance curve were both generated with QIIME.
Biochemical Markers in Ophthalmology
Published in Ching-Yu Cheng, Tien Yin Wong, Ophthalmic Epidemiology, 2022
Abdus Samad Ansari, Pirro G. Hysi
Conversely, functional studies utilize a whole-genome shotgun (WGS) metagenomics approach when looking at the human microbiome [178]. These metagenomics studies offer a more complete picture of the microbiome by also providing taxonomic and functional profiling information. The main benefit of this WGS-based metagenomics is the ability to capture genetic information from all available organisms present within a sample, including those that are unknown or that are not amenable to standard culturing protocols. A number of tools can subsequently be utilized for analysis including mothur [179], W.A.T.E.R.S [180], Ribosomal Database Project (RDP) pyrosequencing [181], and QIIME [182]. Data generated from targeted analysis allow for the assignment of an organism to a specific taxonomy and count the frequency of a group of organisms.
Impact of tuberculosis disease on human gut microbiota: a systematic review
Published in Expert Review of Anti-infective Therapy, 2023
Tejaswini Baral, Shilia Jacob Kurian, Levin Thomas, Chandrashekar Udyavara Kudru, Chiranjay Mukhopadhyay, Kavitha Saravu, Mohan K Manu, Jitendra Singh, Murali Munisamy, Amit Kumar, Bidita Khandelwal, Mahadev Rao, Sonal Sekhar Miraj
The biological specimen for GM analysis was stool in all the studies. All the studies employed the standard protocol for stool sample storage at −80°C prior to processing and sequencing. All studies analyzed GM using the next-generation sequencing (NGS) technique. Among that, nine studies used 16S amplicon sequencing [7,9,14,16–18,20–22], one used shot gun metagenomic sequencing [15], and two used both techniques for the GM analysis [12,19]. Illumina MiSeq and Illumina HiSeq were the most used high-end sequencing platforms. Only Wang et al., 2022 used the Ion S5TMXL platform for sequencing [7]. All the included studies mostly used quantitative insights into microbial ecology (QIIME) software by for processing the bioinformatic data generated from the raw data after sample sequencing (Table 2).
Altered microbial biogeography in an innate model of colitis
Published in Gut Microbes, 2022
Antonia Boger-May, Theodore Reed, Diana LaTorre, Katelyn Ruley-Haase, Hunter Hoffman, Lauren English, Connor Roncagli, Anne-Marie Overstreet, David Boone
QIIME (Version 1.9.1) software was used to analyze the raw fastq 16S rRNA sequencing data. For quality filtering, we used a Phred score of 30, removed chimeras, and removed samples with sequencing below 5000. These sequences were clustered into Operational taxonomic units (OTUs) using uclust open reference as the clustering algorithm with Greengenes reference database and a sequencing identity match of 97%. The most detailed taxonomic level was chosen for each OTU. Alpha and Beta diversity were assessed by phylogenetic diversity using Phyloseq and Vegan in R studio. Samples were grouped by genotype (TRAG and RAG1−/−) and inner or outer mucus layer. Shannon diversity index was used to analyze Alpha diversity. Principal coordinates analysis (PCoA) plots using Bray-Curtis and Weighted UniFrac distances were used to assess the variation between samples.
Streptococcus mutans-associated bacteria in dental plaque of severe early childhood caries
Published in Journal of Oral Microbiology, 2022
Yixin Zhang, Jiakun Fang, Jingyi Yang, Xiaolei Gao, Liying Dong, Xuan Zheng, Liangjie Sun, Bin Xia, Na Zhao, Zeyun Ma, Yixiang Wang
The bioinformatics analysis was conducted using QIIME. The alpha diversity indices of Chao1, ACE and Shannon and Simpson were calculated using Mothur software (version v.1.30) with coverage over 99%. Beta diversity analysis was performed by nonmetric multi-dimensional scaling and unweighted pair-group method with arithmetic mean based on the unweighted UniFrac distances. The Wilcoxon rank-sum test was used to compare the relative abundance of the bacterial species. Linear discriminant analysis (LDA) effect size (LEfSe) was conducted to define the biomarkers of the three groups. The threshold on the logarithmic LDA score for the distinguishing features was set to 4.0. We performed co-occurrence analysis through Spearman correlations for compositional data calculation according to the abundance and variation of each taxon in each sample using SPSS software. Microbial functions were predicted using PICRUSt (v1.0.0) software following the online protocol and aligned to the Clusters of Orthologous Groups of proteins (COG) database. qPCR data were analyzed with ANOVA. Level of statistical significance (p value) was set as <0.05.