Computational protocol: Location-Specific Oral Microbiome Possesses Features Associated With CKD

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Protocol publication

[…] The pair−end sequences were merged using PANDAseq. High-quality reads (length <400 or the quality score 1% of bases) were further split by barcode and trimmed of primer regions using QIIME1.9.0. Duplicate measurements of 10 samples were sequenced using different barcodes and batches to test the sequencing reproducibility. We used the command in QIIME with the default cutoff of 97% to cluster sequencing reads to operational taxonomic units (OTUs) using Uclust. The program further built a biom-formatted OTU table with assigned taxonomic information for each OTU. Using Chimera Slayer, chimera sequences arising from the polymerase chain reaction amplification were detected and excluded from the aligned representative sequences and the OTU table.The microbial diversity within each sample, or α diversity, was calculated using the Shannon index and Inverse Simpson index as metrics and represented the measure of diversity at the genus level., The overall microbiome dissimilarities among all samples, or β diversity, were assessed using the Bray−Curtis distance matrices and visualized by a nonmetric multidimensional scaling plot. The pairwise permutational multivariate analysis of variance (PERMANOVA) procedure,, using the Adonis function of the R package vegan 2.0-5 with the maximum number of permutations = 999, was performed to test the significance of the overall microbiome differences between the oral microbiota grouped by sampling locations and subject clinical status.Using the linear discriminant analysis effect size method, we further selected the microbiome features significantly associated with CKD at various taxonomic ranks with the absolute value of the linear discriminant analysis score >3.0. Several abundant differential genera were further analyzed to compare the mean and variance of the relative abundance. We applied receiver operating characteristic analysis using the pROC package in R to assess the performance of CKD classification based on selected microbiome features. The Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt) was used to predict the metagenome functional content based on the close reference-based OTU table generated using the QIIME pipeline with our 16S rRNA sequencing data. […]

Pipeline specifications

Software tools PANDAseq, QIIME, UCLUST, LEfSe, PICRUSt
Applications Phylogenetics, Metagenomic sequencing analysis, 16S rRNA-seq analysis
Organisms Homo sapiens, Escherichia coli
Diseases Renal Insufficiency, Chronic