Oral Microbiome Core

1. Experimental Design and Analysis Approach
   • The purposes of microbiomic studies are to understand the microbial composition of the biological samples, to compare the difference of the composition between different samples (e.g., health and disease, treatment or non-treatment), and to identify host or environmental factors that are associated with the microbial features (e.g. species, diversity, or functions). Microbiomic studies using the next generation sequencing (NGS) technology query sequence abundance of a single gene (e.g., 16S rRNA or ITS) or multiple genes (e.g., meta-genomic/meta-transcriptomic) in the samples and provide abundance information for hundreds to thousands of microbial genera or species. Thus the data are compositional, high dimensionality, non-normality and contained in phylogenetic structures.
   • FOMC provides complete end-to-end services for microbiome NGS research projects. From the experimental design, to experiments, sequencing, bioinformatics and statistical significance analyses, and manuscript preparation and publication.
     • First and foremost is the experimental design. We will assist you in the early stage of the research project to help design experiments, within the budget limit, with adequate sample size and statistical power in order to achieve significant results for the research hypothesis.
     • Sample size and statistical power calculation, considering false discovery rate and number of species
     • Sequencing depth estimation
     • Statistical analysis consideration and design – differential comparison and/or association analysis; T-test or ANOVA; multivariate analyses.
2. Next Generation Sequencing
   • 16S rRNA gene amplicon sequencing for V1V3 or V3V4 hypervariable regions for prokaryotic taxon identification
   • Ribosomal Internal Transcribed Spacer (ITS) region sequencing for fungal taxon identification
   • Meta-genomic sequencing for total DNA isolated from samples.
3. Comprehensive Data Analyses and Interpretation
   • Dr. Paster and Dr. Chen are well known in the oral bacterial scientific community with many publications that typically focus on, but not restricted to, the human oral and nasal cavities. For 16S rDNA datasets, the compositional data analysis (CoDa) approach will be used to prevent negative correction bias to ensure optimal results and interpretation.
4. Experimental design
   • Sample preparation
   • DNA extraction (as needed)
   • PCR primers for 16S rRNA gene amplicon NGS sequencing
   • DNA library preparation for high-coverage microbial genomic sequencing
   • Nucleic acid library preparation for metagenomic sequencing
5. State-of-the-art data analyses
   • Sequence quality filtering and amplicon sequence variants (ASVs) inference – DADA2
   • Taxonomy Assignment:
     • Alignment based algorithm: A robust species-level taxonomy assignment based on best sequence alignment to a set of 16S rRNA reference sequences originated from HOMD, NCBI and GreenGene. This algorithm works on both human oral/nasal and non-oral/non-nasal samples and are independent of sequenced regions (Al-Hebshi et al, 2015).
     • K-mer based algorithm: Samples from human oral/nasal cavity – Oral/nasal habitat-specific training sets (patent pending) in naïve Bayesian classification to achieve species/supraspecies level taxonomic assignment of 16S rRNA gene-derived ASVs (Escapa et al, 2020) (Watch the Youtube video abstract below)
     • Flowchart of the Taxonomy Assignment Pipeline: IMAGE: 16s_rRNA-pipeline…
   • Downstream 16S rRNA amplicon based data analyses:
     • Visual Analysis – Interactive taxonomy bar plots
     • Compositional data analysis (CoDa) – centered log ratio (clr) data transformation (Aitchison 1986)
     • Distance/Dissimilarity metric measurement – (Aitchison et al 2000) Aitchison distance
     • Microbial profiles – heatmap and clustering
     • Phylogenetic trees with relative abundance
     • Microbial diversity analyses – alpha and beta diversities, core microbiome analysis
     • Variance-based compositional principal component (PCA) biplot for beta-diversity exploration
     • Correlation analysis – SPARCC (Friedman and Alm, 2012)
       Differential Abundance Analysis – ALDEx2
     • LEfSe (Linear discriminant analysis Effect Size) determines the features (organisms, clades, operational taxonomic units, genes, or functions) most likely to explain differences between classes (non-compositional test)
6. Meta-genomic and meta-transcriptomic NGS data analysis
   • Reads quality trimming and removal of human sequences
   • Microbiome community analysis based on 16S rRNA genes identified from metagenomic/metatranscriptomic data
   • Metagenome-assembled genomes (MAGs) based analysis
   • Microbial functional and pathway enrichment analysis
   • Association of metadata with functional features
   • Flowchart of the meta-genomic/meta-transcriptomic pipeline: IMAGE: meta-transcriptomics
7. Consultation and Collaboration
   • Dr. Paster is an internationally known oral microbiome researcher. He can provide consultation on microbiome research projects and assist with data interpretation and writing. His service can also be on a collaboration basis. Please contact Dr. Paster for any of the following services.
     • Professional data interpretation
     • Assist in writing reports, grants, and manuscripts
     • Provide figures for manuscripts and grants

fomc@forsyth.org