This PCR-based method allows for the study of unculturable microorganisms. NGS metagenomic studies allow for the analysis of large numbers of samples, which allows investigators to address questions such as microbial diversity among a population of people or changes over time.
This tutorial will be based on the Qiime2 tool/Pipeline for Microbiome data analysis. This is very widely used, robust, and a preferred choice for many.
Understanding the microbiome: The microbiome refers to the collection of microbes, including bacteria, viruses, fungi, and archaea, that live in and on our bodies. Understanding the composition and function of the microbiome is important for understanding health and disease.
Sample collection: Samples for microbiome analysis can be collected from various body sites, such as the gut, skin, or oral cavity. The method of sample collection can have a large impact on the results of the analysis, so it is important to choose an appropriate method and to carefully control for potential sources of bias.
Sequence data: Microbiome data is typically generated using high-throughput sequencing of the 16S ribosomal RNA gene, which is a conserved region of the microbial genome. The sequencing data can be processed and analyzed to generate profiles of the microbial communities present in the samples.
Data preprocessing: Before analyzing microbiome data, it is important to clean and filter the raw sequencing data to remove contaminants and low-quality reads. This can be done using tools such as Qiime2 (https://qiime2.org/) or DADA2 (https://benjjneb.github.io/dada2/).
Data normalization: To compare samples and quantify differences in microbial community structure, it is important to normalize the data to account for differences in sequencing depth or sample size. Common normalization methods include rarefaction, which subsamples the data to a common depth, or scaling by the total number of reads per sample.
Data visualization: Visualizing the microbiome data can be helpful in understanding the distribution and relative abundance of different microbial taxa. Tools such as the R package ggplot2 (https://ggplot2.tidyverse.org/) or the QIIME2 plugin Emperor (https://emperor.microbiol.washington.edu/index.html) can be used to create plots such as bar charts or heatmaps of the relative abundance of different taxa.
Statistical analysis: Statistical analysis of the microbiome data can be used to identify significant differences in the abundance of microbial taxa between groups, or to detect changes in the microbiome over time. Common statistical methods include t-tests, ANOVA, or linear mixed effects models.
Data interpretation: Interpreting the results of microbiome data analysis requires a deep understanding of the underlying biology and the experimental design. Careful consideration of factors such as false discovery rates, effect size, and sample size can help to ensure that the results are reliable and interpretable.
Integration with other data: Microbiome data can be combined with other data sources, such as transcriptomics or metabolomics, to gain a more comprehensive understanding of the biological system being studied. Tools such as the R package limma (https://bioconductor.org/packages/release/bioc/html/limma.html) can be used to perform multivariate analysis of multiple data types.
Most current metagenomic studies follow one of two basic methods, either to survey and count microbes using amplicon sequencing of a single gene (usually the 16S rDNA gene) and taxonomic informatics methods, or shotgun metagenomic sequencing and a collection of ad hoc informatics methods that include de novo assembly, gene identification, and species identification.
The 16S rRNA gene is used to identify and categorize microorganisms in the microbiome because it is highly conserved within bacteria and has a well-defined structure, making it a suitable target for PCR amplification and sequencing. The 16S gene encodes a component of the ribosome and is present in most bacteria, allowing for broad-spectrum microbial community analysis. The variations within the 16S gene can be used to differentiate between different species and strains of bacteria, making it a valuable tool for characterizing microbial communities and exploring the relationships between microbes and their environment.
Basically, this is key and important to this kind of study because this gene is an essential component of the ribosome and has been found to be present in the genome of every bacteria known in this present day.
The sequence of the 16S gene is composed of highly conserved regions, which are suitable for the design of multispecies PCR primers, and variable regions, whose sequence can be used to distinguish different bacteria at a meaningful taxonomic level
The longer reads are employed because they contain sufficient taxonomic information to identify bacterial species. Shorter reads on the other hand cannot be joined by assembly because they may be coming from templates isolated from different species.
NB: Sometimes a sequence cannot be assigned to a given PCR amplified 16S fragment of a single bacteria species. In this case, it can be assigned to a group at a higher taxonomical level.
Some bacteria have multiple copies of the 16S gene (up to 15 copies have been observed in a single bacterial genome), and these multiple copies may have identical or different sequences. The so-called universal 16S PCR primers create bias in the amplified sequences, so that the abundances of species (or other taxonomic units) observed with one set of primers are not comparable to those
observed with other primers.