The term microbiota and microbiome have become synonymous. Both include bacteria, fungi, protozoa, viruses, phages, and archaea. However, the exact definition of microbiota is not yet clear. It will be clarified in a future opinion paper that will take a holistic, systemic perspective.
Metagenomic libraries are a valuable tool for studying the diversity of microorganisms in the environment. DNA sequencing of metagenomic samples can identify a wide range of species in the air, water, and dirt. They can even identify items in an individual's blood meal. Metagenomics can also help establish the range of invasive and endangered species, track seasonal populations, and profile a person's gut microbiome. It can also be used to detect antibiotic-resistant bacteria.
A microbiome can be defined as a microhabitat containing the genomes and genes of a variety of microbes. A microbiome can also include mobile genetic elements. The two terms are sometimes confused, and it's important to distinguish between them.
Beta diversity measures the degree of difference in community membership or structure between two samples.
A beta diversity metric gives greater weight to the common taxa in a community. It is computed pairwise using all samples and identifies the degree of similarity between two communities. A beta metric is a valuable tool for comparing two samples of microbiota.
Alpha and beta diversity metrics can compare microbiota samples and determine the sample size required for statistical significance. These metrics are also helpful in determining the power of a study. The effect size is calculated from the percentage of differentially abundant microbial features present in one dataset but not the other.
Alpha and beta diversity measures the abundance of taxa in a sample. They also capture the abundance distribution of taxa. Alpha diversity varies between two samples of the same individual but less across. However, richness and evenness differ between individuals more than temporal variation. In addition, the Shannon diversity index varies more between individuals than between samples.
UniFrac measures the proportion of shared branch lengths on a phylogenetic tree.
UniFrac can be used to measure the evolutionary history of sequences by determining the proportion of shared branch lengths on a particular phylogenetic tree. It can be used to compare two sequences and can also be used to make comparisons between phylogenetic trees. This method has been used in over 150 research publications.
The UniFrac metric is used to compare different phylogenetic communities. It measures the proportion of shared branch lengths between two communities. This ratio indicates the similarity between communities. For example, a square and a circle will have very different proportions of shared branch lengths.
UniFrac uses Monte Carlo simulations to compare two communities. If the proportion of shared branch lengths between two communities is higher than expected by chance, the communities are considered different.