Whole Metagenomic Functional Profiling
This approach goes beyond 16S rRNA microbial abundance and diversity determination to provide valuable insight into the functional gene composition of the microbial community. This advanced metagenomic investigation leverages specialized methods in sequencing and bioinformatics to provide a window into the function and pathways encoded in the microbiome.
In order to obtain comprehensive whole genome information, our metagenomic profiling services utilize shotgun sequencing. This technology sequences fragments from the whole genome of multiple microbes simultaneously, creating a broad view of the functions important to bacterial life in the native environment of the microbiota. Our team can provide guidance on recommended sample depth in order to ensure the highest quality metagenomic data set.
All metagenomic investigations include 16S rRNA and non-16S taxonomic annotation. 16S rRNA gene reads are identified and annotated against the Greengenes™ database of cultured and uncultured organisms, and non-16S reads are leveraged to uniquely identify microbes. Multiple methods help ensure a balanced approach to evaluating changes in the community structure across sample groups.
Functional annotation estimates the gene function of each read sequence by matching it against microbial entries in pathway/genome databases . The reference genomes are a consistent, non-redundant, and well-annotated resource providing enzymatic, reaction, metabolite, and pathway annotations.
Pathway annotation assigns sequences to the likely cellular or metabolic pathways related to the proposed function. Each identified pathway includes a review of the relevant biology with full literature citations. Our reference for these annotations is MetaCyc, a database of experimentally determined metabolic pathways, metabolites, reactions, enzymes, and genes that includes more than 2,000 pathways.
After establishing the relative abundance of pathways for each community, an assessment of community-level pathway similarity is obtained. Assuming that microbial communities with similar pathway representation share similar ecology, this assessment can inform the broader function of the community. Differentially represented pathways are also highlighted to inform which pathways are over or underrepresented in the experimental groups.