Microbiome Taxonomic Profiling
Different types of microbes can be distinguished by comparison of the small differences in the DNA sequences of certain genes, such as SSU rRNA, that all microbes have. The result is a taxonomic fingerprint analogous to a barcode. Applying this type of sequence analysis to microbial communities creates detailed profiles of those communities at a relatively low cost. This is the method of choice if the desire is to provide a description of the diversity of microbes making up the microbiome.
Microbiome Metagenomic Profiling
For many applications, it is desirable to know not only what microbes are present in the community, but also the genes and the enzymes that those genes encode. This knowledge can often be used to determine the functional capabilities of the microbial community, and the relationship to environmental conditions. We use high throughput DNA and RNA sequence analysis of the collective community genomes or metagenomes, followed by bioinformatics processing, to generate this information about the metabolic potential of the microbiome.
The functions of many genes cannot be determined based solely on gene sequences. For this reason, we have developed an extensive collection of large-insert clone libraries that contain metagenomic DNA from diverse environmental sources. We can screen these libraries for functions of interest in a variety of different microbial host genetic backgrounds, often resulting in the discovery of truly novel genes with known function that can be considered for your application. If you are interested in a specific type of microbial community or sample, we will construct custom libraries from your samples. We have also developed methods to efficiently sequence newly isolated metagenomic clones, facilitating rapid identification of the genes of interest.
For DNA sequence information to be useful and informative, it must be computationally processed using bioinformatics tools. We provide a suite of bioinformatics services that can be applied based on the requirements of your project. We offer bioinformatic analysis integrated with our sequence packages, but if you have previously generated Envirogenomics data that requires further investigation, we can work with you to achieve the desired analysis.
Characterizing the microbiome through metagenomic sequencing requires considerable sequencing and analysis resources and is cost prohibitive for all but simple environments. We present the Metagenom-1© framework that integrates metagenomic and taxonomic high throughput sequencing to decrease costs and improve characterization of the microbiome.
Sequence-based investigations into the microbiome can broadly be characterized as who is present (survey) and what are they doing (functional). The Metagenom-1© framework seeks to address these questions by extrapolating higher resolution metagenomic libraries using our database of analyzed public genomes and metagenomes, microbial dark matter, and curated sequencing projects in conjunction with paired sequencing of client samples.
Metagenom-1© also provides a clear framework for identifying key functional and taxonomic drivers in the microbiome at decreased cost and increased resolution. A major goal of this strategy is to increase the accessibility of meaningful microbial community sequence analysis so that this technology can live up to its promise as a powerful enabler of solutions.
Highlights of Metagenom-1© platform:
Metagenomic inference using complete and draft genomes, assembled metagenomes, and microbial dark matter reference data (meaning far greater detail than currently available)
- Allows metagenome extrapolation far beyond typical metagenomic sequencing depth (saves money, directs future sequencing efforts)
Taxonomic characterization of a metagenome
- Characterization across any gene family, not only common marker genes
- Correspondence between metagenome and marker gene analysis
Biomarker identification and analysis
- Indicator species and indicator function analysis
Ecological network analysis to identify community structure and functional relationships