Case Studies
Select an option below:
- Multi-omics Data Integration and Analysis
- Clonal-level Microbiomics™ for Strain Tracking
No single analysis can account for how the microbiome influences the host in health or disease. Multi-omics integrative analysis brings together different types of data to help understand the system as a whole and how the microbiome works in this context to influence the host. In the hands of a highly experienced team, such analyses can avoid spurious correlations and identify true signals that can be used for drug development or other important applications.
The Clinical Microbiomics Clonal-level Microbiome Profiling pipeline provides the most sensitive and accurate microbiome profiling in the field, with clonal-level resolution. We offer development and curation of prior knowledge ontologies, enabling smart dimensionality reduction. Gene-neighborhood-aware functional annotation is part of the Clonal-level Microbiomics™️ Platform to identify and/or infer functionality of all species in a community.
In some studies, researchers may want to detect the presence of microbial strains in samples over time. This tracking of strains is important, for example, in determining whether a probiotic strain persists in the gut or whether strains from a fecal microbiota transplant have stably engrafted in their new environment. Many analysis pipelines, however, do not provide sufficient resolution to detect strains (or bacterial clones), which reduces confidence in determining whether the exact strains of interest are present over time.
Clinical Microbiomics offers its Clonal-level Microbiome Profiling pipeline, providing the most sensitive and accurate microbiome profiling in the field. With Clonal-level Microbiomics™️, we resolve clonal populations (i.e. strains) found in samples using single nucleotide differences, and use phylogeny to relate them to each other and build trees. The pipeline can resolve strains with an average nucleotide identity (ANI) of 99.9%. This method also enables us to link host or microbial traits to clades (phylogenetic groups) within microbial populations, and even to mine the genes behind the functional differences.