Clarity Genomics uses well-established and state-of-art algorithms for integrative analysis of host-microbiome data to provide valuable interpretations for clinical research. We support translational and clinical development programs leveraging the therapeutic and diagnostic potential of the host microbiome. Academic collaborations with world-leading research institutions ensure that your project receives relevant information utilizing technologies and techniques on the forefront of innovation.
With access to a manually curated database of thousands of human metagenomes and involvement in large-scale collaboration projects including the American Gut Project, the Earth Microbiome Project and the NIH Human Microbiome Project, Clarity Genomics leverages its data aggregation potential for smarter diagnostics across different microbial ecosystems.
Clarity Genomics works together with certified DNA sequencing and mass spectrometry institutions to offer clients an end-to-end solution for host-microbiome data generation and analysis. Clarity Genomics leverages secure cloud computing for high-throughput data analysis having security and privacy compliance in accordance with HIPPA, and reproducibility and documentation for CLIA compliance.
Some of our services include,
- Study experimental design and consulting
- DNA sequence and metabolomics data quality control
- Targeted gene analysis (16S taxonomy, abundance, diversity analysis)
- Untargeted metabolomics feature detection and statistical analysis
- Putative metabolite identification using spectral matching
- Annotation of unknown compounds using molecular networking
- 2D/3D molecular and microbial mapping
- Pathway mapping of metabolomics data
- Genome assembly and annotation (de novo and re-sequencing)
- Pan-genome and phylogeny analysis for gene presence/absence of specific strains
- Strain profiling against manually curated human metagenome database
- Time series multi-omic analysis (differential gene expression, differential metabolite/protein abundance)
- Gene ontology and metabolic pathways analysis for function of up- or down-regulated genes
- Microbiome-metabolome correlation analysis for assessment of molecules associated with specific members of a microbial community
- Integration of proteomics and metaproteomics data for multi-omic analysis
The goal of human microbiome research is to understand how human microbiota communicate with their host and factors that influence these interactions in health and disease. An imbalance in human microbiome structure or metabolic activity has been observed in many diseases including inflammatory bowel disease, irritable bowel syndrome, type 2 diabetes, obesity and cancer. Moreover, host genetic variation can influence microbiome composition and provide valuable information on therapeutics response. Integrative analysis of multiple data types (proteins, genes, metabolites, biomarkers, clinical metadata, etc.) can provide high-resolution understanding of mechanisms governing host-microbiome interactions under different conditions.
Who is there?
Sequencing of ribosomal RNA and ITS region is widely used for taxonomic classification and abundance estimation of bacteria, archaea and small eukaryotes in complex microbial communities.
What can they do?
Sequencing of all DNA in a sample allows for genome-wide taxonomic classification and function profiling of all microorganisms in a sample, including bacteria, archaea, eukaryotes, protozoa and viruses.
What are they doing?
Sequencing of all RNA in a sample allows for gene expression abundance estimation and differential gene expression analysis, as well as taxonomic classification (if total RNA is available).
How do the host and microbiome communicate?
Non-targeted metabolite profiling can be used for identification and quantification of host and microbial-derived small molecules such as short chain fatty acids, organic acids, vitamins and lipids. Analysis of host-microbiome exchange of metabolite information can help to explain microbial modulation of host immune response.
Do host genetic variations influence the microbiome?
Single nucleotide polymorphisms (SNPs) measure genetic variation between individuals and can be identified using genotyping chips, whole-genome sequencing or host DNA from shotgun metagenomic data (given sufficient depth). Identifying microbiome correlated host genes can elucidate effects of host genetic variation on microbiome structure & function.