Multi-omics data integration

Multi-omics microbiome analysis is transforming our understanding of human health and disease. We help you decode the genetic and metabolic fingerprints of our microbial inhabitants, providing insights into the intricate relationship between the microbiome and human physiology.

The most powerful studies are designed to integrate data from various multi-omics platforms such as genomics, transcriptomics, proteomics, epigenomics, and metabolomics to answer questions about human health. 

We offer high quality metagenomics and metabolomics data generation as part of our services and can integrate additional omics datasets to give you insights about microbiome-mediated mechanisms contributing to different states of health and disease. We have many years of expertise in multi-omics data integration to help you navigate the data complexity and connect the dots between different types of omics-data and clinical data – helping you go beyond correlation to appropriately combining data that converge to a consistent story and biological understanding. 

Combine the power of metabolomics and metagenomics  

Clinical Microbiomics has a proven track record of gaining new insights through integration of microbiome and metabolome data, with lead authors on the first scientific paper that presented a sophisticated integration of metabolome, microbiome, and clinical data,¹ and more recently a comprehensive survey of human plasma metabolite signatures of gut microbiome composition based on 8,500 individuals.² 

Looking for integrative systems biology solutions that help you link metabolomics, metagenomics, and clinical information? Contact us to discuss how our expertise can help. 

References

¹ Pedersen, H., Gudmundsdottir, V., Nielsen, H. et al. Human gut microbes impact host serum metabolome and insulin sensitivity. Nature 535, 376–381 (2016). https://doi.org/10.1038/nature18646

² Dekkers, K.F., Sayols-Baixeras, S., Baldanzi, G. et al. An online atlas of human plasma metabolite signatures of gut microbiome composition. Nature Communications 13, 5370 (2022). https://doi.org/10.1038/s41467-022-33050-0

Pedersen, H.K., Forslund, S.K., Gudmundsdottir, V. et al. A computational framework to integrate high-throughput ‘-omics’ datasets for the identification of potential mechanistic links. Nature Protocols 13, 2781–2800 (2018). https://doi.org/10.1038/s41596-018-0064-z