What can you do with two omes that you can’t do with one?
A dive into systems biology propelled by new analytical approaches
Credit: Molecular & Cellular Proteomics
What can you learn from two omes that you can’t tell from one? You might determine how different bacterial strains in a water sample contribute specific functions to its overall microbiome. You might find that duplication of a section of a chromosome in cancer cells has wide-reaching effects on important proteins–or that it has a smaller effect than expected. First, though, you need to find a way to wrangle gigabytes of data saved in numerous, perhaps incompatible formats.
As high-throughput analytical tools improve, allowing researchers to collect more and more data, the challenge becomes how to interpret it all. When transcriptomic, genomic, metabolomic and proteomic analyses are layered together, parsing out a signal can be a monumental task. The field needs new analytical strategies, and new user-friendly software, to condense RNA counts, genotypes, and mass spectra showing proteins, post-translational modifications, complex carbohydrates and metabolites into coherent, interpretable results.
Researchers surfing this multiomics wave report a plethora of new tools and approaches this month in a special issue of the journal Molecular & Cellular Proteomics devoted to integrating multiple omics. The issue, edited by Bernhard Kuster of the Technical University of Munich and Bing Zhang of the Baylor College of Medicine, includes sixteen articles that explore ways to combine data from two or more omes at a time.
For readers unfamiliar with multiomics, a review by Vitrinel et al. covers different ways that proteomics data and other types of data can be layered and the biological questions one might answer using these approaches.
Here are some highlights from the special issue:
- Ma et al. investigated how copy-number variations, common in cancer, affect the cellular phenotypes through protein and phosphoprotein abundance. They discovered new genome regions that, when altered, affect the abundance of important cancer-associated proteins not encoded in those regions.
- Zhan et al. combined multiomics with clinical outcomes and pathology-lab images to yield histopathological markers, such as cell density or size, that might be prognostic in breast cancer biopsies — a boon, since images of biopsies can be taken at many clinics, while omics approaches are less widely available.
- Easterly et al. present software that allows quantitative comparison between conditions in metaproteomic studies, which measure all of the proteins in a microbial community. The tool also lets researchers ask how different bacterial groups contribute to functions of the microbial community.
- Federspiel et al. used proteomic and transcriptomic datasets to validate and narrow down important proteins in the interactome of a Huntington’s disease-associated protein.
Related Journal Article