When a project is done, when the experiments are all run, and when the data is lined up in a row, only one thing remains: the data analysis.
Today, we recognize that there is no one clear path of treatment and that, from cancer to Covid-19, precision medicine tailored to each individuals' medical state and history yields the best results. Of course, that requires data on how everything going on in a person could affect how they respond to the disease and the treatment. A lot of data. That's where multi-omics analysis steps in.
When dealing with high throughput data, though, running a multi-omics analysis is complicated and takes a lot of time. For the average researcher, without a degree in statistics and a deep understanding of data science, bioinformatics, and even computer programming, these sorts of studies can be out of reach. Thus, so are the molecular profiles much needed to develop computer aided diagnosis, individual predictions of drug response, and other technologies seen as the next step in advancing modern medicine into the future.
One option here would be to hire a third party bioinformatic group, like A2IDEA. Our team is focused solely on these analysis techniques and has access to large databases and computer modeling systems that can provide in-depth information on whatever our clients' study is, quickly, allowing our clients to focus not on the steep learning curve of bioinformatics but on using their expertise in their field to implement the results of their data in order to make the world a better place.
The other option here would be for researchers to learn how to run a multi-omics analysis themselves. We don't recommend that for large projects, solely because it's not something most researchers are trained to do, let alone efficiently and accurately. But for the sake of transparency, and in the hopes of increasing accessibility of the cutting edge technology of bioinformatics, here's how we (and other experts) would go about a multi-omics analysis.
The easiest and most user friendly way to run this would be through web-based multi-omics integration softwares, and to follow an already designed protocol. There is debate on what the best protocol uses, but many have the following three steps.
1: Single-omics data analysis. This is where the focus is on each individual -omics that was researched, one at a time. The Analyst software suite, a collection of free, web-based integration and -omics analysis softwares designed for user accessibility, has two good programs for this: ExpressAnalyst and MetaboAnalyst.
2: Knowledge driven integration. This is looking at the data collected and comparing it to what we already know about how various proteins, genes, amino acids, etc work together. This is when access to large databases and programs that can handle large databases comes in clutch, and one of the industry leaders is OmicsNet.
3: Data driven integration. The final step is to look at all the data together, but on its own from the existing literature. This is often augmented with computer driven visualization softwares, and another Analyst suite product good for this is the OmicsAnalyst software.
All in all, multi-omics is a complex and challenging field, but a promising one. In the future we can look to a standard of medicine tailored to each individual thanks to the high-throughput data analysis multi-omics opens the door to. For more information on bioinformatics tools, see A2IDEA's other articles, such as:
Or contact us directly to get a data analysis of your own.