While interest in microbiome therapeutics is growing rapidly, it is still a relatively new space of research and study, and there are multiple issues that need to be addressed in this space including data collection and sequencing. Data collection issues include variability as well as complexity of the data. Indeed, one of the biggest challenges when dealing with the microbiome is that it is not easy to extract significant data samples. Often, we are not dealing with a singular fungible unit, but rather a collection of organisms, or sometimes even a whole ecosystem.
This poses quite a challenge when using traditional sampling processes, as extracting large representative samples is often unfeasible, if not impossible. Furthermore, when the microbiome is studied in a specific point in time, it reduces our understanding of its variability. As such, scientists are often limited to studying individual microbiotic species, in a single point and time, rather than studying the interaction between multiple species and the human system over a prolonged period.
This also means that some data sets that we collect in one area of the body is not necessarily significant for other parts of the body. For example, some microbes might have a specific role in one part of our body, and a different one in another part.
Furthermore, while the technologies we have are able to detect and identify specific species, they often have issues when trying to identify different strains of the same species. This is especially troublesome, as some of these strains might have a different impact on our health.
In an effort to avoid limited data sets, some might be tempted to collect large, all-encompassing data sets. This leads to another relevant issue, the one relative to data analysis and quality of data. Overall, this means that, using our current existing technologies, it is quite challenging to monitor, catalog and identify individual members of a specific microbiome, as well as understanding how microbiota communities interact and influence their host-pathogen over prolonged period of times. Limited data sets create a very narrow window of understanding while larger data sets are often impossible to analyze. As such, some companies are hoping that artificial intelligence might be able to assist in data analytics of the more complex data sets. Eagle Genomics is one such company. They are developing IT platform solutions for the microbiomics space. They have even partnered with Microsoft Genomics in the hope of being able to scale they current products and solutions.
