Microbes are central to the processes BaseClear’s clients work with. Often in these processes it is not a single species or strain that does the job, but rather a community of microorganisms that coexist in an environment.
These populations play crucial roles in industrial applications like dairy production, probiotics development, cosmetics, and enzyme discovery. In addition, the relationship between the human microbiome and health is being explored by an increasing number of projects. For instance, the skin microbiome can be studied as an outcome measure in clinical trials of dermatological drugs, as discussed in our recent interview with Robert Rissmann, Research Director of Dermatology at CHDR, and Tom van den Bogert, Product Manager Metagenomics at BaseClear.
Innovation and product optimisation in these community-driven worlds rely heavily on well-conducted microbiome research, where appropriate data analysis is vital for the success of the study. Knowing this, BaseClear offers an extensive microbiome analysis portfolio that includes not only study design, sample preparation and sequencing, but also downstream bioinformatic and biostatistical analyses. For this reason, we were recently invited by Frontiers in Genetics to participate in the special research topic Computational Methods for Microbiome Analysis. The result is a recently published perspective article entitled On the Role of Bioinformatics and Data Science in Industrial Microbiome Applications (van den Bogert et al., 2019).
Regarding the team who wrote the article, “Getting Jos Boekhorst from NIZO on board was a logical first step. He’s a very experienced scientist in microbiome analyses”, explains Ali May, Prod. Man. Bioinformatics at BaseClear, who co-authored the article. He adds:
“It was a good moment for us to collectively think about what we learned from all the microbiome projects we’ve been involved in so far. We wanted to provide the reader with a short and accessible text with good leads and insight. Also, we included our outlook on the impact of current and upcoming developments on the microbiome world.”
In the article, the authors first provide an overview of commercial areas in which microbial communities play crucial roles. For each of these, they describe the most important bioinformatic and data science-related requirements and challenges, such as strain-level community characterisation in probiotic screening and determining the strain-specific effect during clinical trials. Next, they summarise several computational advances that help these application areas progress, for instance those in metagenome analyses and machine learning. The authors point out: “Technological advances like those in long-read sequencing are certainly exciting and will have a big impact, but the biggest contribution today seems to be from the growing databases and new data analysis methods that allow answering previously difficult questions.”
Indeed, in a recent review in Microbiome, the Human Microbiome Portfolio Analysis Team of the US National Institutes of Health (NIH) revealed that a staggering 41% of the $188M NIH support on the development of any method, tool, or product in the Human Microbiome Project was spent on projects that focused on the development of new databases, computational methods and statistical tools. The development of experimental tools came second with 29% of the $188M.
Figure 1: Computational and statistical tools are the most expensive microbiome-related technology to develop. The figure depicts the technology development in the microbiome projects between 2012–2016. The three main technology categories of computational/statistical tools, experimental tools and products/devices developed in the microbiome projects (taken from NIH Human Microbiome Portfolio Analysis Team, 2019)
One thing that is clear is that the young microbiome field has a long way to go. The recent article shows that BaseClear is determined to stay at the forefront of this field by adopting the newest advances to meet the ever-increasing need for expertise in microbiome studies.
- van den Bogert, Bartholomeus, et al. “On the role of bioinformatics and data science in industrial microbiome applications.” Frontiers in Genetics 10 (2019): 721. https://doi.org/10.3389/fgene.2019.00721
- NIH Human Microbiome Portfolio Analysis Team. “A review of 10 years of human microbiome research activities at the US National Institutes of Health, Fiscal Years 2007-2016.” Microbiome 7 (2019): 1-19.