The COMMONS Lab research is focused on the development of computational solutions for natural product research. Particular emphasis is placed on the development of tools for the organization, annotation, visualization and interpretation of mass spectrometry data in order to efficiently identify metabolites in complex biological matrices. These results are then used to answer questions in a wide range of research topics spanning from drug discovery to chemical ecology. We are committed to improving knowledge sharing in natural products research and strive to follow the ideals of Open Science.
The commons is the cultural and natural resources accessible to all members of a society, including natural materials such as air, water, and a habitable earth. These resources are held in common, not owned privately. Commons can also be understood as natural resources that groups of people (communities, user groups) manage for individual and collective benefit. Characteristically, this involves a variety of informal norms and values (social practice) employed for a governance mechanism. Commons can be also defined as a social practice of governing a resource not by state or market but by a community of users that self-governs the resource through institutions that it creates. Commons in Wikipedia
We found COMMONS to be a well suited acronym for COmputational Mass spectroMetry & Open Natural products reSearch. The commons definition is aligned with our vision of Open Science and our strong interest in exploring new ways to create and share natural products research knowledge.
In order to study the chemicals present in living organisms (plants, fungi, animals or bacteria) we use a set of tools and techniques aiming to break these into very small parts.
Most of the tools we develop and work on in the COMMONS Lab aim to link and put back together the things we previously broke (see previous section). Just like a big puzzle . Hopefully, during this process we also learn some interesting things on our way !
Putting things together, finding patterns and making links is how humans have learned to read Nature and more specifically how they have encountered natural products of interest historically. It is for example the typical knowledge acquisition scheme in most of the traditionnal medicine systems. In the last century, we have been exploiting reductionist approaches (see above section for example). These approaches are very powerfull but have their limits. See this paper (Pharmacognosy in the digital era: shifting to contextualized metabolomics) for our views on knowledge acquisition strategies in pharmacognosy.
Central among the goals of the COMMONS Lab is the development of novel computational strategies to efficiently organize, annotate and visualize mass spectrometric data. For this, we need to establish links. Links between spectra. Links between spectra and structures. Links between spectra, structures and bioactivities. Links between structures and their biological sources. Links ... everywhere. Putting things back into context. We believe that this is fundamental to harness the power of reductionist approaches. Their is still much to be done in the corner D of the previous figure. This is were we are currently putting our efforts.
Here are some examples of such tools and approaches :
molecular networking (which could in fact also be called spectral networking). By comparing spectra acquired during non-targeted mass spectrometry analyses, this strategy allows to build networks (or graphs) of spectrally related analytes. As the spectra reflect the structures of the analytes, it is in fact families of (potentially) structurally related analytes that are established. This can be done without the need for any metabolite annotation strategy. This is an extremely powerful approach to organize the complex chemistry of natural extracts. Please, refer to the seminal paper Sharing and community curation of mass spectrometry data with Global Natural Products Social Molecular Networking and the GNPS Platform for more details.
Most of the COMMONS Lab research outputs and material (slides used for teaching, presentation etc.) are available at the following Zenodo Community repository https://zenodo.org/communities/commons-lab-repository
Outputs specifically related to the LOTUS Initiative are made available here https://zenodo.org/communities/the-lotus-initiative/
Course material is available here