BioDati: Advancing the Way Biologists Compute, Share and Reuse Knowledge
BioDati’s mission is to advance research by making biological knowledge Shareable, Reusable and Computable. Our platform allows all your research to be easily combined with prior knowledge and made usable by biologists and researchers to search, build networks, generate models, and conduct analysis.
As scientists, we build and refer to networks in order to understand biological interactions. Context is extremely important. By context, I mean: In what species, tissue, and cell type was the relationship observed? What methods were used? There are, of course, differences between fruit flies, yeast, worms, zebrafish, mice, and men. We know that all proteins are not expressed in all cell types, and all cell types do not respond identically to drugs or biological challenges. For instance, in response to the GLP-1 analogue, exendin-4, pancreatic alpha cells undergo cell cycle arrest, but pancreatic beta cells proliferate.
At BioDati, as nanopubs are created, provenance and context are captured for every relationship. That allows you to build your own Networks, filtered as desired, by annotations for species, tissue, cell type, and even experimental methods. These networks can seamlessly combine metabolic, transcriptional, proteomic, chemical, and clinical data, so you can identify a footprint for the disease or genetic change that you are interested in. Comparing footprints for different diseases may suggest drugs to be repurposed, or help explain the observed dysfunctions.
Bulk loading allows you to add tens of thousands of assertions (causal relationships) to a single nanopub at a time, allowing you to automate the loading of your big data. This also allows you to compare results between cell types, drug treatments, diseases, or other biological challenges more easily.
Effective model building often requires combining observations from multiple sources. We require a standardized, open-source language, BEL, so you can do just that. You can compare your data against the BioDati/Selventa knowledge base. You can also collaborate easily with other labs. We can orthologize observations, say from mouse to human, to allow you to predict an observation in another species. We use standardized names to ensure that everyone is discussing the same biological entity. For instance, tumor necrosis factor is variously known as TNF, DIF, TNF-alpha, TNFA, TNFSF2, TNF-α,TNLG1F, and Tumor necrosis factor alpha. Names for genes and proteins can change. For instance, the name for the gene MARCH11 was recently changed to MARCHF11. We track those changes and update the database, so you don’t have to.
The networks are shareable, extendable, and reusable. Since biological knowledge changes and needs change, we have designed our platform to allow you to quickly and easily update your Networks. Because we use an open-source format, you can use our analytics, as well as your own custom analytics on BioDati content.
So- how can you use those relationships and networks?
You can build a target pathway- using your own proprietary data as well as data from public sources.
You can add Networks/pathways together, to get a better understanding of possible downstream consequences.
You can look for the intersection of Networks, or perturbations in protein, RNA, and/or metabolites to find a common thread or footprint between diseases or drugs.
You can identify preclinical models for use in drug screening, that may be translatable for the specifically targeted pathway.
You can identify dysregulated signaling, which can be expanded to identify targets, and screen for drugs that may target that dysregulation.
Given a phenotype change (RNAseq, metabolite measurements…), you can identify the possible mode of action for a new drug.
