Our products are built to take researchers from raw data to biological insights quickly and efficiently. We design for reproducibility, ease-of-use, and speed. We collaborate with leading academic and industrial labs in the fields of metabolomics, genomics, transcriptomics, and integrated omics to bring the latest techniques to our partners.
PollyTM is a one-stop data analysis platform designed to accelerate the drug discovery process. PollyTM can host multiple applications that can be combined by users to create their own analytical workflows. A few clicks are all that are required to perform end-to-end computations and achieve a deeper understanding of the experimental data.
PollyTM currently hosts applications for 13C metabolic labeling data analysis and visualization for LC-MS and LC-MS/MS workflows. As PollyTM grows, we envision adding applications for other computation-heavy areas of drug discovery and enable integrated omics analyses. The platform will soon allow users to host their own applications as well.
El-MAVEN is an LC-MS data processing engine for large-scale metabolomic experiments. It allows automated processing of raw LC-MS data from unlabeled and labeled, targeted and untargeted metabolomic profiling experiments. It is vendor-neutral, open-source and has an interface built for ease of processing and visualizing hundreds of samples.
The project was originally developed as MAVEN at the Rabinowitz Lab at Princeton University. Over the last year, Elucidata has revamped the software architecture of the tool to make it capable of handling large datasets - tens of GBs (thousands of samples). In the process, we have also made it faster and more robust.
We continue to actively develop the application with support from our collaborators. Elucidata offers support for deployment, maintenance and training users. We also develop customized features for El-MAVEN for our partners to accommodate special requirements.
Drug Hunter is an interactive web-based application that enables scientists to create a virtual project plan to deliver a clinical candidate. Using a drug discovery simulation exercise, teams adjust their lead optimization plans as they get real-time warnings. Each team has the freedom to structure their testing paradigm by defining a key hypothesis and critical questions. At the end of the simulation, Drug Hunter provides an evaluation of each plan using metrics like cost, time, and probability of success. The tool teaches scientists how their decisions influence the efficiency and success of their projects. Drug Hunter helps illustrate that lean thinking is as critical to a team’s success as the underlying science