User Story
Yue Ren began his academic journey as an undergraduate majoring in general biology at Purdue University. Known for its strong emphasis on structural biology, Purdue provided the ideal environment for his studies.
As an undergraduate research associate, Yue joined a structural biology group. During this time, he acquired proficiency in Python programming.
His role as a computational biologist predominantly involved data analysis, and he often identified intriguing patterns and collaborated with his laboratory-based colleagues to conduct confirmatory tests.
As a graduate student, Yue worked in a bioinformatics lab researching the evolution of the human immune system before accepting a postdoc position with a major pharmaceutical company.
As a result of the shutdowns during the pandemic, he shifted more to computational work. At the end of the COVID pandemic, he moved to a smaller biotech company where he was involved in advancing pipelines from preclinical studies to phase two clinical trials. After merging companies, he is now back at a large pharmaceutical company.
Challenges
Communicating complex data across multidisciplinary teams
Dealing with the shift in mindset when moving from a big pharmaceutical company to a smaller biotech company. In big pharma, extensive resources and larger teams allow for in-depth research and analysis. In smaller biotechs, however, computational biologists are expected to work with different teams, from discovery to clinical, and communicate complex data in an understandable way.
Dealing with bottlenecks in the analysis process due to the complexity of omics data
Dealing with diverse omics data, particularly transcriptomics, which varies from cell lines to in vivo experiments and even patient data, and making sense of this data and translating it into meaningful insights. The more diverse the data, the harder it becomes to extract valuable information, leading to bottlenecks in the analysis process.
Communication gap between computational biologists and biologists.
The need to convey complex data to non-specialists and help them ask meaningful questions was a time- consuming process. Traditional tools required generating numerous slides for presentations, and this often led to delays in the research process.
Results with Omics Playground
Enhanced communication
Omics Playground improved team communication and understanding, leading to more meaningful discussions.
Increased data exploration
The platform empowered biologists to explore data interactively, resulting in better insights and higher-quality questions during meetings.
Reduction of time and workload
Omics Playground is intuitive and comprehensive, allowing for less time spent on data preparation and visualization, resulting in more efficient workflows.
