As the field of Proteomics matures and becomes more accessible to a broader scientific community, it is essential to ensure the reproducibility, quality and integrity of data analysis and its interpretation. The complexity of the data and the capabilities of a research team to adopt complex analysis pipelines have proven to be an obstacle to effective collaboration and more efficient biological insight generation.
Here, we introduce MD 2.0, a cloud- and web-based platform for quantitative proteomics data, which implements a novel analysis workspace where raw data processing, statistical analyses, visualizations, and external knowledge generation are integrated into a modular fashion. This modularity enables researchers the flexibility to test different hypotheses and to customize and template complex proteomics analysis. This ability, coupled with a human-centered interface design, reduces the barrier to proteomics data analysis without compromising quality and depth and expedites insight generation for complex datasets. The extensible MD 2.0 environment has been built with a scalable architecture to allow rapid development of future analysis modules as well as enhanced tools for remote collaboration.
The new drag-and-drop modules allow a user to easily and quickly assess different aspects of an experiment, including quality control, differential expression and enrichment analysis. The modularity of MD 2.0 lays the foundation to support broader community-based template generation and optimized collaboration between proteomics experts and biologists, thereby accelerating research teams’ abilities to extract knowledge from complex proteomics datasets. Outcomes from case studies on several published proteomics analyses of cancer cohorts will be presented from both independent and combined analyses to highlight MD2.0’s ability to facilitate insight generation.