Argonaut: A Web Platform for Collaborative Multi-omic Data Visualization and Exploration - Dain R. Brademan; Ian J. Miller; Nicholas W. Kwiecien; David J. Pagliarini; Michael S. Westphall; Joshua J. Coon; Evgenia Shishkova (Patterns, 2020)
Summary: Researchers now generate large multi-omic datasets using increasingly mature mass spectrometry techniques at an astounding pace, facing new challenges of “Big Data” dissemination, visualization, and exploration. Conveniently, web-based data portals accommodate the complexity of multi-omic experiments and the many experts involved. However, developing these tailored companion resources requires programming expertise and knowledge of web server architecture—a substantial burden for most. Here, we describe Argonaut, a simple, code-free, and user-friendly platform for creating customizable, interactive data-hosting websites. Argonaut carries out real-time statistical analyses of the data, which it organizes into easily sharable projects. Collaborating researchers worldwide can explore the results, visualized through popular plots, and modify them to streamline data interpretation. Increasing the pace and ease of access to multi-omic data, Argonaut aims to propel discovery of new biological insights. We showcase the capabilities of this tool using a published multi-omics dataset on the large mitochondrial protease deletion collection. The Bigger Picture: Modern systems biology experiments profile thousands of biomolecules across many experimental conditions to generate insights about the biological system. While data collection for these experiments can be routine, interpretation of the resultant datasets often requires interlaboratory collaboration of scientists with diverse expertise and is hindered by challenges inherent to sharing and exploring “Big Data.” We have developed Argonaut, a web-based platform purpose-built to accommodate large-scale, multi-omic experiments and to enable intuitive and interactive exploration of the associated data. Argonaut presents the experimental results in an online code-free environment, empowering both experts and non-experts worldwide to easily interact with and share the data. Our platform aims to streamline derivation of impactful experimental conclusions by overcoming the hurdles of working with large datasets and lowering the barrier to entry for biological and clinical collaborators.