Omics-profiling is a collection of increasingly prominent approaches that result in large-scale biological datasets, for instance capturing an organism’s behavior and response in an environment. It can be daunting to manually analyze and interpret such large datasets without some programming experience. Additionally, with increasing amounts of data, management, storage and sharing challenges arise.
Boston College researchers present ShinyOmics, a web-based application that allows rapid collaborative exploration of omics-data. By using Tn-Seq, RNA-Seq, microarray and proteomics datasets from two human pathogens, they exemplify several conclusions that can be drawn from a rich dataset. They identify a protease and several chaperone proteins upregulated under aminoglycoside stress, show that antibiotics with the same mechanism of action trigger similar transcriptomic responses, point out the dissimilarity in different omics-profiles, and overlay the transcriptional response on a metabolic network.
ShinyOmics is easy to set up and customize, and can utilize user supplied metadata. It offers several visualization and comparison options that are designed to assist in novel hypothesis generation, as well as data management, online sharing and exploration. Moreover, ShinyOmics can be used as an interactive supplement accompanying research articles or presentations.
Comparison of all experiments from the same strain
The heatmap shows DE of all experiments included in the experiment sheet for a specific strain (T4: TIGR4). The dendrogram on the heatmap and the PCA (colored by antibiotic) shows that the RNA synthesis inhibitor rifampicin (RIF) is most dissimilar to other antibiotics. AB: antibiotic. KAN: Kanamycin. LVX: Levofloxacin. VNC: Vancomycin. PEN: Penicillin
Availability = Project home page: https://github.com/dsurujon/ShinyOmics