"Omics" technologies (genomics, transcriptomics, proteomics, metabolomics, etc.) have significantly improved our understanding of biological systems. They have become standard tools in biological research, for example, identifying and unraveling transcriptional networks, building predictive models, and discovering candidate biomarkers. The rapid increase of omics data presents both a challenge and great potential when it comes to providing valuable insights into the underlying patterns of the investigated biological processes. The challenge is extracting, processing, integrating, and interpreting the corresponding datasets. The potential, on the other hand, arises from generation of verifiable hypotheses to understand molecular mechanisms behind biological processes, for example, gene expression patterns. Exploratory data analysis techniques are used to get a first impression of the important characteristics of a dataset and to reveal its underlying structure. However, investigators are often faced with the difficulties of managing the high-dimensional nature of the data. In order to efficiently analyze biological data and to gain a deeper understanding of underlying biological mechanisms, it is essential to have robust and interactive data visualization tools.
Keywords: Bokeh; Data exploration; Interactive visualization; Jupyter notebook; Magnaporthe oryzae; Panel framework; PaperBLAST; Python; RNA-seq analysis; Transcriptomics.