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Biology has fully embraced big-data, high-throughput approaches, and data workflows (Figure below) have become indispensable for processing, analyzing, understanding, and enabling new discoveries. These workflows begin with the interpretation of raw signals from laboratory instruments, such as DNA sequencers and mass spectrometers, and proceed through multiple steps of aggregation and filtering. This preprocessing stage transforms complex data into a biologically interpretable form, such as mutations statuses or levels of gene transcripts and proteins, and sets the stage for modeling and analysis. The data modeling and analysis stage uses various data science approaches to generate new insights into functioning of living organisms or finding drivers of disease that could potentially turn into new drugs, drug targets, or clinical biomarkers.