The Piccolo lab's overarching goal is to make discoveries that transform our understanding of biology and human health—discoveries that may only be realized with the aid of advanced computational approaches.
Recent biotechnological advances have enabled researchers to profile organisms, tissues, and cells at an unprecedented scale. For example, high-throughput molecular profiling has made it possible to identify DNA variation across entire genomes and to quantify the presence of RNA transcripts, proteins, metabolites, and other types of molecule. These data have incredible potential to shed light on basic biological processes and disease mechanisms. However, to make best use of such large and complex data sets, an interdisciplinary approach is crucial. Accordingly, the Piccolo lab integrates knowledge and techniques across biology, computer science, medicine, and statistics.
Most biomedical phenomena are driven by combinations of factors that may each induce subtle effects. Accordingly, we uses computational methods that attempt to account for this complexity. We also seek to aggregate evidence across multiple types of input data. This research falls within the realm of "dry lab biology,'' which takes advantage of massive, publicly available databases to make fundamental scientific discoveries (more here). Vast troves of data exist; our goal is to integrate and mine these resources and make connections that complement wet-lab and clinical research.