Next Generation Sequencing has lead to a rapid expansion of the human genetic pathology 'atlas'. However, the rapidly increasing group of rare genetic disorders remain poorly understood as NGS techniques proves incapable of differentiating polymorphisms from clinically relevant variants. Clinical adoption of these findings now often require highly time-expensive in-vitro experiments. We argue that you could acquire novel insights by employing phenotypical data to infer the functional impact and overall clinical relevancy of these genetic variants [1].
Deep Phenotyping is a comprehensive analysis based on the principle that clinical similarities are indicative of a shared underlying pathopysiology. This technique allows researchers to extrapolate pathophysiological insights from well-known diseases to rare disorders, rendering increased insight in underlying pathophysiology of rare disorders. However, there is no consensus on the best deep phenotyping strategy yet.
One major advantage of Deep Phenotyping is that it utilizes information that is already available: phenotypes. To combat the data scarcity, these phenotypic descriptions can directly be extracted from case studies. Hence, we build a HPO-extraction tool to facilitate deep phenotyping studies.
See manual or GitHub wiki for more information.
