Abstract: Disclosed herein are classifier models, computer implemented systems, machine learning systems and methods thereof for classifying clinical variants of unknown or uncertain significance into a pathogenicity category using measured phenotype features extracted from phenotype assays of transgenic organism expressing the human clinical variant. Embodiments of the present invention relate generally to methods for generating classifier models using machine learning and use of those classifier models to predict the pathogenicity of a clinical variant for a specific human disease (e.g. genetic disease), assigning a patient clinical variant to a pathogenicity category (e.g. pathogenic or benign) for the specific human disease to determine whether that patient should be followed up with additional, more invasive diagnostic testing, or treatment.

Abstract: The present disclosure provides homologous recombination reporter nucleic acid construct reagents for increasing the likelihood of detecting successful modification of a specific sequence in chromosomal DNA of a host cell via homologous recombination. The homologous recombination reporter constructs contain a sequence element inserted within the coding sequence for a reporter gene resulting in a mutated reporter gene. The sequence element is removed via homologous recombination based on the presence of two homology regions present in the reporter construct.

Abstract: The present disclosure provides methods and systems for performing, observing, and/or recording defecation motor program (DMP) events using microfluidic devices. The methods may be performed wherein the nematodes ingest fluorescent or color material and are then loaded in a microfluidic chip and stimulated to feed and defecate. DMP events are observed with use of a fluorescent microscope. In other methods, a microfluidic device with two or more electrodes is used to record electrical events of the DMP.