Evaluation of an automated genome interpretation model for rare disease routinely used in a clinical genetic laboratory.

PURPOSE: The analysis of exome and genome sequencing data for the diagnosis of rare diseases is challenging and time-consuming. In this study, we evaluated an artificial intelligence model, based on machine learning for automating variant prioritization for diagnosing rare genetic diseases in the Baylor Genetics clinical laboratory. METHODS: The automated analysis model was developed using a supervised learning approach based on thousands of manually curated variants. The model was evaluated on 2 cohorts. The model accuracy was determined using a retrospective cohort comprising 180 randomly selected exome cases (57 singletons, 123 trios); all of which were previously diagnosed and solved through manual interpretation. Diagnostic yield with the modified workflow was estimated using a prospective "production" cohort of 334 consecutive clinical cases. RESULTS: The model accurately pinpointed all manually reported variants as candidates. The reported variants were ranked in top 10 candidate variants in 98.4% (121/123) of trio cases, in 93.0% (53/57) of single proband cases, and 96.7% (174/180) of all cases. The accuracy of the model was reduced in some cases because of incomplete variant calling (eg, copy number variants) or incomplete phenotypic description. CONCLUSION: The automated model for case analysis assists clinical genetic laboratories in prioritizing candidate variants effectively. The use of such technology may facilitate the interpretation of genomic data for a large number of patients in the era of precision medicine.

A new approach towards the Debye length challenge for specific and label-free biological sensing based on field-effect transistors.

Biologically-modified field-effect transistors (BioFETs) are promising platforms for specific and label-free biosensing due to their sub-micron footprint suitable for multiplexing in ultra-small samples, low noise levels, inherent amplification, etc. Debye screening length is a well-recognized challenge for any BioFET-based technology. The screening length is the smallest at the double layer, where the solution ion population is higher than the bulk population. One way to address the small double layer screening length is to electrostatically modify the potential drop across the solution such as to minimize the potential drop over the double layer. This will decrease the population of the double layer ions and increase the screening length. However, this is not possible with BioFETs as voltage application to the reference electrode simultaneously affects both the double layer and the BioFET conducting channel. The current study addresses the screening length challenge with the novel Meta-Nano-Channel (MNC) BioFET. The MNC BioFET, which is fabricated in a complementary-metal-oxide-silicon (CMOS) process, allows decoupling of the electrostatics of the double layer from the electrodynamics of the conducting channel. The study explores the mechanism of sensing with the MNC BioFET, and demonstrates how the double layer can be electrostatically tuned in order to optimize the screening length without affecting the conducting channel. Finally, specific and label-free sensing of 10 ng ml-1 prostate specific antigen (PSA) is demonstrated. It is shown that by electrostatically increasing the double layer screening length, the sensing signal increases from 70 mV to 133 mV.