Machine Learning Routines for Nanopore Sensing

Nanopore technology is being successfully deployed for the sequencing of nucleic acids however it is still challenging to analyse heterogenous biomolecular samples. The analysis of datasets generated from nanopore sensors relies on a signal processing chain that employs advanced algorithms to classify translocation events to specific analytes passing through the nanopore. Often, the signatures of complex analyte mixtures are too convoluted to be analysed with high precision with current data analysis protocols. This project will develop of a fit-for-purpose data analytics approach to enable high-precision real-time analysis of highly convoluted nanopore datasets. While some features can readily be quantified using analytical tools (such as peak amplitude and dwell time), others (such as shape) are more challenging and will be better suited for analysis with machine learning approaches.
The project will implement machine learning approaches for the clustering and classification of single molecule biosensing datasets generated using nanopores and functional DNA origami to support the development of the next generation of medical diagnostic devices.
The project will also involve the development of multimodal characterization of catalytic nanoparticle systems where nanopore sensing will be complemented with electrochemical characterization at the single entity level. The project will implement sensor fusion algorithms to generate improved signal classification that will allow the development and characterization of new materials for a low-carbon future.