Current genetic diagnosis by next-generation sequencing requires a large investment of resources and offers little point-of-care portability. Furthermore, it is unable to detect many types of genetic variations ­– including large deletions, duplications, and balanced translocations ­– that are relevant to human diseases and health.
Comparing to other sequencing technologies, Nanopore sequencing owns the advantages of point-of-care (i.e., sequencing anywhere anytime), long reads (i.e., assembly-free to detect various genetic variations), and PCR free (i.e., sample preparation is easy). However, its application is severely limited by a number of challenges, including low base-calling accuracy, lack of training data for AI-based methods, and computational burden on reads mapping.
In this talk, I will first give an overview of the research activities in Structural and Functional Bioinformatics Group (https://cemse.kaust.edu.sa/sfb). I will then focus on our efforts on developing computational methods to tackle key open problems in Nanopore sequencing. In particular, I will introduce our recent works on developing a collection of computational methods to decode raw electrical current signal sequences into DNA sequences, to simulate raw signals of Nanopore, and to efficiently and accurately align electrical current signal sequences with DNA sequences. I will further introduce their applications in biomedicine and healthcare.
 

测序，纳米孔