Abstract

Nanopore sequencing by Oxford Nanopore Technologies is a long-read sequencing method that has considerable advantages for characterising RNA isoforms. It works by recording changes in electrical current when a DNA or RNA molecule traverses through a pore. However, basecalling of this raw signal (known as a squiggle) is error prone, making it challenging to accurately identifying splice junctions. Existing strategies include using matched short-read data and/or annotated splice junctions to correct splice junctions from mapped nanopore reads, but add expense or limit junctions to known (incomplete) annotations. Therefore, a method that could accurately identify splice junctions solely from nanopore data would have numerous advantages.

In this talk, I will present a method ``NanoSplicer’’ that exploits the information in raw nanopore signals (squiggles) to improve splice junction identification. The key idea is to identify, for each splice junction, which of the squiggles predicted from potential splice junction sequences best matches the observed junction squiggle. This enables NanoSplicer to identify splice junctions solely from the nanopore data and its performance to be independent of other reads or read depth, having the potential to better identify rare splice junctions. Using both synthetic and biological data, we demonstrate that NanoSplicer improves splice junction identification, especially when the basecalling error rate near the splice junction is elevated.

This is a joint work with Yupei You and Michael Clark at the University of Melbourne.