Modelling the nanopore sequencing process with Helicase HMMs

TL;DR

This paper introduces the Helicase Hidden Markov Model (HHMM), a new framework that models helicase dynamics in nanopore sequencing to improve data interpretation and basecalling accuracy.

Contribution

The novel HHMM framework integrates helicase motor protein dynamics with nucleotide sequences, enhancing analysis of ion currents in high-resolution nanopore sequencing.

Findings

HHMM effectively visualizes raw ion currents and nucleotide alignment.

HHMM serves as a foundation for developing advanced basecalling algorithms.

The approach supports analysis of millions of states, improving understanding of sequencing data.

Abstract

Recent advancements in nanopore sequencing technology, particularly the R10 nanopore from Oxford Nanopore Technology, have necessitated the development of improved data processing methods to utilize their potential for more than 9-mer resolution fully. The processing of the ion currents predominantly utilizes neural network-based methods known for their high basecalling accuracy but face developmental bottlenecks at higher resolutions. In light of this, we introduce the Helicase Hidden Markov Model (HHMM), a novel framework designed to incorporate the dynamics of the helicase motor protein alongside the nucleotide sequence during nanopore sequencing. This model supports the analysis of millions of distinct states, enhancing our understanding of raw ion currents and their alignment with nucleotide sequences. Our findings demonstrate the utility of HHMM not only as a potent visualization tool but also as an effective base for developing advanced basecalling algorithms. This approach offers a promising avenue for leveraging the full capabilities of emerging high-resolution nanopore sequencing technologies.