  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.
