Precise Nanopore Signal Modeling Improves Unsupervised Single-Molecule Methylation Detection

Abstract

AbstractBase calling in nanopore sequencing is a difficult and computationally intensive problem, typically resulting in high error rates. In many applications of nanopore sequencing, analysis of raw signal is a viable alternative. Dynamic time warping (DTW) is an important building block for raw signal analysis. In this paper, we propose several improvements to DTW class of algorithms to better account for specifics of nanopore signal modeling. We have implemented these improvements in a new signal-to-reference alignment tool Nadavca. We demonstrate that Nadavca alignments improve unsupervised methylation detection over Tombo. We also demonstrate that by providing additional information about the discriminative power of positions in the signal, an otherwise unsupervised method can approach the accuracy of supervised models.Availability and implementationNadavca is available under MIT license athttps://github.com/fmfi-compbio/nadavca. Nanopore sequencing data sets are available from ENA bioproject PRJEB64246.Jaminaea angkorensisreference genome assembly is available from Zenodohttps://doi.org/10.5281/zenodo.8145315.