Nanopore basecalling from a perspective of instance segmentation

Yao-zhong Zhang,Seiya Imoto,Georg Tremmel,Arda Akdemir,Satoru Miyano,Rui Yamaguchi,Tetsuo Shibuya

doi:10.1186/s12859-020-3459-0

Yao-zhong Zhang, Seiya Imoto + Show 5 more

Open Access

https://doi.org/10.1186/s12859-020-3459-0

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Abstract

BackgroundNanopore sequencing is a rapidly developing third-generation sequencing technology, which can generate long nucleotide reads of molecules within a portable device in real-time. Through detecting the change of ion currency signals during a DNA/RNA fragment’s pass through a nanopore, genotypes are determined. Currently, the accuracy of nanopore basecalling has a higher error rate than the basecalling of short-read sequencing. Through utilizing deep neural networks, the-state-of-the art nanopore basecallers achieve basecalling accuracy in a range from 85% to 95%.ResultIn this work, we proposed a novel basecalling approach from a perspective of instance segmentation. Different from previous approaches of doing typical sequence labeling, we formulated the basecalling problem as a multi-label segmentation task. Meanwhile, we proposed a refined U-net model which we call UR-net that can model sequential dependencies for a one-dimensional segmentation task. The experiment results show that the proposed basecaller URnano achieves competitive results on the in-species data, compared to the recently proposed CTC-featured basecallers.ConclusionOur results show that formulating the basecalling problem as a one-dimensional segmentation task is a promising approach, which does basecalling and segmentation jointly.

Highlights

Nanopore sequencing is a rapidly developing third-generation sequencing technology, which can generate long nucleotide reads of molecules within a portable device in real-time
Our results show that formulating the basecalling problem as a one-dimensional segmentation task is a promising approach, which does basecalling and segmentation jointly
On the modellevel, based on the basic U-net model [8], we propose an enhanced model called UR-net that is capable of modeling sequential dependencies for a one-dimensional (1D) segmentation task

Summary

Introduction

Nanopore sequencing is a rapidly developing third-generation sequencing technology, which can generate long nucleotide reads of molecules within a portable device in real-time. Through detecting the change of ion currency signals during a DNA/RNA fragment’s pass through a nanopore, genotypes are determined. The accuracy of nanopore basecalling has a higher error rate than the basecalling of short-read sequencing. A nanopore sequencer measures currency changes during the transit of a DNA or an RNA molecule through a nanoscopic pore and Basecalling is usually the initial step to analyze nanopore sequencing signals. A basecaller translates raw signals (referred to as squiggle) into nucleotide sequences and feeds the nucleotide sequences to downstream analysis. It is not a trivial task, as the currency signals are. Nanopore basecalling still has a higher error rate when compared with short-read sequencing. More and more work is focusing on solving challenges to further improve basecalling accuracy

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