Nanopore basecalling from a perspective of instance segmentation.
BMC Bioinformatics
; 21(Suppl 3): 136, 2020 Apr 23.
Article
em En
| MEDLINE
| ID: mdl-32321433
ABSTRACT
BACKGROUND:
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. 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%.RESULT:
In 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.CONCLUSION:
Our results show that formulating the basecalling problem as a one-dimensional segmentation task is a promising approach, which does basecalling and segmentation jointly.Palavras-chave
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Base de dados:
MEDLINE
Assunto principal:
Sequenciamento por Nanoporos
Idioma:
En
Ano de publicação:
2020
Tipo de documento:
Article