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DeepRepeat: direct quantification of short tandem repeats on signal data from nanopore sequencing.


ABSTRACT: Despite recent improvements in basecalling accuracy, nanopore sequencing still has higher error rates on short-tandem repeats (STRs). Instead of using basecalled reads, we developed DeepRepeat which converts ionic current signals into red-green-blue channels, thus transforming the repeat detection problem into an image recognition problem. DeepRepeat identifies and accurately quantifies telomeric repeats in the CHM13 cell line and achieves higher accuracy in quantifying repeats in long STRs than competing methods. We also evaluate DeepRepeat on genome-wide or candidate region datasets from seven different sources. In summary, DeepRepeat enables accurate quantification of long STRs and complements existing methods relying on basecalled reads.

SUBMITTER: Fang L 

PROVIDER: S-EPMC9052667 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature

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DeepRepeat: direct quantification of short tandem repeats on signal data from nanopore sequencing.

Fang Li L   Liu Qian Q   Monteys Alex Mas AM   Gonzalez-Alegre Pedro P   Davidson Beverly L BL   Wang Kai K  

Genome biology 20220428 1


Despite recent improvements in basecalling accuracy, nanopore sequencing still has higher error rates on short-tandem repeats (STRs). Instead of using basecalled reads, we developed DeepRepeat which converts ionic current signals into red-green-blue channels, thus transforming the repeat detection problem into an image recognition problem. DeepRepeat identifies and accurately quantifies telomeric repeats in the CHM13 cell line and achieves higher accuracy in quantifying repeats in long STRs than  ...[more]

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