DeepRepeat: direct quantification of short tandem repeats on signal data from nanopore sequencing View Full Text


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Article Info

DATE

2022-04-28

AUTHORS

Li Fang, Qian Liu, Alex Mas Monteys, Pedro Gonzalez-Alegre, Beverly L. Davidson, Kai Wang

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. More... »

PAGES

108

References to SciGraph publications

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    http://scigraph.springernature.com/pub.10.1186/s13059-022-02670-6

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    http://dx.doi.org/10.1186/s13059-022-02670-6

    DIMENSIONS

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    PUBMED

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    59 lines
    60 method
    61 nanopore sequencing
    62 problem
    63 quantification
    64 rate
    65 reads
    66 recent improvements
    67 recognition problem
    68 repeats
    69 sequencing
    70 short tandem repeats
    71 signal data
    72 signals
    73 source
    74 summary
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