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 de...
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Autor*in:	Fang, Li [verfasserIn] Liu, Qian Monteys, Alex Mas Gonzalez-Alegre, Pedro Davidson, Beverly L. Wang, Kai

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	Short tandem repeat Nanopore sequencing Deep learning Telomeric repeat

Anmerkung:	© The Author(s) 2022

Übergeordnetes Werk:	Enthalten in: Genome biology - London : BioMed Central, 2000, 23(2022), 1 vom: 28. Apr.
Übergeordnetes Werk:	volume:23 ; year:2022 ; number:1 ; day:28 ; month:04

Links:	Volltext

DOI / URN:	10.1186/s13059-022-02670-6

Katalog-ID:	SPR050675370

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allfields	10.1186/s13059-022-02670-6 doi (DE-627)SPR050675370 (SPR)s13059-022-02670-6-e DE-627 ger DE-627 rakwb eng Fang, Li verfasserin aut DeepRepeat: direct quantification of short tandem repeats on signal data from nanopore sequencing 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2022 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. Short tandem repeat (dpeaa)DE-He213 Nanopore sequencing (dpeaa)DE-He213 Deep learning (dpeaa)DE-He213 Telomeric repeat (dpeaa)DE-He213 Liu, Qian aut Monteys, Alex Mas aut Gonzalez-Alegre, Pedro aut Davidson, Beverly L. aut Wang, Kai (orcid)0000-0002-5585-982X aut Enthalten in Genome biology London : BioMed Central, 2000 23(2022), 1 vom: 28. Apr. (DE-627)326173617 (DE-600)2040529-7 1474-760X nnns volume:23 year:2022 number:1 day:28 month:04 https://dx.doi.org/10.1186/s13059-022-02670-6 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 23 2022 1 28 04
spelling	10.1186/s13059-022-02670-6 doi (DE-627)SPR050675370 (SPR)s13059-022-02670-6-e DE-627 ger DE-627 rakwb eng Fang, Li verfasserin aut DeepRepeat: direct quantification of short tandem repeats on signal data from nanopore sequencing 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2022 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. Short tandem repeat (dpeaa)DE-He213 Nanopore sequencing (dpeaa)DE-He213 Deep learning (dpeaa)DE-He213 Telomeric repeat (dpeaa)DE-He213 Liu, Qian aut Monteys, Alex Mas aut Gonzalez-Alegre, Pedro aut Davidson, Beverly L. aut Wang, Kai (orcid)0000-0002-5585-982X aut Enthalten in Genome biology London : BioMed Central, 2000 23(2022), 1 vom: 28. Apr. (DE-627)326173617 (DE-600)2040529-7 1474-760X nnns volume:23 year:2022 number:1 day:28 month:04 https://dx.doi.org/10.1186/s13059-022-02670-6 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 23 2022 1 28 04
allfields_unstemmed	10.1186/s13059-022-02670-6 doi (DE-627)SPR050675370 (SPR)s13059-022-02670-6-e DE-627 ger DE-627 rakwb eng Fang, Li verfasserin aut DeepRepeat: direct quantification of short tandem repeats on signal data from nanopore sequencing 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2022 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. Short tandem repeat (dpeaa)DE-He213 Nanopore sequencing (dpeaa)DE-He213 Deep learning (dpeaa)DE-He213 Telomeric repeat (dpeaa)DE-He213 Liu, Qian aut Monteys, Alex Mas aut Gonzalez-Alegre, Pedro aut Davidson, Beverly L. aut Wang, Kai (orcid)0000-0002-5585-982X aut Enthalten in Genome biology London : BioMed Central, 2000 23(2022), 1 vom: 28. Apr. (DE-627)326173617 (DE-600)2040529-7 1474-760X nnns volume:23 year:2022 number:1 day:28 month:04 https://dx.doi.org/10.1186/s13059-022-02670-6 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 23 2022 1 28 04
allfieldsGer	10.1186/s13059-022-02670-6 doi (DE-627)SPR050675370 (SPR)s13059-022-02670-6-e DE-627 ger DE-627 rakwb eng Fang, Li verfasserin aut DeepRepeat: direct quantification of short tandem repeats on signal data from nanopore sequencing 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2022 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. Short tandem repeat (dpeaa)DE-He213 Nanopore sequencing (dpeaa)DE-He213 Deep learning (dpeaa)DE-He213 Telomeric repeat (dpeaa)DE-He213 Liu, Qian aut Monteys, Alex Mas aut Gonzalez-Alegre, Pedro aut Davidson, Beverly L. aut Wang, Kai (orcid)0000-0002-5585-982X aut Enthalten in Genome biology London : BioMed Central, 2000 23(2022), 1 vom: 28. Apr. (DE-627)326173617 (DE-600)2040529-7 1474-760X nnns volume:23 year:2022 number:1 day:28 month:04 https://dx.doi.org/10.1186/s13059-022-02670-6 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 23 2022 1 28 04
allfieldsSound	10.1186/s13059-022-02670-6 doi (DE-627)SPR050675370 (SPR)s13059-022-02670-6-e DE-627 ger DE-627 rakwb eng Fang, Li verfasserin aut DeepRepeat: direct quantification of short tandem repeats on signal data from nanopore sequencing 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2022 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. Short tandem repeat (dpeaa)DE-He213 Nanopore sequencing (dpeaa)DE-He213 Deep learning (dpeaa)DE-He213 Telomeric repeat (dpeaa)DE-He213 Liu, Qian aut Monteys, Alex Mas aut Gonzalez-Alegre, Pedro aut Davidson, Beverly L. aut Wang, Kai (orcid)0000-0002-5585-982X aut Enthalten in Genome biology London : BioMed Central, 2000 23(2022), 1 vom: 28. Apr. (DE-627)326173617 (DE-600)2040529-7 1474-760X nnns volume:23 year:2022 number:1 day:28 month:04 https://dx.doi.org/10.1186/s13059-022-02670-6 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 23 2022 1 28 04
language	English
source	Enthalten in Genome biology 23(2022), 1 vom: 28. Apr. volume:23 year:2022 number:1 day:28 month:04
sourceStr	Enthalten in Genome biology 23(2022), 1 vom: 28. Apr. volume:23 year:2022 number:1 day:28 month:04
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Short tandem repeat Nanopore sequencing Deep learning Telomeric repeat
isfreeaccess_bool	true
container_title	Genome biology
authorswithroles_txt_mv	Fang, Li @@aut@@ Liu, Qian @@aut@@ Monteys, Alex Mas @@aut@@ Gonzalez-Alegre, Pedro @@aut@@ Davidson, Beverly L. @@aut@@ Wang, Kai @@aut@@
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author	Fang, Li
spellingShingle	Fang, Li misc Short tandem repeat misc Nanopore sequencing misc Deep learning misc Telomeric repeat DeepRepeat: direct quantification of short tandem repeats on signal data from nanopore sequencing
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topic_title	DeepRepeat: direct quantification of short tandem repeats on signal data from nanopore sequencing Short tandem repeat (dpeaa)DE-He213 Nanopore sequencing (dpeaa)DE-He213 Deep learning (dpeaa)DE-He213 Telomeric repeat (dpeaa)DE-He213
topic	misc Short tandem repeat misc Nanopore sequencing misc Deep learning misc Telomeric repeat
topic_unstemmed	misc Short tandem repeat misc Nanopore sequencing misc Deep learning misc Telomeric repeat
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title	DeepRepeat: direct quantification of short tandem repeats on signal data from nanopore sequencing
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title_full	DeepRepeat: direct quantification of short tandem repeats on signal data from nanopore sequencing
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author_browse	Fang, Li Liu, Qian Monteys, Alex Mas Gonzalez-Alegre, Pedro Davidson, Beverly L. Wang, Kai
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title_sort	deeprepeat: direct quantification of short tandem repeats on signal data from nanopore sequencing
title_auth	DeepRepeat: direct quantification of short tandem repeats on signal data from nanopore sequencing
abstract	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. © The Author(s) 2022
abstractGer	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. © The Author(s) 2022
abstract_unstemmed	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. © The Author(s) 2022
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title_short	DeepRepeat: direct quantification of short tandem repeats on signal data from nanopore sequencing
url	https://dx.doi.org/10.1186/s13059-022-02670-6
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author2	Liu, Qian Monteys, Alex Mas Gonzalez-Alegre, Pedro Davidson, Beverly L. Wang, Kai
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up_date	2024-07-03T17:03:27.754Z
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