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DNAscent v2: detecting replication forks in nanopore sequencing data with deep learning
Measuring DNA replication dynamics with high throughput and single-molecule resolution is critical for understanding both the basic biology behind how cells replicate their DNA and how DNA replication can be used as a therapeutic target for diseases like cancer. In recent years, the detection of bas...
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Published in: | BMC genomics 2021-06, Vol.22 (1), p.1-430, Article 430 |
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description | Measuring DNA replication dynamics with high throughput and single-molecule resolution is critical for understanding both the basic biology behind how cells replicate their DNA and how DNA replication can be used as a therapeutic target for diseases like cancer. In recent years, the detection of base analogues in Oxford Nanopore Technologies (ONT) sequencing reads has become a promising new method to supersede existing single-molecule methods such as DNA fibre analysis: ONT sequencing yields long reads with high throughput, and sequenced molecules can be mapped to the genome using standard sequence alignment software. This paper introduces DNAscent v2, software that uses a residual neural network to achieve fast, accurate detection of the thymidine analogue BrdU with single-nucleotide resolution. DNAscent v2 also comes equipped with an autoencoder that interprets the pattern of BrdU incorporation on each ONT-sequenced molecule into replication fork direction to call the location of replication origins termination sites. DNAscent v2 surpasses previous versions of DNAscent in BrdU calling accuracy, origin calling accuracy, speed, and versatility across different experimental protocols. Unlike NanoMod, DNAscent v2 positively identifies BrdU without the need for sequencing unmodified DNA. Unlike RepNano, DNAscent v2 calls BrdU with single-nucleotide resolution and detects more origins than RepNano from the same sequencing data. DNAscent v2 is open-source and available at https://github.com/MBoemo/DNAscent. This paper shows that DNAscent v2 is the new state-of-the-art in the high-throughput, single-molecule detection of replication fork dynamics. These improvements in DNAscent v2 mark an important step towards measuring DNA replication dynamics in large genomes with single-molecule resolution. Looking forward, the increase in accuracy in single-nucleotide resolution BrdU calls will also allow DNAscent v2 to branch out into other areas of genome stability research, particularly the detection of DNA repair. |
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In recent years, the detection of base analogues in Oxford Nanopore Technologies (ONT) sequencing reads has become a promising new method to supersede existing single-molecule methods such as DNA fibre analysis: ONT sequencing yields long reads with high throughput, and sequenced molecules can be mapped to the genome using standard sequence alignment software. This paper introduces DNAscent v2, software that uses a residual neural network to achieve fast, accurate detection of the thymidine analogue BrdU with single-nucleotide resolution. DNAscent v2 also comes equipped with an autoencoder that interprets the pattern of BrdU incorporation on each ONT-sequenced molecule into replication fork direction to call the location of replication origins termination sites. DNAscent v2 surpasses previous versions of DNAscent in BrdU calling accuracy, origin calling accuracy, speed, and versatility across different experimental protocols. Unlike NanoMod, DNAscent v2 positively identifies BrdU without the need for sequencing unmodified DNA. Unlike RepNano, DNAscent v2 calls BrdU with single-nucleotide resolution and detects more origins than RepNano from the same sequencing data. DNAscent v2 is open-source and available at https://github.com/MBoemo/DNAscent. This paper shows that DNAscent v2 is the new state-of-the-art in the high-throughput, single-molecule detection of replication fork dynamics. These improvements in DNAscent v2 mark an important step towards measuring DNA replication dynamics in large genomes with single-molecule resolution. Looking forward, the increase in accuracy in single-nucleotide resolution BrdU calls will also allow DNAscent v2 to branch out into other areas of genome stability research, particularly the detection of DNA repair.</description><identifier>ISSN: 1471-2164</identifier><identifier>EISSN: 1471-2164</identifier><identifier>DOI: 10.1186/s12864-021-07736-6</identifier><identifier>PMID: 34107894</identifier><language>eng</language><publisher>London: BioMed Central Ltd</publisher><subject>Accuracy ; Artificial neural networks ; Computer programs ; Deep learning ; Deoxyribonucleic acid ; DNA ; DNA biosynthesis ; DNA repair ; DNA replication ; DNA sequencing ; DNAscent ; Genomes ; Genomics ; Identification and classification ; Machine learning ; Neural networks ; Nucleotide sequence ; Nucleotide sequencing ; Nucleotides ; Origins ; Oxford nanopore ; Performance evaluation ; Probability ; Replication ; Replication fork ; Replication forks ; Replication origins ; Residual neural networks ; Software ; Source code ; Technology application ; Therapeutic targets ; Thymidine</subject><ispartof>BMC genomics, 2021-06, Vol.22 (1), p.1-430, Article 430</ispartof><rights>COPYRIGHT 2021 BioMed Central Ltd.</rights><rights>2021. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). 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In recent years, the detection of base analogues in Oxford Nanopore Technologies (ONT) sequencing reads has become a promising new method to supersede existing single-molecule methods such as DNA fibre analysis: ONT sequencing yields long reads with high throughput, and sequenced molecules can be mapped to the genome using standard sequence alignment software. This paper introduces DNAscent v2, software that uses a residual neural network to achieve fast, accurate detection of the thymidine analogue BrdU with single-nucleotide resolution. DNAscent v2 also comes equipped with an autoencoder that interprets the pattern of BrdU incorporation on each ONT-sequenced molecule into replication fork direction to call the location of replication origins termination sites. DNAscent v2 surpasses previous versions of DNAscent in BrdU calling accuracy, origin calling accuracy, speed, and versatility across different experimental protocols. Unlike NanoMod, DNAscent v2 positively identifies BrdU without the need for sequencing unmodified DNA. Unlike RepNano, DNAscent v2 calls BrdU with single-nucleotide resolution and detects more origins than RepNano from the same sequencing data. DNAscent v2 is open-source and available at https://github.com/MBoemo/DNAscent. This paper shows that DNAscent v2 is the new state-of-the-art in the high-throughput, single-molecule detection of replication fork dynamics. These improvements in DNAscent v2 mark an important step towards measuring DNA replication dynamics in large genomes with single-molecule resolution. Looking forward, the increase in accuracy in single-nucleotide resolution BrdU calls will also allow DNAscent v2 to branch out into other areas of genome stability research, particularly the detection of DNA repair.</description><subject>Accuracy</subject><subject>Artificial neural networks</subject><subject>Computer programs</subject><subject>Deep learning</subject><subject>Deoxyribonucleic acid</subject><subject>DNA</subject><subject>DNA biosynthesis</subject><subject>DNA repair</subject><subject>DNA replication</subject><subject>DNA sequencing</subject><subject>DNAscent</subject><subject>Genomes</subject><subject>Genomics</subject><subject>Identification and classification</subject><subject>Machine learning</subject><subject>Neural networks</subject><subject>Nucleotide sequence</subject><subject>Nucleotide sequencing</subject><subject>Nucleotides</subject><subject>Origins</subject><subject>Oxford nanopore</subject><subject>Performance evaluation</subject><subject>Probability</subject><subject>Replication</subject><subject>Replication fork</subject><subject>Replication forks</subject><subject>Replication origins</subject><subject>Residual neural networks</subject><subject>Software</subject><subject>Source code</subject><subject>Technology application</subject><subject>Therapeutic 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v2: detecting replication forks in nanopore sequencing data with deep learning</title><author>Boemo, Michael A</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c574t-92fba5aba24f95e8f6757f44b71f8000250d43cae745dac38e39f8dbd80455d23</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Accuracy</topic><topic>Artificial neural networks</topic><topic>Computer programs</topic><topic>Deep learning</topic><topic>Deoxyribonucleic acid</topic><topic>DNA</topic><topic>DNA biosynthesis</topic><topic>DNA repair</topic><topic>DNA replication</topic><topic>DNA sequencing</topic><topic>DNAscent</topic><topic>Genomes</topic><topic>Genomics</topic><topic>Identification and classification</topic><topic>Machine learning</topic><topic>Neural networks</topic><topic>Nucleotide sequence</topic><topic>Nucleotide sequencing</topic><topic>Nucleotides</topic><topic>Origins</topic><topic>Oxford nanopore</topic><topic>Performance evaluation</topic><topic>Probability</topic><topic>Replication</topic><topic>Replication fork</topic><topic>Replication forks</topic><topic>Replication origins</topic><topic>Residual neural networks</topic><topic>Software</topic><topic>Source code</topic><topic>Technology application</topic><topic>Therapeutic targets</topic><topic>Thymidine</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Boemo, Michael A</creatorcontrib><collection>CrossRef</collection><collection>Science (Gale in Context)</collection><collection>ProQuest Central (Corporate)</collection><collection>Calcium & Calcified Tissue Abstracts</collection><collection>Chemoreception Abstracts</collection><collection>Entomology Abstracts (Full archive)</collection><collection>Neurosciences Abstracts</collection><collection>Toxicology Abstracts</collection><collection>ProQuest_Health & Medical Collection</collection><collection>ProQuest Central 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Abstracts</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ - Directory of Open Access Journals</collection><jtitle>BMC genomics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Boemo, Michael A</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>DNAscent v2: detecting replication forks in nanopore sequencing data with deep learning</atitle><jtitle>BMC genomics</jtitle><date>2021-06-09</date><risdate>2021</risdate><volume>22</volume><issue>1</issue><spage>1</spage><epage>430</epage><pages>1-430</pages><artnum>430</artnum><issn>1471-2164</issn><eissn>1471-2164</eissn><abstract>Measuring DNA replication dynamics with high throughput and single-molecule resolution is critical for understanding both the basic biology behind how cells replicate their DNA and how DNA replication can be used as a therapeutic target for diseases like cancer. In recent years, the detection of base analogues in Oxford Nanopore Technologies (ONT) sequencing reads has become a promising new method to supersede existing single-molecule methods such as DNA fibre analysis: ONT sequencing yields long reads with high throughput, and sequenced molecules can be mapped to the genome using standard sequence alignment software. This paper introduces DNAscent v2, software that uses a residual neural network to achieve fast, accurate detection of the thymidine analogue BrdU with single-nucleotide resolution. DNAscent v2 also comes equipped with an autoencoder that interprets the pattern of BrdU incorporation on each ONT-sequenced molecule into replication fork direction to call the location of replication origins termination sites. DNAscent v2 surpasses previous versions of DNAscent in BrdU calling accuracy, origin calling accuracy, speed, and versatility across different experimental protocols. Unlike NanoMod, DNAscent v2 positively identifies BrdU without the need for sequencing unmodified DNA. Unlike RepNano, DNAscent v2 calls BrdU with single-nucleotide resolution and detects more origins than RepNano from the same sequencing data. DNAscent v2 is open-source and available at https://github.com/MBoemo/DNAscent. This paper shows that DNAscent v2 is the new state-of-the-art in the high-throughput, single-molecule detection of replication fork dynamics. These improvements in DNAscent v2 mark an important step towards measuring DNA replication dynamics in large genomes with single-molecule resolution. Looking forward, the increase in accuracy in single-nucleotide resolution BrdU calls will also allow DNAscent v2 to branch out into other areas of genome stability research, particularly the detection of DNA repair.</abstract><cop>London</cop><pub>BioMed Central Ltd</pub><pmid>34107894</pmid><doi>10.1186/s12864-021-07736-6</doi><orcidid>https://orcid.org/0000-0002-0326-8200</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Accuracy Artificial neural networks Computer programs Deep learning Deoxyribonucleic acid DNA DNA biosynthesis DNA repair DNA replication DNA sequencing DNAscent Genomes Genomics Identification and classification Machine learning Neural networks Nucleotide sequence Nucleotide sequencing Nucleotides Origins Oxford nanopore Performance evaluation Probability Replication Replication fork Replication forks Replication origins Residual neural networks Software Source code Technology application Therapeutic targets Thymidine |
title | DNAscent v2: detecting replication forks in nanopore sequencing data with deep learning |
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