Image Processing Algorithms in the DNA Sequencer “Nanofor SPS”

The success of genomic sequencing is impossible without the development of information technologies and mathematical methods for data processing to establish various features in the analyzed objects (nucleic acids) and trends in their changes. The volume of experimental data in the research of the g...

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Veröffentlicht in:	Technical physics Jg. 69; H. 3; S. 612 - 618
Hauptverfasser:	Manoilov, V. V., Borodinov, A. G., Saraev, A. S., Petrov, A. I., Zarutskiy, I. V., Kurochkin, V. E.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Moscow Pleiades Publishing 01.03.2024 Springer Springer Nature B.V
Schlagworte:	Algorithms Camcorders Cameras Classical and Continuum Physics Data processing DNA DNA sequencing Equipment and supplies Evaluation Genomes Genomics Image processing Methods Nucleic acids Nucleotide sequencing Nucleotides Physics Physics and Astronomy Signal to noise ratio Software image processing nucleic acid data processing sequencing
ISSN:	1063-7842, 1090-6525
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Abstract	The success of genomic sequencing is impossible without the development of information technologies and mathematical methods for data processing to establish various features in the analyzed objects (nucleic acids) and trends in their changes. The volume of experimental data in the research of the genome has grown significantly, and new methods and algorithms are required for their processing. The primary stage of processing the data of devices for genomic parallel sequencing is the evaluation of the parameters of images obtained from video cameras in the form of electrical signals. The next stage of processing is the construction of a sequence of nucleotides according to algorithms that depend on the principle of operation of the device for sequencing nucleic acids. When performing this stage, algorithms for evaluating quality indicators for all individual readings (reads) are important. One of the ways to assess quality is to use algorithms based on the k -measure analysis methodology. The calculation of the number of occurrences of k -measures during the experiment on the parallel sequencing system makes it possible to assess the reliability of the analysis. In this article, algorithms for processing genetic analyzer data are considered.
AbstractList	The success of genomic sequencing is impossible without the development of information technologies and mathematical methods for data processing to establish various features in the analyzed objects (nucleic acids) and trends in their changes. The volume of experimental data in the research of the genome has grown significantly, and new methods and algorithms are required for their processing. The primary stage of processing the data of devices for genomic parallel sequencing is the evaluation of the parameters of images obtained from video cameras in the form of electrical signals. The next stage of processing is the construction of a sequence of nucleotides according to algorithms that depend on the principle of operation of the device for sequencing nucleic acids. When performing this stage, algorithms for evaluating quality indicators for all individual readings (reads) are important. One of the ways to assess quality is to use algorithms based on the k-measure analysis methodology. The calculation of the number of occurrences of k-measures during the experiment on the parallel sequencing system makes it possible to assess the reliability of the analysis. In this article, algorithms for processing genetic analyzer data are considered. The success of genomic sequencing is impossible without the development of information technologies and mathematical methods for data processing to establish various features in the analyzed objects (nucleic acids) and trends in their changes. The volume of experimental data in the research of the genome has grown significantly, and new methods and algorithms are required for their processing. The primary stage of processing the data of devices for genomic parallel sequencing is the evaluation of the parameters of images obtained from video cameras in the form of electrical signals. The next stage of processing is the construction of a sequence of nucleotides according to algorithms that depend on the principle of operation of the device for sequencing nucleic acids. When performing this stage, algorithms for evaluating quality indicators for all individual readings (reads) are important. One of the ways to assess quality is to use algorithms based on the k -measure analysis methodology. The calculation of the number of occurrences of k -measures during the experiment on the parallel sequencing system makes it possible to assess the reliability of the analysis. In this article, algorithms for processing genetic analyzer data are considered.
Audience	Academic
Author	Zarutskiy, I. V. Petrov, A. I. Saraev, A. S. Manoilov, V. V. Borodinov, A. G. Kurochkin, V. E.
Author_xml	– sequence: 1 givenname: V. V. surname: Manoilov fullname: Manoilov, V. V. email: alex.niispb@yandex.ru organization: Institute of Analytical Instrument Making, Russian Academy of Sciences – sequence: 2 givenname: A. G. surname: Borodinov fullname: Borodinov, A. G. organization: Institute of Analytical Instrument Making, Russian Academy of Sciences – sequence: 3 givenname: A. S. surname: Saraev fullname: Saraev, A. S. organization: Institute of Analytical Instrument Making, Russian Academy of Sciences – sequence: 4 givenname: A. I. surname: Petrov fullname: Petrov, A. I. organization: Institute of Analytical Instrument Making, Russian Academy of Sciences – sequence: 5 givenname: I. V. surname: Zarutskiy fullname: Zarutskiy, I. V. organization: Institute of Analytical Instrument Making, Russian Academy of Sciences – sequence: 6 givenname: V. E. surname: Kurochkin fullname: Kurochkin, V. E. organization: Institute of Analytical Instrument Making, Russian Academy of Sciences
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Cites_doi	10.1046/j.1365-2818.1997.2630819.x 10.1093/bioinformatics/btr011 10.1002/jemt.20118 10.1364/JOT.84.000095 10.1073/pnas.74.12.5463 10.1109/ROBOT.1986.1087510 10.15622/sp.2019.18.4.1010-1036 10.1073/pnas.74.2.560 10.1093/bioinformat-ics/btp38 10.1109/ICCE.2006.1598314 10.1364/JOT.85.000203 10.1016/j.patcog.2012.11.011
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Copyright	Pleiades Publishing, Ltd. 2024. ISSN 1063-7842, Technical Physics, 2024, Vol. 69, No. 3, pp. 612–618. © Pleiades Publishing, Ltd., 2024. Russian Text © The Author(s), 2022, published in Zhurnal Tekhnicheskoi Fiziki, 2022, Vol. 92, No. 7, pp. 985–992. English Text © Ioffe Institute, 2022. COPYRIGHT 2024 Springer Pleiades Publishing, Ltd. 2024.
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References_xml	– reference: V. V. Manoilov, I. V. Zarutsky. Obrabotka signalov fluorestsentsii massovogo parallel’nogo sekvenirovaniya nukleinovykh kislot. Sv-vo o gos. registratsii programmy dlya EVM. 2019663248. – reference: J. P. Lewis. Fast Template Matching, Vision Interface, 120–123 (1995). – reference: V. V. Manoilov, A. G. Borodinov, I. V. Zarutsky, A. I. Petrov, V. E. Kurochkin. Zhurn. Trudy SPII RAN, 18 (4), 1010–1036 (2019).(in Russian).https://doi.org/10.15622/sp.2019.18.4.1010-1036 – reference: V. S. Sizikov. Pryamye i obratnye zadachi v vosstanovleniya izobrazheniy, spekroskopii i tomografii c Matlab (Lan’, SPb., 2017), 412 p. (in Russian). – reference: PertuzS.D. Puig, M. A. GarciaPattern Recognition201246141514322013PatRe..46.1415P10.1016/j.patcog.2012.11.011 – reference: Zh. Maks. Metody i tekhnika obrabotki signalov pri phizicheskikh izmereniyakh: v 2-kh tomakh, per. s frants. (Mir, M., 1983), vol. 1, 312, p. (in Russian). – reference: WhitefordN.SkellyTCurtisCh.RitchieM. E.LohrA.ZaranekA. W.AbnizovaI.BrownC.Bioinformatics2009252194219910.1093/bioinformat-ics/btp38 – reference: A. V. Aleksandrov, A. A. Shalyto. Nauchno-tekhnichecky vestnik informatsionnykh tekhnologiy, mekhaniki i optiki, (1), 108–114 (2016) (in Russian). – reference: K. V. Romanenkov. Metod otsenki kachestva cborki genoma na ocnove chastotkmerov, Preprinty IPM im. M.V. Keldysha, 2017, 11. – reference: SizikovV. S.J. Opt. Technol.2017849510110.1364/JOT.84.000095 – reference: SizikovV. S.StepanovA. V.MezheninA. V.BurlovR. A.EksemplyarovR. A.J. Opt. Technol.2018859510110.1364/JOT.85.000203 – reference: MarcaisG.KingsfordC.Bioinformatics20112776477010.1093/bioinformatics/btr011 – reference: D. V. Rebrikov, D. O. Korostin, E. S. Shubina, V. V. Ilinskiy. NGS: vysokoproizvoditel’noe sekvenirovanie, pod obshchey red. D. V. Rebrikova (BINOM, Laboratoriya znaniy, M., 2014), 232 p. (in Russian). – reference: E. Krotkov, J. -P. Martin. Range From Focus. Proceed. IEEE Intern. Conf. Robotics and Automation, 1093–1098 (1986). https://doi.org/10.1109/ROBOT.1986.1087510 – reference: SunYuDuthalerS.NelsonB. J.Microscopy Res. Tech.20046513914910.1002/jemt.20118 – reference: Chun-Hung Shen, H. H. Chen. Robust Focus Measure for Low-Contrast Images. 2006 Digest of Technical Papers Intern. Conf. Consumer Electron., 69–70 (2006). https://doi.org/10.1109/ICCE.2006.1598314 – reference: SangerF.NicleinS.CoulsonA.R.Proc. Natl. Acad. Sci. USA197774546354671977PNAS...74.5463S10.1073/pnas.74.12.5463 – reference: R. Vuds, R. Gonsales. Tsifrovaya obrabotka izobrazheniy (Tekhnosfera, M. 2012), 3-e izd., ispr. i dop., 1104 p. (in Russian). – reference: SantosA.de SolorzanoC. O.VaqueroJ. J.PenaJ. M.MapicaN.PozoF. D.J. Microscopy199718826427210.1046/j.1365-2818.1997.2630819.x – reference: MaxamA. M.GilbertW.Proc. Natl. Acad. Sci. USA1977745605641977PNAS...74..560M10.1073/pnas.74.2.560 – reference: T. J. Treangen, D. D. Sommer, F. E. Angly, S. Koren, M. Pop. Current Protocols in Bioinformatics, 11 (11.8), 1–18 (2011). – volume: 188 start-page: 264 year: 1997 ident: 4293_CR7 publication-title: J. Microscopy doi: 10.1046/j.1365-2818.1997.2630819.x – volume: 27 start-page: 764 year: 2011 ident: 4293_CR21 publication-title: Bioinformatics doi: 10.1093/bioinformatics/btr011 – volume: 65 start-page: 139 year: 2004 ident: 4293_CR8 publication-title: Microscopy Res. Tech. doi: 10.1002/jemt.20118 – volume: 84 start-page: 95 year: 2017 ident: 4293_CR13 publication-title: J. Opt. Technol. doi: 10.1364/JOT.84.000095 – volume: 74 start-page: 5463 year: 1977 ident: 4293_CR1 publication-title: Proc. Natl. Acad. Sci. USA doi: 10.1073/pnas.74.12.5463 – ident: 4293_CR10 doi: 10.1109/ROBOT.1986.1087510 – ident: 4293_CR15 – ident: 4293_CR4 doi: 10.15622/sp.2019.18.4.1010-1036 – volume: 74 start-page: 560 year: 1977 ident: 4293_CR2 publication-title: Proc. Natl. Acad. Sci. USA doi: 10.1073/pnas.74.2.560 – ident: 4293_CR12 – ident: 4293_CR18 – volume: 25 start-page: 2194 year: 2009 ident: 4293_CR17 publication-title: Bioinformatics doi: 10.1093/bioinformat-ics/btp38 – ident: 4293_CR9 doi: 10.1109/ICCE.2006.1598314 – ident: 4293_CR11 – ident: 4293_CR16 – ident: 4293_CR20 – ident: 4293_CR19 – volume: 85 start-page: 95 year: 2018 ident: 4293_CR14 publication-title: J. Opt. Technol. doi: 10.1364/JOT.85.000203 – ident: 4293_CR5 – volume: 46 start-page: 1415 year: 2012 ident: 4293_CR6 publication-title: Pattern Recognition doi: 10.1016/j.patcog.2012.11.011 – ident: 4293_CR3
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SubjectTerms	Algorithms Camcorders Cameras Classical and Continuum Physics Data processing DNA DNA sequencing Equipment and supplies Evaluation Genomes Genomics Image processing Methods Nucleic acids Nucleotide sequencing Nucleotides Physics Physics and Astronomy Signal to noise ratio Software
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Title	Image Processing Algorithms in the DNA Sequencer “Nanofor SPS”
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