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A Parallel Algorithm for Designing Primer and Probe for Accurate Detection of Severe Acute Respiratory Syndrome Coronavirus

Year 2023, , 477 - 485, 15.10.2023

Hilal Arslan , Rıza Durmaz

https://doi.org/10.34248/bsengineering.1324890

Abstract

The spread of the SARS-CoV-2 in many countries has led to multiple SARS-CoV-2 variants, and this makes accurate detection of SARS-CoV-2 difficult. The reverse transcription real-time polymerase chain reaction (RT-PCR) is a widely used gold-standard method to detect SARS-CoV-2, and accurate designing of primers and probes is crucial to prevent false negative results, especially with the rise of new dangerous variants. Therefore, it is significant to determine primers and probes targeting conserved regions in the genome sequence to diagnose many variants of SARS-CoV-2. In this paper, we propose a novel and efficient method for identifying PCR primers and probe sequences by evaluating sequences belonging to SARS-CoV-2 variant of concern and variants of interest. We propose 13 primer and probe sets by analyzing 54,524 sequences in Alpha variant, 25,465 sequences in Beta variant, 53,501 sequences in Gamma variant, 46,225 sequences in Delta variant, and 43,682 sequences in Omicron variant from GISAID. Furthermore, we analyzed 1,008 sequences in Lambda variant as well as 5,844 sequences in Mu variant to extract primer and probe sets from GISAID. The proposed primer and probe sets were validated in 406,757 new SARS-CoV-2 unique genomes collected from NCBI. In silico evaluation presented that the proposed set of primers and probes are found inside about 99% of SARS-CoV-2 genome sequences. Designed primers present a higher potential to detect the main SARS-CoV-2 recent variant of concerns and the variants of interests. The superiority of the proposed method is also highlighted by comparing the state-of-the-art PCR primer and probe sets based on the number of mismatches for various types of SARS-CoV-2 genomes.

Keywords

COVID-19 SARS-CoV-2 SARS-CoV-2 variants Primer and Probe Real Time PCR

References

  • Ali S, Tamkanat-E-Ali Khan MA, Khan I, Patterson M. 2021. Effective and scalable clustering of sars-cov-2 sequences. Proceedings of the 5th International Conference on Big Data Research, September 23-25, Qingdao, China, pp: 42-49.
  • Anantharajah A, Helaers R, Defour J.-P, Olive N, Kabera F, Croonen L, Kabamba-Mukadi B. 2021. How to choose the right real-time RT-PCR primer sets for the SARS-CoV-2 genome detection. J Virol Methods, 295: 114197.
  • Arslan H. 2021a. COVID-19 prediction based on genome similarity of human SARS-CoV-2 and bat SARS-CoV-like coronavirus. Comput Indust Engin, 161: 107666.
  • Arslan H. 2021b. Machine learning methods for Covid-19 prediction using human genomic data. Proceed 74 (1): 20.
  • Arslan H, Arslan H. 2021. A new covid-19 detection method from human genome sequences using cpg island features and knn classifier. Engin Sci Technol Inter J, 24(4): 839-847.
  • Arslan H, Aygun B. 2021. Performance analysis of machine learning algorithms in detection of covid-19 from common symptoms. Proceeding of 29th Signal Processing and Communications Applications Conference, June 9-11, Istanbul, Türkiye, pp: 1-4.
  • Arslan H. 2022a. Classification of SARS-CoV-2 Variants in Turkey. J Turkish Operat Manage, 6(1): 1092-1101 .
  • Arslan H. 2022b. Bagging and boosting for predicting mortality of patients with COVID-19. Dicle Univ J Engin, 13(2): 221-226.
  • Arslan H. 2023. A k-mer based metaheuristic approach for detecting COVID-19 variants. Dicle Univ J Engin, 14(1): 17-26.
  • Baj A, Novazzi F, Ferrante FD, Genoni A, Cassani G, Prestia M, Maggi F. 2021. Introduction of SARS-COV-2 c.37 (WHO VOI lambda) from Peru to Italy. J Medical Virol, 93(12): 6460–6461.
  • Bustin SA, Benes V, Garson JA, Hellemans J, Huggett J, Kubista M, Wittwer CT. 2009. The MIQE guidelines: Minimum information for publication of quantitative realtime PCR experiments. Clin Chem, 55(4): 611–622.
  • Cobb BR, Vaks JE, Do T, Vilchez RA. 2011. Evolution in the sensitivity of quantitative HIV-1 viral load tests. J Clin Virol, 52 S77–S82.
  • Davi MJP, Jeronimo SMB, Lima JPMS, Lanza DCF. 2021. Design and in silico validation of polymerase chain reaction primers to detect severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Sci Rep, 11(1): 12565.
  • Irudayasamy A, Ganesh D, Natesh M. 2022. Big data analytics on the impact of OMICRON and its influence on unvaccinated community through advanced machine learning concepts. Int J Syst Assur Eng Manag, 2022: 1-10.
  • Jain A, Rophina M, Mahajan S, Krishnan BB, Sharma M, Mandal S, Scaria V. 2021. Analysis of the potential impact of genomic variants in global sars-cov-2 genomes on molecular diagnostic assays. Inter J Infect Diseas, 102: 460-462.
  • Jamil S, Rahman M. 2021. A dual-stage vocabulary of features (vof)-based technique for covid-19 variants’ classification. Applied Sci, 11 (24): 11902.
  • Jiang X, Coffee M, Bari A, Wang J, Jiang X, Huang J, Huang Y. 2020. Towards an Artificial Intelligence Framework for Data-Driven Prediction of Coronavirus Clinical Severity. Comput Mater Contin, 62(3): 537–551.
  • Kent WJ, Sugnet CW, Furey TS, Roskin KM, Pringle TH, Zahler AM, Haussler D. 2002. The human genome browser at UCSC. Genome Res, 12(6): 996–1006.
  • Langer T, Favarato M, Giudici R, Bassi G, Garberi R, Villa F, Fumagalli R. 2020. Development of machine learning models to predict RT-PCR results for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in patients with influenza-like symptoms using only basic clinical data. Scandinavian J Trauma Resuscit Emerg Med, 28 (1): 113.
  • Lauring AS, Malani PN. 2021. Variants of SARS-CoV-2. JAMA, 326(9): 880-880.
  • Lopez-Rincon A, Tonda A, Mendoza-Maldonado L, Mulders DGJC, Molenkamp R, Perez-Romero CA, Kraneveld AD. 2021. Classification and specific primer design for accurate detection of SARS-CoV-2 using deep learning. Sci Reports 11(1): 947.
  • Lownik JC, Farrar JS, Way GW, McKay A, Roychoudhury P, Greninger AL, Martin RK. 2021. Fast sars-cov-2 variant detection using snapback primer high-resolution melting. Diagnostics, 11(10): 1788.
  • Mlcochova P, Kemp SA, Dhar MS. 2021. SARS-CoV-2 b.1.617.2 delta variant replication and immune evasion. Nature, 599(7883): 114–119.
  • Mohamadou Y, Halidou A, Kapen PT. 2020 November. A review of mathematical modeling artificial intelligence and datasets used in the study prediction and management of COVID- 19. Applied Intell, 50(11): 3913–3925.
  • Muhammad LJ, Algehyne EA, Usman SS, Ahmad A, Chakraborty C, Mohammed IA. 2021 February. Supervised machine learning models for prediction of Covid-19 infection using epidemiology dataset. SN Computer Sci, 2(1): 11.
  • NCBISD. 2021. National center for biotechnology information search database. URL: https://www.ncbi.nlm.nih.gov/ sars-cov-2/. (accessed date: December 04, 2021).
  • Nayar G, Seabolt EE, Kunitomi M, Agarwal A, Beck KL, Mukherjee V, Kaufman JH. 2021. Analysis and forecasting of global real time RT-PCR primers and probes for SARS-CoV-2. Sci Reports, 11 (1): 8988.
  • Ogiela MR, Ogiela U. 2021. Linguistic methods in healthcare application and COVID-19 variants classification. Neural Comput Applicat, 35: 13935–13940
  • Os´orio NS, Correia-Neves M. 2021. Implication of SARS-CoV-2 evolution in the sensitivity of RT-qPCR diagnostic assays. The Lancet Infect Diseas, 21(2): 166–167.
  • Park M, Won J, Choi BY, Lee CJ. 2020. Optimiza- 5 tion of primer sets and detection protocols for SARS-CoV-2 of coronavirus disease 2019 (COVID-19) using PCR and real-time PCR. Experiment Molec Med, 52(6): 963–977.
  • PCR Primer Stats. 2022. URL: https://www.bioinformatics.org/sms2/pcrprimerstats.html. (accessed date October 01, 2022).
  • Primer3plus. 2022. URL: https://www.bioinformatics.nl/cgi-bin/primer3plus/primer3plus.cgi. (accessed date October 01, 2022).
  • Rychlik W. 2007. OLIGO 7 primer analysis software. Humana Pressi In PCR primer design, New York, USA, pp: 35–59.
  • Sabino EC, Buss LF, Carvalho MPS. 2021. Resurgence of COVID-19 in manaus brazil despite high seroprevalence. The Lancet, 397(10273): 452–455.
  • Sahoo JP, Samal KC. 2021. World on alert: WHO designated south african new COVID strain (Omicron/B.1.1.529) as a variant of concern. Biotica Res Today, 3(11): 1086–1088.
  • Shi F, Wang J, Shi J, Wu Z, Wang Q, Tang Z, Shen D. 2021. Review of artificial intelligence techniques in imaging data acquisition segmentation and diagnosis for covid-19. IEEE Rev Biomedic Eng, 14 4-15.
  • Shu Y, McCauley J. 2017. GISAID: Global initiative on sharing all influenza data - from vision to reality. Eurosurveillance, 22(13): 30494.
  • Stothard P. 2000. The sequence manipulation suite: JavaScript programs for analyzing and formatting protein and DNA sequences. BioTechniques, 28(6): 1102–1104.
  • Tegally H, Wilkinson E, Giovanetti M. 2021 March. Detection of a SARS-CoV-2 variant of concern in South Africa. Nature, 592(7854): 438–443.
  • Togrul M, Arslan H. 2022. Detection of SARS-CoV-2 main variants of concerns using deep learning. Proceeding of Innovations in Intelligent Systems and Applications Conference, September 07-09, Antalya, Türkiye, pp: 32.
  • UCSC In-silico PCR. 2022. URL: https://genome.ucsc.edu/cgi-bin/hgPcr. (accessed date October 01, 2022).
  • Uriu K, Kimura I, Shirakawa K, Takaori-Kondo A, aki Nakada T, Kaneda A, Sato K. 2021. Neutralization of the SARS-CoV-2 mu variant by convalescent and vaccine serum. New England J Med, 385(25): 2397–2399.
  • Volz E, Mishra S, Chand M, Barrett J. 2021. Assessing transmissibility of SARS-CoV-2 lineage b.1.1.7 in England. Nature, 593(7858): 266–269.
  • Wink PL, Volpato FCZ, Monteiro FL, Willig JB, Zavascki AP, Barth AL, Martins AF. 2021. First identification of SARS-CoV-2 lambda (c.37) variant in southern Brazil. Infect Control Hospital Epidemiol, 2021: 1–2.
  • Xu Y, Ye W, Song Q, Shen L, Liu Y, Guo Y, Liu G, Wu H, Wang X, Sun X, Bai L, Luo C, Liao T, Chen H, Song C, Huang C, Wu Y, Xu Z. 2022. Using machine learning models to predict the duration of the recovery of COVID-19 patients hospitalized in Fangcang shelter hospital during the Omicron BA. 2.2 pandemic. In Frontiers Med, 9: 1001801.
  • Yaniv K, Ozer E, Shagan M, Lakkakula S, Plotkin N, Bhandarkar NS, Kushmaro A. 2021. Direct RT-QPCR assay for sars-cov-2 variants of concern (alpha b.1.1.7 and beta b.1.351) detection and quantification in wastewater. Environ Res, 201: 111653.
  • Zoabi Y, Deri-Rozov S, Shomron N. 2021. Machine learning-based prediction of COVID-19 diagnosis based on symptoms. NPJ Digital Medi, 4(1): 3.

A Parallel Algorithm for Designing Primer and Probe for Accurate Detection of Severe Acute Respiratory Syndrome Coronavirus

Year 2023, , 477 - 485, 15.10.2023

Hilal Arslan , Rıza Durmaz

https://doi.org/10.34248/bsengineering.1324890

Abstract

The spread of the SARS-CoV-2 in many countries has led to multiple SARS-CoV-2 variants, and this makes accurate detection of SARS-CoV-2 difficult. The reverse transcription real-time polymerase chain reaction (RT-PCR) is a widely used gold-standard method to detect SARS-CoV-2, and accurate designing of primers and probes is crucial to prevent false negative results, especially with the rise of new dangerous variants. Therefore, it is significant to determine primers and probes targeting conserved regions in the genome sequence to diagnose many variants of SARS-CoV-2. In this paper, we propose a novel and efficient method for identifying PCR primers and probe sequences by evaluating sequences belonging to SARS-CoV-2 variant of concern and variants of interest. We propose 13 primer and probe sets by analyzing 54,524 sequences in Alpha variant, 25,465 sequences in Beta variant, 53,501 sequences in Gamma variant, 46,225 sequences in Delta variant, and 43,682 sequences in Omicron variant from GISAID. Furthermore, we analyzed 1,008 sequences in Lambda variant as well as 5,844 sequences in Mu variant to extract primer and probe sets from GISAID. The proposed primer and probe sets were validated in 406,757 new SARS-CoV-2 unique genomes collected from NCBI. In silico evaluation presented that the proposed set of primers and probes are found inside about 99% of SARS-CoV-2 genome sequences. Designed primers present a higher potential to detect the main SARS-CoV-2 recent variant of concerns and the variants of interests. The superiority of the proposed method is also highlighted by comparing the state-of-the-art PCR primer and probe sets based on the number of mismatches for various types of SARS-CoV-2 genomes.

Keywords

COVID-19 SARS-CoV-2 SARS-CoV-2 variants Primer and Probe Real Time PCR

References

  • Ali S, Tamkanat-E-Ali Khan MA, Khan I, Patterson M. 2021. Effective and scalable clustering of sars-cov-2 sequences. Proceedings of the 5th International Conference on Big Data Research, September 23-25, Qingdao, China, pp: 42-49.
  • Anantharajah A, Helaers R, Defour J.-P, Olive N, Kabera F, Croonen L, Kabamba-Mukadi B. 2021. How to choose the right real-time RT-PCR primer sets for the SARS-CoV-2 genome detection. J Virol Methods, 295: 114197.
  • Arslan H. 2021a. COVID-19 prediction based on genome similarity of human SARS-CoV-2 and bat SARS-CoV-like coronavirus. Comput Indust Engin, 161: 107666.
  • Arslan H. 2021b. Machine learning methods for Covid-19 prediction using human genomic data. Proceed 74 (1): 20.
  • Arslan H, Arslan H. 2021. A new covid-19 detection method from human genome sequences using cpg island features and knn classifier. Engin Sci Technol Inter J, 24(4): 839-847.
  • Arslan H, Aygun B. 2021. Performance analysis of machine learning algorithms in detection of covid-19 from common symptoms. Proceeding of 29th Signal Processing and Communications Applications Conference, June 9-11, Istanbul, Türkiye, pp: 1-4.
  • Arslan H. 2022a. Classification of SARS-CoV-2 Variants in Turkey. J Turkish Operat Manage, 6(1): 1092-1101 .
  • Arslan H. 2022b. Bagging and boosting for predicting mortality of patients with COVID-19. Dicle Univ J Engin, 13(2): 221-226.
  • Arslan H. 2023. A k-mer based metaheuristic approach for detecting COVID-19 variants. Dicle Univ J Engin, 14(1): 17-26.
  • Baj A, Novazzi F, Ferrante FD, Genoni A, Cassani G, Prestia M, Maggi F. 2021. Introduction of SARS-COV-2 c.37 (WHO VOI lambda) from Peru to Italy. J Medical Virol, 93(12): 6460–6461.
  • Bustin SA, Benes V, Garson JA, Hellemans J, Huggett J, Kubista M, Wittwer CT. 2009. The MIQE guidelines: Minimum information for publication of quantitative realtime PCR experiments. Clin Chem, 55(4): 611–622.
  • Cobb BR, Vaks JE, Do T, Vilchez RA. 2011. Evolution in the sensitivity of quantitative HIV-1 viral load tests. J Clin Virol, 52 S77–S82.
  • Davi MJP, Jeronimo SMB, Lima JPMS, Lanza DCF. 2021. Design and in silico validation of polymerase chain reaction primers to detect severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Sci Rep, 11(1): 12565.
  • Irudayasamy A, Ganesh D, Natesh M. 2022. Big data analytics on the impact of OMICRON and its influence on unvaccinated community through advanced machine learning concepts. Int J Syst Assur Eng Manag, 2022: 1-10.
  • Jain A, Rophina M, Mahajan S, Krishnan BB, Sharma M, Mandal S, Scaria V. 2021. Analysis of the potential impact of genomic variants in global sars-cov-2 genomes on molecular diagnostic assays. Inter J Infect Diseas, 102: 460-462.
  • Jamil S, Rahman M. 2021. A dual-stage vocabulary of features (vof)-based technique for covid-19 variants’ classification. Applied Sci, 11 (24): 11902.
  • Jiang X, Coffee M, Bari A, Wang J, Jiang X, Huang J, Huang Y. 2020. Towards an Artificial Intelligence Framework for Data-Driven Prediction of Coronavirus Clinical Severity. Comput Mater Contin, 62(3): 537–551.
  • Kent WJ, Sugnet CW, Furey TS, Roskin KM, Pringle TH, Zahler AM, Haussler D. 2002. The human genome browser at UCSC. Genome Res, 12(6): 996–1006.
  • Langer T, Favarato M, Giudici R, Bassi G, Garberi R, Villa F, Fumagalli R. 2020. Development of machine learning models to predict RT-PCR results for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in patients with influenza-like symptoms using only basic clinical data. Scandinavian J Trauma Resuscit Emerg Med, 28 (1): 113.
  • Lauring AS, Malani PN. 2021. Variants of SARS-CoV-2. JAMA, 326(9): 880-880.
  • Lopez-Rincon A, Tonda A, Mendoza-Maldonado L, Mulders DGJC, Molenkamp R, Perez-Romero CA, Kraneveld AD. 2021. Classification and specific primer design for accurate detection of SARS-CoV-2 using deep learning. Sci Reports 11(1): 947.
  • Lownik JC, Farrar JS, Way GW, McKay A, Roychoudhury P, Greninger AL, Martin RK. 2021. Fast sars-cov-2 variant detection using snapback primer high-resolution melting. Diagnostics, 11(10): 1788.
  • Mlcochova P, Kemp SA, Dhar MS. 2021. SARS-CoV-2 b.1.617.2 delta variant replication and immune evasion. Nature, 599(7883): 114–119.
  • Mohamadou Y, Halidou A, Kapen PT. 2020 November. A review of mathematical modeling artificial intelligence and datasets used in the study prediction and management of COVID- 19. Applied Intell, 50(11): 3913–3925.
  • Muhammad LJ, Algehyne EA, Usman SS, Ahmad A, Chakraborty C, Mohammed IA. 2021 February. Supervised machine learning models for prediction of Covid-19 infection using epidemiology dataset. SN Computer Sci, 2(1): 11.
  • NCBISD. 2021. National center for biotechnology information search database. URL: https://www.ncbi.nlm.nih.gov/ sars-cov-2/. (accessed date: December 04, 2021).
  • Nayar G, Seabolt EE, Kunitomi M, Agarwal A, Beck KL, Mukherjee V, Kaufman JH. 2021. Analysis and forecasting of global real time RT-PCR primers and probes for SARS-CoV-2. Sci Reports, 11 (1): 8988.
  • Ogiela MR, Ogiela U. 2021. Linguistic methods in healthcare application and COVID-19 variants classification. Neural Comput Applicat, 35: 13935–13940
  • Os´orio NS, Correia-Neves M. 2021. Implication of SARS-CoV-2 evolution in the sensitivity of RT-qPCR diagnostic assays. The Lancet Infect Diseas, 21(2): 166–167.
  • Park M, Won J, Choi BY, Lee CJ. 2020. Optimiza- 5 tion of primer sets and detection protocols for SARS-CoV-2 of coronavirus disease 2019 (COVID-19) using PCR and real-time PCR. Experiment Molec Med, 52(6): 963–977.
  • PCR Primer Stats. 2022. URL: https://www.bioinformatics.org/sms2/pcrprimerstats.html. (accessed date October 01, 2022).
  • Primer3plus. 2022. URL: https://www.bioinformatics.nl/cgi-bin/primer3plus/primer3plus.cgi. (accessed date October 01, 2022).
  • Rychlik W. 2007. OLIGO 7 primer analysis software. Humana Pressi In PCR primer design, New York, USA, pp: 35–59.
  • Sabino EC, Buss LF, Carvalho MPS. 2021. Resurgence of COVID-19 in manaus brazil despite high seroprevalence. The Lancet, 397(10273): 452–455.
  • Sahoo JP, Samal KC. 2021. World on alert: WHO designated south african new COVID strain (Omicron/B.1.1.529) as a variant of concern. Biotica Res Today, 3(11): 1086–1088.
  • Shi F, Wang J, Shi J, Wu Z, Wang Q, Tang Z, Shen D. 2021. Review of artificial intelligence techniques in imaging data acquisition segmentation and diagnosis for covid-19. IEEE Rev Biomedic Eng, 14 4-15.
  • Shu Y, McCauley J. 2017. GISAID: Global initiative on sharing all influenza data - from vision to reality. Eurosurveillance, 22(13): 30494.
  • Stothard P. 2000. The sequence manipulation suite: JavaScript programs for analyzing and formatting protein and DNA sequences. BioTechniques, 28(6): 1102–1104.
  • Tegally H, Wilkinson E, Giovanetti M. 2021 March. Detection of a SARS-CoV-2 variant of concern in South Africa. Nature, 592(7854): 438–443.
  • Togrul M, Arslan H. 2022. Detection of SARS-CoV-2 main variants of concerns using deep learning. Proceeding of Innovations in Intelligent Systems and Applications Conference, September 07-09, Antalya, Türkiye, pp: 32.
  • UCSC In-silico PCR. 2022. URL: https://genome.ucsc.edu/cgi-bin/hgPcr. (accessed date October 01, 2022).
  • Uriu K, Kimura I, Shirakawa K, Takaori-Kondo A, aki Nakada T, Kaneda A, Sato K. 2021. Neutralization of the SARS-CoV-2 mu variant by convalescent and vaccine serum. New England J Med, 385(25): 2397–2399.
  • Volz E, Mishra S, Chand M, Barrett J. 2021. Assessing transmissibility of SARS-CoV-2 lineage b.1.1.7 in England. Nature, 593(7858): 266–269.
  • Wink PL, Volpato FCZ, Monteiro FL, Willig JB, Zavascki AP, Barth AL, Martins AF. 2021. First identification of SARS-CoV-2 lambda (c.37) variant in southern Brazil. Infect Control Hospital Epidemiol, 2021: 1–2.
  • Xu Y, Ye W, Song Q, Shen L, Liu Y, Guo Y, Liu G, Wu H, Wang X, Sun X, Bai L, Luo C, Liao T, Chen H, Song C, Huang C, Wu Y, Xu Z. 2022. Using machine learning models to predict the duration of the recovery of COVID-19 patients hospitalized in Fangcang shelter hospital during the Omicron BA. 2.2 pandemic. In Frontiers Med, 9: 1001801.
  • Yaniv K, Ozer E, Shagan M, Lakkakula S, Plotkin N, Bhandarkar NS, Kushmaro A. 2021. Direct RT-QPCR assay for sars-cov-2 variants of concern (alpha b.1.1.7 and beta b.1.351) detection and quantification in wastewater. Environ Res, 201: 111653.
  • Zoabi Y, Deri-Rozov S, Shomron N. 2021. Machine learning-based prediction of COVID-19 diagnosis based on symptoms. NPJ Digital Medi, 4(1): 3.
There are 47 citations in total.

Details

Primary Language English
Subjects Virology
Journal Section Research Articles
Authors

Hilal Arslan 0000-0002-6449-6952

Rıza Durmaz 0000-0001-6561-778X

Early Pub Date October 3, 2023
Publication Date October 15, 2023
Submission Date July 9, 2023
Acceptance Date September 26, 2023
Published in Issue Year 2023

Cite

APA Arslan, H., & Durmaz, R. (2023). A Parallel Algorithm for Designing Primer and Probe for Accurate Detection of Severe Acute Respiratory Syndrome Coronavirus. Black Sea Journal of Engineering and Science, 6(4), 477-485. https://doi.org/10.34248/bsengineering.1324890
AMA Arslan H, Durmaz R. A Parallel Algorithm for Designing Primer and Probe for Accurate Detection of Severe Acute Respiratory Syndrome Coronavirus. BSJ Eng. Sci. October 2023;6(4):477-485. doi:10.34248/bsengineering.1324890
Chicago Arslan, Hilal, and Rıza Durmaz. “A Parallel Algorithm for Designing Primer and Probe for Accurate Detection of Severe Acute Respiratory Syndrome Coronavirus”. Black Sea Journal of Engineering and Science 6, no. 4 (October 2023): 477-85. https://doi.org/10.34248/bsengineering.1324890.
EndNote Arslan H, Durmaz R (October 1, 2023) A Parallel Algorithm for Designing Primer and Probe for Accurate Detection of Severe Acute Respiratory Syndrome Coronavirus. Black Sea Journal of Engineering and Science 6 4 477–485.
IEEE H. Arslan and R. Durmaz, “A Parallel Algorithm for Designing Primer and Probe for Accurate Detection of Severe Acute Respiratory Syndrome Coronavirus”, BSJ Eng. Sci., vol. 6, no. 4, pp. 477–485, 2023, doi: 10.34248/bsengineering.1324890.
ISNAD Arslan, Hilal - Durmaz, Rıza. “A Parallel Algorithm for Designing Primer and Probe for Accurate Detection of Severe Acute Respiratory Syndrome Coronavirus”. Black Sea Journal of Engineering and Science 6/4 (October 2023), 477-485. https://doi.org/10.34248/bsengineering.1324890.
JAMA Arslan H, Durmaz R. A Parallel Algorithm for Designing Primer and Probe for Accurate Detection of Severe Acute Respiratory Syndrome Coronavirus. BSJ Eng. Sci. 2023;6:477–485.
MLA Arslan, Hilal and Rıza Durmaz. “A Parallel Algorithm for Designing Primer and Probe for Accurate Detection of Severe Acute Respiratory Syndrome Coronavirus”. Black Sea Journal of Engineering and Science, vol. 6, no. 4, 2023, pp. 477-85, doi:10.34248/bsengineering.1324890.
Vancouver Arslan H, Durmaz R. A Parallel Algorithm for Designing Primer and Probe for Accurate Detection of Severe Acute Respiratory Syndrome Coronavirus. BSJ Eng. Sci. 2023;6(4):477-85.

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