¹ Grupo de Investigación Computo Avanzado y a Gran Escala - CAGE, Universidad Industrial de Santander, Bucaramanga, Santander, 680006, Colombia
² Grupo de Investigación en Demografía, Salud Pública y Sistemas de Salud - GUINDESS, Universidad Industrial de Santander, Bucaramanga, Santander, 680006, Colombia
³ Centro de Supercomputación y Cálculo Científico de la Universidad Industrial de Santander -SC3UIS, Universidad Industrial de Santander, Bucaramanga, Santander, 680006, Colombia

Cristian E. Cadena-Caballero
Roles: Conceptualization, Data Curation, Formal Analysis, Investigation, Methodology, Resources, Validation, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing

Lina M. Vera-Cala
Roles: Project Administration, Resources, Supervision, Validation, Visualization, Writing – Review & Editing

Carlos Barrios-Hernandez
Roles: Project Administration, Resources, Software, Supervision, Validation, Visualization, Writing – Review & Editing

Diego Rueda-Plata
Roles: Data Curation, Investigation, Methodology, Software, Validation

Lizeth J. Forero-Buitrago
Roles: Data Curation, Investigation

Carolina S. Torres-Jimenez
Roles: Data Curation, Investigation, Methodology

Erika Lizarazo-Gutierrez
Roles: Data Curation

Mayra Agudelo-Rodriguez
Roles: Data Curation, Investigation

Francisco Martinez-Perez
Roles: Conceptualization, Formal Analysis, Funding Acquisition, Investigation, Methodology, Project Administration, Resources, Supervision, Validation, Writing – Original Draft Preparation, Writing – Review & Editing

This study was approved by the Research Ethics Committees of the Industrial University of Santander, the Chicamocha Clinic, and the Chicamocha Clinical Laboratory (L3C) (Bucaramanga, Santander, Colombia). The study participants were hospitalized patients diagnosed with SARS-CoV-2, individuals presenting to the emergency room, or volunteers. All participants provided written informed consent and voluntarily participated in our study. Further, all participants were kept anonymous and were informed that the present study was conducted strictly for research purposes and that its outcomes were not intended to serve as treatment or diagnosis.

Nasopharyngeal swab samples were acquired by L3C personnel. Two samples were obtained per study participant, one for clinical diagnosis, as authorized by the Ministry of Health and Social Protection of Colombia, and the other for our study. The samples were placed in Universal Transport Medium (UTM) and stored at −80°C until required for downstream analyses (Rogers et al., 2020). Samples were collected over a one-week period and packaged in accordance with applicable Colombian health regulations (Ministry of Health and Social Protection, 2020) prior to transport and processing. All samples were transferred to the Central Research Laboratory of the Faculty of Health Sciences at the Industrial University of Santander (LCI-FS-UIS) and maintained at 4°C during handling and processing. In all cases, written informed consent was obtained prior to sample collection, and documentation was properly verified and archived in accordance with institutional and national ethical guidelines.

Because sample collection was not conducted under a predefined experimental sampling framework but was instead constrained by voluntary participation and ethical requirements during the COVID-19 pandemic, an anonymized coding system was implemented to ensure participant confidentiality, in accordance with Resolution 8430 of 1993 and the applicable Colombian ethical framework (Ministry of Health and Social Protection, 1993). The alphanumeric code consisted of two digits corresponding to the participant, followed by “CO” (Colombia) and a consecutive number assigned to each sample.

Subsequently, samples were aliquoted in a Class II, Type A2 biosafety cabinet (Thermo Fisher Scientific). A total of three aliquots were obtained per sample, including one 200 μL aliquot used immediately for total RNA extraction and two 650 μL backup aliquots stored at −80°C.

To account for reported mutations in SARS-CoV-2 genomes that could affect primer and probe binding and potentially generate false-negative results, a region encoding the accessory protein ORF8 was selected for analysis. This region was chosen based on its predicted mutation profile, and monthly consensus sequences were generated to assess its identity pattern across newly available genomes. Publicly available SARS-CoV-2 genome sequences were obtained from the GenBank and GISAID databases (RRID:SCR_002760; RRID:SCR_018251) (Sayers et al., 2021; Shu & McCauley, 2017). To gather data from the GenBank database, a Python script (RRID:SCR_008394) was executed in the GUANE-1 High Performance and Scientific Computing Center of the Industrial University of Santander (SC3UIS) through the Entrez Programming Utilities interface using the Biopython package (RRID:SCR_013249; RRID:SCR_007173) (Cock et al., 2009; Geer et al., 2010), whereas all GISAID data were manually downloaded. Genomes with uncharacterized regions/nucleotides were discarded using another Python script (RRID:SCR_008394), which was used to conduct monthly FASTA sequence alignments using the MAFFT software (RRID:SCR_011811) (Katoh & Standley, 2013) coupled with previously described DNA loss model parameters (Martínez-Pérez et al., 2002, 2007). The consensus sequences were generated using BioEdit version 7.2 (RRID:SCR_007361) with a 100% threshold frequency (Hall, 1999).

Using the SARS-CoV-2 (NC_045512) reference genome and consensus sequences from January to April 2020 (Zhu et al., 2020), an approximately 150-base pair (bp) region of the ORF8 gene was selected based on the secondary structure of its RNA transcript, which in turn was predicted using the algorithms proposed by Zuker (Zuker & Jacobson, 1998) via the Mfold software (RRID:SCR_001360) (Zuker, 2003). Thus, the last set containing the codons of the central region of the ORF8 gene, which encodes the secretion protein ORF8, was chosen because it allows proper viral adhesion to the host cell (Chan et al., 2020). The obtained sequence was used as a template to create primers, a TaqMan FAM-BBQ probe, and substrate oligos for RT-qPCR. The specificity of the aforementioned molecules was confirmed via GenBank BLAST analyses (RRID:SCR_004870) (Ye et al., 2006). Genes E and N from the Berlin and CDC protocols were used as controls (Biotek, 2020; Corman et al., 2020).

All molecules were synthesized by Bioneer (Korea). The primers were purified via separation on a reverse-phase cartridge, whereas the probe and substrate oligos were purified via high-performance liquid chromatography (HPLC) and polyacrylamide gel electrophoresis (PAGE), respectively. The ORF8 RT-qPCR conditions were implemented as described by the Berlin protocol (Corman et al., 2020) using the aforementioned molecules coupled with the 2019-nCoV TaqMan RT-qPCR Kit (Norgen Biotek Corp) developed by the CDC (Biotek, 2020).

A:T and G:C ratios were calculated based on the monthly SARS-CoV-2 consensus sequences to obtain an average for each nucleotide. These averages were then used to determine the minimum concentrations of TEA (ABCAM-USA), DMSO (Scharlab-Spain), and dNTPs (100 mM each, Promega-USA) in molecular-grade ultrapure water (Promega-USA), in addition to the MgSO₄ concentration recommended by the Berlin protocol.

Total RNA extraction was conducted using the MagMAX™ Viral/Pathogen II (MVP II) Nucleic Acid Isolation Kit (2000 RXNs) (Applied Biosystems-USA) using a KingFisher Duo Prime (5400110) DNA/RNA extraction system according to the manufacturer’s instructions (Thermo Fisher Scientific-USA) (Fang et al., 2007).

RT-qPCR was conducted using the ORF8-specific primers and probe designed herein, in addition to the E (Berlin protocol) and N genes (N1-N2; CDC protocol). The RNase P gene was used as an external control, as proposed by both of the aforementioned protocols. The reactions were conducted using the SuperScript III One-Step RT-qPCR System with Platinum Taq DNA Polymerase (Ref. 11732088; Invitrogen-USA) and the 2019-nCoV TaqMan RT-qPCR Kit (Ref. TM67100; Norgen-Biotek-Canada). Each reaction for each diagnostic system was conducted in either 15- or 25-μL reaction volumes consisting of 2 μL of patient-derived purified RNA, 2× One-Step RT-qPCR Master Mix, 2× nuclease-free buffer, and the respective primers/probe at the concentrations recommended by the CDC. Moreover, a denaturing stock solution was added to obtain a final concentration of 0.7% TEA, 0.2% DMSO, and 0.8 mMol MgSO₄. The dNTP reagent had a final concentration of 12 mMol dATP-dTTP, 10 mMol dCTP-dGTP, and 0.8 mMol MgSO₄. The reaction conditions were the following: 55°C for 15 minutes, 95°C for 3 minutes followed by 45 cycles of 95°C for 15 s and 58°C for 30 s for the SuperScript™ III One-Step RT-qPCR kit; and 95°C for 3 s followed by 55°C for 20 s for the 2019-nCoV TaqMan RT-qPCR kit. Fluorescence signals were quantified using a QuantStudio 1 Real-Time PCR System (No. A40427) in a 96-well 0.2 μL block (Thermo Fisher Scientific-USA).

The above-described procedure was conducted using two different Multiplex One-Step RT-qPCR protocols. In the first instance, the primers and probes for the E, ORF8, and N (N1) genes were mixed, whereas the other reaction was performed by mixing the N1 and N2 sets of the N gene. The RNase P gene was independently assessed in both cases. All reactions were performed as described above.

Single and multiplex RT-qPCR assays were compared based on Ct values obtained under identical experimental conditions. Comparative analyses were performed at both the individual primer/probe level and at the sample level, allowing evaluation of performance differences between assay formats.

To estimate sensitivity, specificity, Positive Predictive Value (PPV), and Negative Predictive Value (NPV) for the single and multiplex RT-qPCR assays, a custom script was implemented in R using the Caret package (https://github.com/GenomicUIS/Sensitivity-specificity-PPV-and-NPV-for-SARS-CoV-2.git) (Kuhn, 2008; R Core Team, 2020). All scripts and processed datasets used in these analyses are publicly available in the referenced repository.

Because this study was designed as an exploratory methodological study and not as a clinical diagnostic validation, performance estimates were derived from predefined Ct-based operational criteria applied uniformly across assays. For individual reactions, Ct values between 18 and 35 were considered within the operational positive range. Ct values outside this interval, including values >35 or <18, were conservatively classified as non-positive or indeterminate for this exploratory internal analysis.

Ct values were subsequently pooled and evaluated at the sample level. A sample was considered positive when at least one primer/probe set met the predefined operational Ct criterion. For multiplex RT-qPCR assays, the same Ct-based criteria were applied, but results were evaluated collectively at the sample level.

These metrics were used to compare assay behavior under the evaluated experimental conditions. Because no independent external clinical validation was available, and because access to additional clinical information was restricted by ethical and legal considerations, these estimates should not be interpreted as measures of absolute diagnostic accuracy.

A total of 19,317 genomes were retrieved from the GenBank database from January to October 2020 based on our search criteria, whereas 107,259 complete genomes were obtained from the GISAID database between January and December 2020. In both datasets, the number of available sequences increased progressively over time. However, the frequency of base substitutions in the monthly consensus sequences for the E, ORF8, and N genes was higher in the GISAID dataset compared to GenBank ( Figure 1).

The region of the E gene employed herein exhibited a 113 bp length, whereas the amplicons of the N1-N2 system of the N gene were 72 and 67 bp in length, respectively. All regions exhibited loop-bubble structures. A 154 bp region within the first half of the ORF8 gene also presented a loop-bubble structure similar to those observed in the E and N genes. The third loop of the N1 system and the second loop of the ORF8 gene were formed by four and seven canonical base pairings, respectively. These structures are comparable to scorpion-type primer or probe configurations used in RT-qPCR; however, such loop-bubble structures are typically formed by 2–6 canonical base pairings ( Figure 2). Table 1 summarizes the primer sequences used in this study.

A total of 49 samples were collected between October 25, 2020, and January 21, 2021, of which 22 tested positive and 27 tested negative. Samples collected in October were used to evaluate the detection of SARS-CoV-2 using the E, ORF8, and N gene targets by RT-qPCR ( Table 2, Figure 3). Based on the secondary structure and nucleotide composition analyses, which indicated an A:T ratio between 63% and 70% across consensus sequences, denaturing and dNTP solutions were incorporated into the reactions (see Methods for details). Under these conditions, changes in Ct values were observed compared to the commercial procedures, although variability was detected among samples ( Table 2, Figure 3).

A decrease in amplification performance was observed in samples collected from December onward. This effect was first detected in the E gene system, followed by the N2 system and subsequently ORF8. This pattern was evaluated using ten samples collected between December 14 and 21, 2020. The observed results were classified into three outcomes depending on the detection system: (1) positive results were obtained following the manufacturer’s protocol with the addition of the denaturing solution; (2) positive results were obtained only in the presence of the denaturing solution; and (3) positive results were obtained only in the absence of the denaturing solution ( Table 2).

Analysis of global SARS-CoV-2 mutation patterns using GenBank and GISAID databases showed that the number of mutations increased from March 2020 onward relative to the reference genome (GenBank accession: NC_045512.2). This trend was particularly evident in the quencher and forward primer regions of the ORF8 and N genes. The number of potential primer and probe combinations also increased over time, with a marked rise observed from November onward in the GISAID dataset. Higher values were observed in December. For example, the ORF8 system and the N1 set exhibited 3.4×10¹¹ and 1.7×10¹² possible combinations, respectively, whereas the N2 set and the E gene exhibited 1.4×10⁵ and 32 combinations ( Figure 1).

Multiplex RT-qPCR controls using the E, ORF8, and N (N1) systems with positive samples 02 and 03 produced amplification profiles consistent with detection. Similar patterns were observed in samples 05 and 06 collected in October 2020. Under the same reaction conditions, the addition of the denaturing solution was associated with decreased Ct values in samples collected in October, whereas increased Ct values were observed in samples collected in November. In contrast, the dNTP solution exhibited an opposite trend ( Table 3, Figure 4).

The influence of the denaturing and dNTP solutions was further evaluated using nine samples collected between December 4 and 7, 2020, and January 14 and 15, 2021, using the N1–N2 primers and probe described by the CDC. Six samples exhibited the same pattern described above, whereas three samples tested negative based on both amplification curves and Ct values ( Table 3, Figure 5). The multiplex RT-qPCR assay detected SARS-CoV-2 in samples collected between January 19 and 29, 2021 ( Table 3).

Under the predefined Ct-based classification framework, the denaturing solution was associated with higher apparent sensitivity values than the commercial solution in single RT-qPCR assays, whereas the commercial solution showed higher apparent specificity values. In multiplex RT-qPCR assays, no major differences in apparent sensitivity were observed between commercial and denaturing conditions.

In multiplex reactions, specificity values were 0.0 under both evaluated conditions in this dataset. Therefore, these metrics should be interpreted as exploratory descriptors of assay behavior under the defined experimental conditions rather than as evidence of clinical diagnostic performance. The dNTP conditions were not included in this analysis because the number of evaluable positive results was limited (Table 4).

Performance metrics were calculated using predefined Ct-based operational criteria for internal comparative analysis and should not be interpreted as externally validated diagnostic accuracy estimates.

This study has several limitations. The sample size was constrained by logistical factors and low participation rates during the pandemic, including reluctance to undergo additional sampling procedures (McElfish et al., 2021), as well as compliance with legal and ethical requirements. Consequently, only participants who provided informed consent were included.

Additionally, sample classification was based on Ct threshold criteria without independent external validation. Therefore, the reported performance metrics reflect comparative assay behavior under the evaluated conditions rather than absolute diagnostic accuracy.

These limitations should be considered when interpreting the results. Further studies incorporating larger datasets and additional validation approaches are required to assess the broader applicability of the proposed methodology. In addition, clinical characterization of participants, including symptom severity and extent of infection, was not available for analysis because this information was protected under the informed consent framework and applicable legal restrictions.

The authors would like to express their gratitude to the volunteers that participated in this study, as well as to the Central Research Laboratory of the Industrial University of Santander Health Faculty (LCI-FS-UIS), the Chicamocha Clinical Laboratory, the High Performance and Scientific Computing Center of the Industrial University of Santander (SC3UIS), the Vice-Rectory of Research and Extension of the Industrial University of Santander, and Ministerio de Ciencia, Tecnología e Innovación, MINCIENCIAS; invitation No. 1015, Mincienciaton. Contract 369-2020. We would also like to thank Dr. Francisco Mora at SciWrite Solutions for providing English editing.

•  Arakawa Y, Nishida Y, Sakanashi D, et al.: Clinical evaluation of a modified SARS-CoV-2 rapid molecular assay, ID NOW ^TM COVID-19 2.0. J. Infect. Chemother.2024; S1341–321X(24): 00073-4. Publisher Full Text
•  Aranha C, Patel V, Bhor V, et al.: Cycle threshold values in RT-PCR to determine dynamics of SARS-CoV-2 viral load: An approach to reduce the isolation period for COVID-19 patients. J. Med. Virol. 2021; 93(12): 6794–6797. Publisher Full Text
•  Andrews RJ, O’Leary CA, Tompkins VS, et al.: A map of the SARS-CoV-2 RNA structurome. NAR Genom. Bioinform. 2021; 3(2): 1–14. PubMed Abstract | Publisher Full Text
•  Biotek N: 2019-nCoV TaqMan RT-PCR Kit. 2020; (pp. 1–7). Reference Source
•  Cadena-Caballero CE, Vera-Cala LM, Barrios-Hernandez C, et al.: Denaturing and dNTPs reagents improve SARS-CoV-2 detection via single and multiplex RT-qPCR (version 1.0).2022. Publisher Full Text
•  Chan JF, Kok KH, Zhu Z, et al.: Genomic characterization of the 2019 novel human-pathogenic coronavirus isolated from a patient with atypical pneumonia after visiting Wuhan. Emerg. Microbes. Infect. 2020; 9(1): 221–236. Publisher Full Text
•  Chen Z, Azman AS, Chen X, et al.: Global landscape of SARS-CoV-2 genomic surveillance and data sharing. Nat Genet. 2022; 54: 499–507. PubMed Abstract | Publisher Full Text | Free Full Text
•  Cock PJA, Antao T, Chang JT, et al.: Biopython: freely available Python tools for computational molecular biology and bioinformatics. Bioinformatics. 2009; 25(11): 1422–1423. PubMed Abstract | Publisher Full Text
•  Corman V, Bleicker T, Brünink S, et al.: Diagnostic detection of 2019-nCoV by real-time RT-RCR. Carité Berlin. 2020: 1–13. Reference Source
•  Fang X, Willis RC, Burrell A, et al.: Automation of nucleic acid isolation on KingFisher magnetic particle processors. J. Assoc. Lab. Autom. 2007; 12(1): 195–201. Publisher Full Text
•  Geer LY, Marchler-Bauer A, Geer RC, et al.: The NCBI BioSystems database. Nucleic Acids Res. 2010; 38(suppl_1): D492–D496. PubMed Abstract | Publisher Full Text
•  Grobe N, Cherif A, Wang X, et al.: Sample pooling: burden or solution? Clin. Microbiol. Infect. 2021; 27(9): 1212–1220. Publisher Full Text
•  Hall TA: BioEdit: A User-Friendly Biological Sequence Alignment Editor and Analysis Program for Windows 95/98/NT. Nucleic Acids Symp. Ser. 1999; 41(1): 95–98.
•  Hammerling MJ, Yoesep DJ, Jewett MC: Single enzyme RT-PCR of full-length ribosomal RNA. Synth. Biol. 2020; 5(1): 1–9. PubMed Abstract | Publisher Full Text
•  Hasan MR, Sundararaju S, Manickam C, et al.: A novel point mutation in the N gene of SARS-CoV-2 may affect the detection of the virus by Reverse Transcription-Quantitative PCR. J. Clin. Microbiol. 2021; 59(4): 1–8. PubMed Abstract | Publisher Full Text
•  Huston NC, Wan H, Strine MS, et al.: Comprehensive in vivo secondary structure of the SARS-CoV-2 genome reveals novel regulatory motifs and mechanisms. Mol. Cell. 2021; 81(3): 584–598.e5. PubMed Abstract | Publisher Full Text
•  Jaeger LH, Nascimento TC, Rocha FD, et al.: Adjusting RT-qPCR conditions to avoid unspecific amplification in SARS-CoV-2 diagnosis. Int. J. Infect. Dis. 2021; 102(1): 437–439. PubMed Abstract | Publisher Full Text
•  Katoh K, Standley DM: MAFFT multiple sequence alignment software version 7: Improvements in performance and usability. Mol. Biol. Evol. 2013; 30(4): 772–780. PubMed Abstract | Publisher Full Text
•  Kevadiya BD, Machhi J, Herskovitz J, et al.: Diagnostics for SARS-CoV-2 infections. Nat. Mater. 2021; 20(5): 593–605. PubMed Abstract | Publisher Full Text
•  Kovarova M, Draber P: New specificity and yield enhancer of polymerase chain reactions. Nucleic Acids Res. 2000; 28(13): e70–e74. PubMed Abstract | Publisher Full Text | Free Full Text
•  Kuhn M: Building Predictive Models in R Using the caret Package. J. Stat. Softw. 2008; 28(5): 1–26. Publisher Full Text
•  Kudo E, Israelow B, Vogels CBF, et al.: Detection of SARS-CoV-2 RNA by multiplex RT-qPCR. PLoS Biol. 2020; 18(10): e3000867. PubMed Abstract | Publisher Full Text
•  Martín J, Tena N, Asuero AG: Current state of diagnostic, screening and surveillance testing methods for COVID-19 from an analytical chemistry point of view. Microchem. J. 2021; 167: 106305. PubMed Abstract | Publisher Full Text
•  Martínez-Pérez F, Becerra A, Valdés J, et al.: A possible molecular ancestor for mollusk APGWamide, insect Adipokinetic Hormone, and crustacean Red Pigment Concentrating Hormone. J. Mol. Evol. 2002; 54(6): 703–714. PubMed Abstract | Publisher Full Text
•  Martínez-Pérez F, Durán-Gutiérrez D, Delaye L, et al.: Loss of DNA: A plausible molecular level explanation for crustacean neuropeptide gene evolution. Peptides. 2007; 28(1): 76–82. PubMed Abstract | Publisher Full Text
•  McElfish PA, Purvis RS, James LP, et al.: Perceived barriers to COVID-19 testing. Int. J. Environ. Res. Public Health. 2021; 18(5): 2278. PubMed Abstract | Publisher Full Text | Free Full Text
•  Ministry of Health and Social Protection: Resolution 8430 of 1993: Scientific, technical and administrative standards for health research. Bogotá: Ministry of Health of Colombia; 1993. https://www.minsalud.gov.co/sites/rid/lists/bibliotecadigital/ride/de/dij/resolucion-8430-de-1993.pdf
•  Ministry of Health and Social Protection: Guidelines for the management of samples during the SARS-CoV-2 (COVID-19) pandemic in Colombia. Ministry of Health and Social Protection. 2020; 1(1): 1–14. Reference Source
•  Nalla AK, Casto AM, Huang M-LW, et al.: Comparative performance of SARS-CoV-2 detection assays using seven different primer-probe sets and one assay kit. J. Clin. Microbiol. 2020; 58(6): 1–6. PubMed Abstract | Publisher Full Text
•  Navarathna DH, Sharp S, Lukey J, et al.: Understanding false positives and the detection of SARS-CoV-2 using the Cepheid Xpert Xpress SARS-CoV-2 and BD MAX SARS-CoV-2 assays. Diagn. Microbiol. Infect. Dis. 2021; 100(1): 115334. PubMed Abstract | Publisher Full Text
•  Nur SM, Hasan MA, Al Amin M , et al.: Design of potential RNAi (miRNA and siRNA) molecules for Middle East Respiratory Syndrome Coronavirus (MERS-CoV) gene silencing by computational method. Interdisciplinary Sciences: Computational Life Sciences. 2015; 7(3): 257–265. PubMed Abstract | Publisher Full Text
•  R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing. 2020; Vienna, Austria. https://www.R-project.org/
•  Rangan R, Zheludev IN, Hagey RJ, et al.: RNA genome conservation and secondary structure in SARS-CoV-2 and SARS-related viruses: A first look. RNA. 2020; 26(8): 937–959. PubMed Abstract | Publisher Full Text
•  Rogers AA, Baumann RE, Borillo GA, et al.: Evaluation of transport media and specimen transport conditions for the detection of SARS-CoV-2 by use of Real-Time Reverse Transcription-PCR. J. Clin. Microbiol. 2020; 58(8): 1–5. PubMed Abstract | Publisher Full Text
•  Ruhan A, Wang H, Wang W, et al.: Summary of the Detection Kits for SARS-CoV-2 Approved by the National Medical Products Administration of China and Their Application for Diagnosis of COVID-19. Virol. Sin. 2020; 35(6): 699–712. PubMed Abstract | Publisher Full Text
•  Sayers EW, Cavanaugh M, Clark K, et al.: GenBank. Nucleic Acids Res. 2021; 49(1): D92–D96. PubMed Abstract | Publisher Full Text
•  Shu Y, McCauley J: GISAID: Global initiative on sharing all influenza data – from vision to reality. Eurosurveillance. 2017; 22(13): 2–4. PubMed Abstract | Publisher Full Text
•  Tahan S, Parikh BA, Droit L, et al.: SARS-CoV-2 E gene variant alters analytical sensitivity characteristics of viral detection using a commercial RT-PCR assay. J. Clin. Microbiol. 2021; 59(1): e0007521–e0007523. PubMed Abstract | Publisher Full Text
•  Wacker A, Weigand JE, Akabayov SR, et al.: Secondary structure determination of conserved SARS-CoV-2 RNA elements by NMR spectroscopy. Nucleic Acids Res. 2020; 48(22): 12415–12435. PubMed Abstract | Publisher Full Text
•  Wan Y, Shang J, Graham R, et al.: Receptor recognition by the novel Coronavirus from Wuhan: An analysis based on decade-long structural studies of SARS Coronavirus. J. Virol. 2020; 94(7): 1–9. PubMed Abstract | Publisher Full Text
•  WHO: CDC protocol of realtime RT-PCR for Influenza A (H1N1). World Health Organisation; 2009. Reference Source
•  WHO: Statement on the first meeting of the International Health Regulations (2005) Emergency Committee regarding the outbreak of novel coronavirus (2019-nCoV). World Health Organisation; 2020. Reference Source
•  Ye J, McGinnis S, Madden TL: BLAST: Improvements for better sequence analysis. Nucleic Acids Res. 2006; 34(Web Server): W6–W9. PubMed Abstract | Publisher Full Text
•  Zhu N, Zhang D, Wang W, et al.: A novel Coronavirus from patients with pneumonia in China, 2019. N. Engl. J. Med. 2020; 382(8): 727–733. PubMed Abstract | Publisher Full Text
•  Zuker M: Mfold web server for nucleic acid folding and hybridization prediction. Nucleic Acids Res. 2003; 31(13): 3406–3415. PubMed Abstract | Publisher Full Text | Free Full Text
•  Zuker M, Jacobson AB: Using reliability information to annotate RNA secondary structures. RNA. 1998; 4(6): 669–679. PubMed Abstract | Publisher Full Text | Free Full Text

Published: 02 May 2026, 11:331

Published: 28 Mar 2024, 11:331

Published: 18 Mar 2022, 11:331

Current Reviewer Status: ?

VERSION 3

PUBLISHED 02 May 2026

Revised

Reviewer Report 17 Jul 2026

Lina Silva-Trujillo, Oswaldo Cruz Institute, Rio de Janeiro, Brazil 

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Reviewer Report 05 May 2026

Juan Sebastian Quintero Barbosa, University of Virginia, Charlottesville, Virginia, USA 

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VERSION 2

PUBLISHED 28 Mar 2024

Revised

Reviewer Report 28 Mar 2026

Juan Sebastian Quintero Barbosa, University of Virginia, Charlottesville, Virginia, USA 

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VERSION 1

PUBLISHED 18 Mar 2022

Reviewer Report 16 May 2022

Andrew D. Beggs, Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, UK 

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