{"paper_id":"30e5832d-0a8d-4594-b199-27d10a14bf33","body_text":"Reducing Supply Chain Dependencies for Viral Genomic Surveillance: Get by with a Little HELP from \nCommercial Enzymes already in your Lab Freezer \nGanna Kovalenko1§#, Myra Hosmillo1, Chris Kent2, Kess Rowe1, Andrew Rambaut3, Nicholas J Loman2, Joshua \nQuick2, Ian Goodfellow1 # \n1Division of Virology, Department of Pathology, University of Cambridge, Cambridge, UK  \n2Institute of Microbiology and Infection, School of Biosciences, University of Birmingham, Birmingham, UK \n3Institute of Evolutionary Biology, University of Edinburgh, Edinburgh, UK \n \n#Co-corresponding authors. Address correspondence to: \nGanna Kovalenko kovalenk@hs.uci.edu  \nIan Goodfellow ig299@cam.ac.uk   \n§Present affiliation: Department of Population Health and Disease Prevention, University of California Irvine, Irvine, \nUSA \n \nABSTRACT  \nThe COVID-19 pandemic exposed vulnerabilities in global laboratory supply chains, disrupting genomic surveillance \nefforts essential to epidemic response. To address this challenge, we developed ARTIC HELP (Homebrew Enzymes for \nLibrary Preparation), a practical, open-source adaptation of the widely adopted ARTIC nanopore sequencing protocol \nfor viral genomic surveillance. We describe generic, cost -effective alternatives to all enzyme mixes used in tiling \nmultiplex RT-PCR amplification of the virus genome, and the nanopore native barcoding workflow, including end-prep \n(EP), barcode ligation (BL), and adapter ligation (AL), making it broadly applicable to any laboratory. Through \nsystematic evaluation, we identified a wild -type M -MLV reverse transcriptase and tw o types of proofreading DNA \npolymerases as effective alternatives when standard reagents are unavailable due to high cost or limited supply: B -\nfamily Pfu-based polymerases with a fused Sso7d DNA-binding domain, and blends combining A-family (Taq-based) \nand B-family (Pfu-based) polymerases. V alidation on clinical samples of SARS-CoV-2 and Norovirus GII confirmed \nthat the HELP workflow achieves genome coverage comparable to the ARTIC LoCost protocol. For SARS -CoV-2 \nsamples (Ct ≤28), the wild-type M-MLV RT combined with selected Pfu or A+B polymerases, along with optimised \nHELP mixes (EP, BL, AL), achieved genome coverage of 84.0–99.6%. For Norovirus GII (Ct ≤32), the HELP workflow \nusing one of the Pfu polymerases achieved genome coverage of >85% for six out of  eight genotypes tested. Notably, \nseveral of the other polymerases tested showed reduced performance at higher Ct values. However, they still achieved \nstrong coverage at Ct <24, supporting their use as emergency alternatives in rapid outbreak-response sequencing when \nviral input is high and RNA quality is sufficient. Our approach, ARTIC HELP , provides a framework which can be \nimplemented to address supply chain disruptions, while maintaining robust genomic sequencing capabilities. A cost \nanalysis highlights the well-known significant global disparities in reagent pricing, driven not by protocol differences \nbut by import fees and supply barriers. Thus, our findings highlight the need for fairer global pricing models and support \nfor local sourcing strategies like HELP, to promote equity in genomic research and ensure preparedness for future public \nhealth challenges.   \n \n \nIntroduction  \nWhen the West African Ebola outbreak hit in 2014, sequencing a virus in real time, let alone in on -site settings, was \neffectively unprecedented in outbreak settings ( 1). That crisis marked a turning point, driving the development of \ngenomics-informed approaches to global pathogen surveillance systems that have since reshaped public health responses \nto emerging infectious disease thr eats ( 2, 3 ). The COVID -19 pandemic reinforced this shift, catalysing a wave of \ninvestment in global sequencing infrastructure and prompting the World Health Organization to launch a 10 -year \nstrategy (2022–2032) to expand genomic surveillance capacity world wide (4-6). These efforts laid the foundation for \nintegrating genomics into routine public health practice, supporting timely detection and response to infectious disease \noutbreaks. \nOne of the most influential efforts that emerged in response to this shift was the ARTIC Network, the first coordinated \ninitiative to deliver end-to-end, on-site deployable protocols for portable genomic surveillance (https://artic.network/ ; \nhttps://community.artic.network/t/a-beginners-guide-to-artic/531). Launched during the Ebola outbreak and aligned \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted June 12, 2025. ; https://doi.org/10.1101/2025.06.11.658579doi: bioRxiv preprint \n\nwith the rise of Oxford Nanopore Technology (ONT) sequencing, A RTIC has since developed tools for rapid viral \ngenome sequencing and analysis, enabling real-time response in real-world outbreak settings. The network has played \na pivotal role in responses to Zika, SARS -CoV-2, and more recently, Monkeypox ( 7-9). Drawing on experience from \nEbola and Zika, the ARTIC team rapidly designed and released a whole-genome sequencing protocol for SARS-CoV-2 \nat the onset of the pandemic. The ARTIC primers, based on an amplicon tiling scheme, became the backbone of SARS-\nCoV-2 genomic surveillance, contributing to over 18 million genome sequences across platforms and enabling timely \nvariant detection and public health response (10-13). To support broader adoption and higher-throughput use, the ARTIC \nLoCost workflow was introduced, reducing reagent volumes and lowering costs ( 14, 15). This cost-effective protocol \nhas been widely implemented in laboratories worldwide. More recently, adaptations of the ARTIC workflow have also \nbeen applied to wastewater surveillance and other viral targets , demonstrating its flexibility and ongoing relevance in \npublic health genomics (13, 16).  \nYet the pandemic also exposed the limitations of even widely adopted protocols, revealing structural weaknesses in \nglobal diagnostic and genomic surveillance systems , particularly their reliance on protocols that depend on specific \nreagents without validated alternatives. This lack of flexibility made laboratory operations highly vulnerable to supply \nchain disruptions, leading to shortages and delays in essential reag ents, enhanced by transportation restrictions and \nincreased competition for resources (17, 18), making even widely used protocols such as ARTIC difficult to sustain. To \nremain effective in future outbreaks, genomic workflows must be not only technically ro bust but also adaptable to \nresource constraints and resilient in the face of supply chain instability. While several high -level strategies have been \nproposed to improve resilience in health -related supply chains, such as supply diversification, local sourc ing, and \nimproved logistics, these efforts primarily focus on procurement and distribution systems, rather than the design of \nlaboratory workflows themselves (17). Our work addresses this under -served layer by filling a precise, practical gap: \nthe need for validated, protocol-level flexibility in genomic surveillance workflows. \nIn response to these challenges, we developed and validated the ARTIC HELP workflow (Homebrew Enzymes for \nLibrary Preparation) as a flexible alternative to the ARTIC LoCost protocol. We designed HELP to include open-\nsource substitutes for all key enzymatic steps in the native barcoding workflow (ONT), including end-repair, barcode \nligation, and adapter ligation, making the workflow broadly applicable to any laboratory using nanopore sequencing. \nRather than aiming to replace commercial enzyme mixes entirely, we focused on providing practical alternatives that \nuse enzymes and buffers commonly found in standard molecular biology laboratories. We validated the HELP \nworkflow on clinical samples of SARS-CoV-2 and Norovirus GII, confirming its performance under real-world \nconditions. By offering reliable substitutes for critical reagents, we aim to strengthen the resilience and continuity of \npathogen genomic surveillance during public health crises. \n \nMaterials and methods \nVirus culture \nLive SARS-CoV-2 (SARS-CoV-2/human/Liverpool/REMRQ001/2020) was cultured in Vero-E6 (A TCC) cells grown \nin Dulbecco’s Modified Eagle Medium (Pan Biotech) supplemented with 1% Glutamine (Gibco), 10% foetal calf serum, \n100 U/ml penicillin and 100 μg/ml streptomycin (Thermo Scientific), at 37 °C with 5% CO2.  \nViral RNA extraction \nSARS-CoV-2 control panel RNA was generated using the isolate SARS -CoV-2/human/Liverpool/REMRQ001/2020. \nFollowing SARS-CoV-2 live virus inoculation, infected cells were collected by cent rifugation at 12000x g for 5 mins. \nCells were lysed in lysis buffer containing 4M guanidine isothiocyanate, 1% beta -mercaptoethanol and 2% Triton X-\n100 for 10 mins at room temperature. After lysis, samples were centrifuged through a QIAshredder homogenizer  and \nethanol was then added to the cleared lysates. Total RNAs were isolated and purified using the Sigma GenElute protocol, \nand the viral RNA was eluted with 60 μl of RNase-free water.  \nStool specimens were originally obtained and anonymized with written consent from patients at Addenbrooke’s Hospital \nin Cambridge, United Kingdom, who tested positive for HuNoV infection. These samples were collected under the \nethical approval (REC -12/EE/0482) for a previous study ( 19). Each specimen was diluted 1:5 or 1:10  (wt/vol) with \nphosphate-buffered saline (PBS) depending on the water content of the stool sample. Diluted samples were then vortexed \nvigorously for 30 sec and centrifuged for 5 min at 8000 x g at room temperature. Following centrifugation, 200 μl \naliquots of the supernatants were collected for total RNA extraction or immediately frozen at -80C until required. RNA \nwas extracted from the samples using the Sigma GenElute protocol using 700 μl of guanidine isothiocyanate (GITC)-\n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted June 12, 2025. ; https://doi.org/10.1101/2025.06.11.658579doi: bioRxiv preprint \n\ncontaining buffer with 1% beta-mercaptoethanol per 200 μl of diluted stool samples. The RNA was purified according \nto the manufacturer’s instructions and the viral RNAs were eluted with 30 μl of RNase-free water.  \nRT-qPCR for the detection and quantification \nViral RNA levels in all samples were confirmed immediately prior to use. Ct-values were determined by RT-qPCR for \nSARS-CoV-2 using a standard diagnostic workflow for 2019 -nCoV screening (20) and for Norovirus GII ( 21). A ten-\nfold serial dilution of in vitro transcribed RNA was used to generate a standard curve and determine the absolute copy \nnumber of viral RNA. \ncDNA synthesis and multiplex PCR  \nDetailed master mix recipes and a full list of reagents including catalog numbers used in the HELP study are provided \nin the Extended Data (Table S1 and Table S2). The ARTIC LoCost sequencing protocol for SARS-CoV-2 v3 was used \nas the baseline (15).  \nM-MLV reverse transcriptase (Promega) was explored as an alternative to the standard  LunaScript RT (NEB) used in \nthe ARTIC LoCost sequencing protocol. For cDNA synthesis, a reaction comprising of random hexamers (Invitrogen), \ndNTP Mix (Thermo Fisher), RNase OUT (Invitrogen), and 12 µL of template RNA was prepared (Extended data: Table \nS1). The RT reaction was carried out under the following conditions: 25°C for 5 min, 42°C for 50 min and 70°C for 10 \nmin. Six DNA polymerases were used alongside the standard Q5 Hot Start High-Fidelity DNA Polymerase (NEB), their \ncharacteristics are summarised in Extended data Table S3. These included: 1) (Platinum) Pl atinum SuperFi DNA \nPolymerase (Invitrogen); 2) (PrimeSTAR) PrimeSTAR GXL DNA Polymerase (TAKARA); 3) (KAPA) KAPA Taq \nExtra HotStart ReadyMix PCR Kit (KAPAbiosystems); 4) (EcoDry) High Fidelity PCR EcoDry Premix (TAKARA); 5) \n(Phusion) Phusion High-Fidelity DNA Polymerase (Thermo Scientific); 6) (KOD) KOD Hot Start DNA Polymerase \n(Sigma-Aldrich). Multiplex amplicon -based PCR was run using artic -sars-cov2/400/v4.1.0  ( 22) and norovirus -\ngii/800/v1.1.0 (23) primer schemes. Then 2.5 µL of cDNA was added to the PCR reactions for each pool, bringing the \ntotal reaction volume to 25 µL ( Extended data:  Table S1). Samples were amplified under the same PCR cycling \nconditions, adapted from the LoCost protocol ( 15): heat activation at 98°C for 30 seconds, followed by 30 cy cles of \n95°C for 15 seconds and 63°C for 5 minutes. The PCR reactions were then pooled together, purified, and quantified \nfollowing the LoCost protocol before end-prep. Briefly, 5 µL of each PCR reaction from each pool were combined in \n40 µL of nuclease -free water (NFW) and purified using a 1:1 ratio of PCRClean DX magnetic beads (Aline \nBioSciences), then quantified using the Qubit dsDNA High Sensitivity assay (Life Technologies) on the Qubit Flex \nfluorometer. In some instances, gel electrophoresis (1% agarose) was performed to analyse the PCR products generated \nby each condition. \nLibrary preparation (end-prep, barcode and adapter ligation) \nEnd-prep (EP). The ends prep reaction incorporates a number of enzymatic reactions; the ends of the amplicons are first \npolished using the Klenow fragment of DNA Polymerase I (Klenow) (NEB) and T4 DNA Polymerase (NEB), then \nphosphorylated and adenylated using T4 Polynucleotide Kinase (T4 PNK) (NEB) and Taq DNA Polymerase (Thermo \nScientific), respectively. Three options of HELP EP mixes were tested (A, B and C), which varied in the concentrations \nof enzymes described in more detail in the text ( Extended data: Table S1). All mixes included 1X T4 DNA Ligase \nReaction Buffer (50 mM Tris-HCl, 10 mM MgCl2, 1 mM A TP, 10 mM DTT), 0.5 mM dNTPs, and 5% PEG-8000 for \nefficiency. Reactions contained 3.3 µL of amplicons, with NFW to a final volume of 10 µL. The reaction was incubated \nfor 30 minutes at 20°C, 30 minutes at 65°C, and finally cooled on ice.  \nBarcode Ligation (BL): The HELP BL master mixes were developed as alternatives to the NEBNext® Ultra ™ II \nLigation Module and Blunt/TA Ligase Master Mix (NEB), and are detailed in Extended Data Table S1. Five formulations \n(A–E) were prepared using T4 DNA Ligase at 1000 U per reaction, along with ligation enhancers including hexamine \ncobalt chloride (HCC), 1,2 -Propanediol (1,2-PrD), PEG-8000, and two concentration options of the T4 DNA Ligase \nReaction Buffer (NEB). The native barcoding expansion kit (EXP-NBD196, ONT) was used, with barcodes diluted in \na ratio of 1.4:1 with NFW,  prior to use (NFW:barcode). Subsequently, 3 µL of the diluted barcodes were utilized per \nreaction. For each reaction, 1.5 µL of end-prepared amplicons from the EP step were subjected to barcode ligation. NFW \nwas added to adjust the reaction volume to 10 µL, and the reaction was incubated for 30 minutes at 20°C, 10 minutes at \n70°C, and then cooled on ice. \nAdapter Ligation (AL): Following barcoding, individual reactions were pooled, purified using a 0.4× volume of \nPCRClean DX magnetic beads, and quantified by Qubit, in line with the ARTIC LoCost protocol. Adapter ligation was \nthen performed using alternative HELP AL mixes developed in place of the NEBNext® Quick Ligation Module (NEB) \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted June 12, 2025. ; https://doi.org/10.1101/2025.06.11.658579doi: bioRxiv preprint \n\n(Extended data: Table S1). The AL reaction was set up in 2X T4 DNA Ligase Reaction Buffer (100 mM Tris -HCl, 20 \nmM MgCl₂, 2 mM A TP, 20 mM DTT, pH 7.5) and 10 % of PEG-8000. Two HELP AL formulations were tested: HELP \nAL-A (4000 U T4 Ligase/reaction) and HELP AL-B (2000 U T4 Ligase/reaction). Each reaction included 30 μl of the \nbarcoded amplicon pool and 5 μl of Adapter Mix II (AMII, EXP-NBD196, ONT), with the final volume adjusted to 70 \nμl using NFW. For comparison, a parallel adapter ligation using the commercial Rapid DNA Ligation Kit (Thermo \nScientific) was set up in a 50 μl reaction volume. All ligation reactions were incubated at room temperature for 20 \nminutes. Libraries were then cleaned using a 1:1 ratio of PCRClean DX beads and quantified according to the LoCost \nprotocol. \nSequencing and Bioinformatics workflow \nFinal libraries were generated using the Ligation Sequencing Kit (SQK-LSK109, ONT), loaded onto FLO-MIN106 flow \ncells on a GridION device (ONT) and sequenced using the MinKNOW software using real-time basecalling with a high \naccuracy model. Demultiplexing was conducted using the barcode-both-ends option and read filtering based on quality \nscore 9. The ARTIC nCoV-2019 novel coronavirus bioinformatics protocol using reference -based medaka workflow \nwas used to process the output into conse nsus genome sequences (24). Genome regions with depth of <20× coverage \nwere masked and represented with N characters. The sequencing results were additionally analysed using ncov -tools \n(25). For Norovirus GII sequences the ARTIC field bioinformatics pipeline was applied using reference-based medaka \nworkflow (26). The closest reference genomes were selected using Rampart ( 27). Norovirus sequences were analysed \nwith the RIVM Norovirus Typing Tool v.2.0 ( 28). Data plots and heatmaps were generated using Jupyte r Notebook \n(https://jupyter.org/). \n  \nResults \nThe study was conducted in three stages (Figure 1). In Stage 1, we systematically replaced reagents at each step of the \nARTIC LoCost sequencing protocol across six workflows (wf), each targeting one of the five experimental steps: RT, \nPCR, and the library preparation steps (EP, BL, and AL). This approach aimed to identify viable reagent alternatives. In \nStage 2, we determined the optimal replacements within a single library workflow, ensuring the new components worked \nseamlessly together. Sequencing performance in Stages 1 and 2 was tested across four SARS-CoV-2 RNA concentrations \nfrom the lab-grown isolate (SARS-CoV-2/human/Liverpool/REMRQ001/2020), corresponding to Ct values 21.2, 24.6, \n27.9, and 31.4, equating to 2.2x10^4, 2.01x10^3, 1.83x10^2, and 1.65x10^1 RNA copies/reaction, respectively. Finally, \nStage 3 validated the best replacements using clinical samples of SARS-CoV-2 and Norovirus GII (with Ct-values <33), \nconfirming their effectiveness in real -world applications. HELP workflows from Stage 3 are available on protocol.io \nhttps://protocols.io/view/artic-help-protocol-for-amplicon-based-viral-genom-gzsibx6cf. Performance metrics included \nread count, percentage of mapped reads, mean and medium depth, genome coverage, and amplicon dropouts (regions \nwith ≤20x depth), detailed in Extended data Tables S4–S6.  \n \nFigure 1. Schematic representation of the HELP study stages.  \nStage 1 highlights the six workflows (wf) designed to assess the efficacy of generic enzyme replacements across the various \nenzymatic steps in the ARTIC LoCost workflow. Workflow 1 (wf -1) represents the r eference ARTIC LoCost protocol where all \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted June 12, 2025. ; https://doi.org/10.1101/2025.06.11.658579doi: bioRxiv preprint \n\ncommercial mixes are used. In workflows 2 to 6 (wf-2 to wf-6), reagents were systematically replaced at each enzymatic step in the \nlibrary preparation process using generic equivalents (highlighted in orange and described in more detail in the text). Stages 2 and \n3 represent the evaluation of the most effective replacements identified in Stage 1 within a single library preparation workflow. \n \nStage 1: Systematic replacement of the core reagents in each step of the ARTIC sequencing workflow  \nWorkflow 1 (wf-1) followed the ARTIC LoCost protocol using commercial mixes from NEB, while workflows wf-2 to \nwf-6 systematically tested alternative enzyme mixes at each step of library preparation. Figure 2 summarizes this \ncomparison, showing genome coverage across four Ct values using a SARS-CoV-2 isolate. In wf-2, LunaScript RT was \nreplaced with M -MLV RT, while the remaining steps utilised reagents from th e LoCost protocol. In wf -3, Q5 DNA \nPolymerase was replaced with one of six alternatives: Platinum, PrimeSTAR, KAPA, EcoDry, Phusion, or KOD. By gel \nelectrophoresis, we assessed the amplification efficiency from wf -1, wf-2 and wf -3 (Extended data: Figure S1) and \nfound that while all polymerases generated expected products, amplification efficiency varied. KOD polymerase was \nparticularly inefficient and excluded from further experiments. We also noted that EcoDry, PrimeSTAR, and KAPA \nproduced longer (~1,000 bp) chimeric products in samples with lower Ct values, a known artifact in PCR amplifications \n(29). \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted June 12, 2025. ; https://doi.org/10.1101/2025.06.11.658579doi: bioRxiv preprint \n\n \nFigure 2. Stage 1: Summary of workflow comparisons. \nFlowchart summarising the workflow comparison for library preparation, evaluating alternative enzyme mixes at key steps in th e \nLoCost protocol. Genome coverage percentages obtained using SARS -CoV-2 isolate across four Ct values (21.2, 24.6, 27.9, and \n31.4) is shown for each workflow. The reference ARTIC LoCost protocol (wf-1) is represented by the reagents in dark green boxes, \nand the % genome coverage obtained is listed first for each step as the reference. Alternative workflows (wf -2 to wf -6) are \nhighlighted in orange, corresponding to specific enzymatic steps tested in reverse transcription, PCR, end prep, barcode ligation, \nand adapter ligation. \n \nReplacing LunaScript with M-MLV RT combined with Q5 polymerase (wf-2) delivered comparable performance to the \nARTIC LoCost protocol, except at the highest Ct value (31.4), where M -MLV showed reduced genome coverage \n(91.3%) and 10 amplicon dropouts (Figures 2 and 3). In contrast, the LoCost workflow (LunaScript) maintained 97.1% \ncoverage with only three dropouts, h ighlighting the importance of RT enzyme selection. Nonetheless, M -MLV RT \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted June 12, 2025. ; https://doi.org/10.1101/2025.06.11.658579doi: bioRxiv preprint \n\ndemonstrated viability as an effective alternative, achieving comparable genome coverage and sequencing depth under \nmost conditions. \n \nFigure 3. Stage 1: Comparison of M-MLV RTase and alternative polymerases to the LoCost protocol. \n(A) Bar plots comparing the performance of M -MLV RTase (wf-2) and five polymerases,Platinum, PrimeSTAR, KAPA, EcoDry , \nand Phusion (wf-3),against the LoCost protocol (wf-1) across four key metrics: Read Count, % of Mapped Reads, Mean Depth, and \nAmplicon Dropouts. Evaluations were conducted using SARS-CoV-2 isolate RNA at Ct values of 21.2, 24.6, 27.9, and 31.4. (B) \nHeatmap illustrating amplicon depth coverage across the same Ct values. The text on the y-axis lists the M-MLV RTase (wf-2) and \nthe various polymerases (wf-3) evaluated in the study . The x-axis text highlight the amplicon IDs (1 to 99) from the SARS-CoV-2 \ngenome targeted by the ARTIC primer scheme v4.1. Colour gradient: red (low values) indicates poor or no coverage (depth ≤ 20), \nrepresenting amplicon dropouts. \n \nIn wf-3, where polymerases were compared, Platinum and EcoDry demonstrated the most consistent performance, \nclosely matching the LoCost workflow with minimal dropout rates (Figures 2 and 3 ; Extended data:  Table S4). \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted June 12, 2025. ; https://doi.org/10.1101/2025.06.11.658579doi: bioRxiv preprint \n\nSpecifically, Platinum was the most comparable, achieving high genome coverage (97.3% to 99.6%) with only three \namplicon dropouts. In contrast, under the conditions used, PrimeSTAR and Phusion were less reliable, with lower \ncoverage (76.2% and 74.1%, respectively) at the highest Ct 31.4. We noted that the amplicons which failed to amplify \nefficiently varied between polymerases, with Platinum consistently exhibiting dropout of amplicon 90 and EcoDry \nprimarily at amplicon 82 (Figur e 2B). Amplicons such as 21, 22, 51, 60, and 66 frequently appeared as problematic \nareas across multiple conditions (including M -MLV RT with Q5 polymerase), particularly with high Ct samples. The \nresults of wf-3 clearly demonstrated that Platinum and EcoDr y polymerases can reliably substitute for Q5 polymerase \nin the LoCost workflow, maintaining high and stable genome coverage (96-99%) across all Ct values.  \nIn wf-4, we replaced the commercial end-prep enzyme mix with generic HELP-EP mixes (A, B, and C), combining T4 \nPNK, T4 DNA Polymerase, Klenow, and Taq DNA Polymerase in a single reaction for amplicon end repair (Extended \ndata: Table S1). HELP EP-A used the highest enzyme concentrations: 0.2 U/µl T4 PNK, 0.01 U/µl Klenow, 0.02 U/µl \nT4 Pol, and 0.04 U/µl T aq Pol. While HELP EP -B included 2 -fold reductions in all enzymes except Klenow, which \nremained constant. HELP EP-C mirrored EP-A in enzyme concentration, but excluded Klenow to test whether T4 DNA \nPolymerase alone could support efficient end-preparation. Buffer components, 1X T4 DNA Ligase Reaction Buffer (1 \nmM A TP), 5% PEG-8000, and 0.5 mM dNTPs, were consistent across all HELP -EP mixes. As with wf1 -3, all other \nsteps of the sequencing workflow were maintained as per the LoCost protocol and the sequencing performance of each \nHELP-EP mix was compared using a dilution series of RNA extracted from the lab-grown SARS-CoV-2 isolate. HELP \nEP-C, excluding Klenow, was the most comparable to the LoCost protocol, achieving consistent genome coverage >95% \nacross all Ct values (Figures 2 and 4; Extended data: Table S4). In contrast, HELP EP-A and HELP EP-B, while effective \nat lower Ct values, showed slightly reduced performance at higher Ct values (31.4), with genome coverage of 93.8%. \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted June 12, 2025. ; https://doi.org/10.1101/2025.06.11.658579doi: bioRxiv preprint \n\n \nFigure 4. Stage 1: Comparison of HELP end-prep mixes to the LoCost protocol. \n(A) Bar plots comparing the performance of three HELP end-prep mixes (EP-A, EP-B, and EP-C) (wf-4) against the LoCost protocol \n(wf-1) across four key metrics: Read Count, % of Mapped Reads, Mean Depth, and Amplicon Dropouts. Evaluations were conducted \nusing a SARS-CoV-2 isolate at Ct values of 21.2, 24.6, 27.9, and 31.4. (B) Heatmap illustrating amplicon depth coverage across the \nsame Ct values. The y-axis lists the HELP EP mixes evaluated in the study . The x-axis text represents amplicon IDs (1 to 99) from \nthe SARS-CoV-2 genome targeted by the ARTIC primer scheme v4.1. Colour gradient: red (low values) indicates poor or no \ncoverage (depth ≤ 20), representing amplicon dropouts. \n \nIn wf-5, we evaluated five barcode ligation mixes (HELP BL, A–E), alongside a commercial DNA Ligation Kit, Mighty \nMix (TAKARA) (Figures 2 and 5; Extended data: Table S4). Each HELP mix contained a consistent concentration of \nT4 DNA Ligase (1000 U/reaction) but differed in ligation enhancer supplements, including one or combinations of PEG-\n8000, 1,2-PrD, and HCC. HELP BL-D, which included both 10% PEG-8000 and 1 mM HCC, performed identically to \nthe LoCost, achieving high genome coverage (>97%) across all Ct val ues. HELP BL-A (10% PEG-8000) and HELP \nBL-E (10% PEG -8000 with 2X Ligase buffer) also performed well, achieving genome coverage >95%. In contrast, \nHELP BL-B (15% PEG-8000) and HELP BL-C (10% PEG-8000 with 12% 1,2-PrD) had high genome coverage (>99%) \nat lower Ct values, but dropped to 93.8% at Ct 31.4. In contrast to the generic enzyme mixes, under the conditions used, \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted June 12, 2025. ; https://doi.org/10.1101/2025.06.11.658579doi: bioRxiv preprint \n\nMighty Mix consistently showed the lowest performance across all metrics, with genome coverage dropping from 99.6% \nat Ct 24.6 to 97% at Ct 21.2 and 27.9, and further declining to 87.6% with 14 amplicon dropouts at Ct 31.4.  \n \nFigure 5. Stage 1: Comparison of HELP barcode ligation mixes to the LoCost protocol. \n(A) Bar plots comparing the performance of five HELP barcode ligation mixes (BL-A to BL-E) and a commercial Ligation Mighty \nMix (TAKARA) (wf-5) against the LoCost protocol (wf-1) across four key metrics: Read Count, % of Mapped Reads, Mean Depth, \nand Amplicon Dropouts. Evaluations were conducted using SARS -CoV-2 isolate at Ct values of 21.2, 24.6, 27.9, and 31.4.  (B) \nHeatmap illustrating amplicon depth coverage across the same Ct values. The y-axis lists the HELP BL mixes evaluated in the study . \nThe x-axis represents amplicon IDs (1 to 99) from the SARS -CoV-2 genome targeted by the ARTIC primer scheme v4.1. Colour \ngradient: red (low values) indicates poor or no coverage (depth ≤ 20), representing amplicon dropouts. \n \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted June 12, 2025. ; https://doi.org/10.1101/2025.06.11.658579doi: bioRxiv preprint \n\nIn wf-6, we evaluated alternatives to the NEBN ext Quick Ligation Module for adapter ligation in the ARTIC LoCost \nprotocol by testing the HELP AL-A mix and Thermo Rapid DNA Ligation Kit (RLK, Thermo). The HELP AL-A mix \nwas prepared with a final concentration of 2X T4 DNA Ligase Reaction Buffer, enhance d with 10% PEG-8000 and a \nhigh concentration of T4 DNA Ligase (4000 U/reaction). Both mixes showed performance comparable to the ARTIC \nLoCost workflow and proved to be reliable options for sequencing (Figures 2 and 6; Extended data: Table S4). \n \nFigure 6. Stage 1: Comparison of HELP adapter ligation mixes to the LoCost protocol. \n(A) Bar plots comparing the performance of the HELP adapter ligation mix and the commercial Thermo Rapid DNA Ligation Kit \n(RLK) (wf-6) against the LoCost protocol (wf -1) across four key metrics: Read Count, % of Mapped Reads, Mean Depth, and \nAmplicon Dropouts. Evaluations were conducted using SARS-CoV-2 isolate at Ct values of 21.2, 24.6, 27.9, and 31.4. (B) Heatmap \nillustrating amplicon depth coverage across the same Ct values. The y-axis lists the mixes evaluated for adapter ligation (wf-6) in \nthis study . The x-axis represents amplicon IDs (1 to 99) from the SARS-CoV-2 genome targeted by the ARTIC primer scheme v4.1. \nColour gradient: red (low values) indicates poor or no coverage (depth ≤ 20), representing amplicon dropouts. \n \nStage 2. Comparative Analysis of HELP Workflows Relative to the ARTIC LoCost Workflow \nWe next evaluated the performance of the generic replacements identified in Stage 1, within a single-library workflow, \nselecting only those that performed well in the initial screen. M -MLV served as a generic RT replacement, and for the \nPCR step, we selected Platinum and EcoDry polymerases due to their highest efficiency under the conditions used. For \nend prep, we selected HELP EP-A and EP-C mixes, which differed only by the inclusion of the Klenow enzyme in the \nEP-A. For barcode ligation, we selected HELP BL -A (10% PEG-8000) as the simplest recipe and BL -D (10% PEG -\n8000 and 1mM HCC) as the best performing mix. In the adaptor ligation step, we tested AL-A and AL-B which varied \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted June 12, 2025. ; https://doi.org/10.1101/2025.06.11.658579doi: bioRxiv preprint \n\nonly by the amount of ligase per reaction, 4000U and 2000U, respectively. Stage 2 comprised two groups: Group I used \nPlatinum polymerase and Group II using EcoDry polymerase, each with five workflo ws as illustrated in Figure 7. All \ncombinations of the HELP workflows yielded results comparable to the ARTIC LoCost approach, although their \nperformance varied across different sequencing quality control metrics, particularly as viral load in samples redu ced \n(Figures 7 and 8; Extended data: Table S4).  \n \nFigure 7. Stage 2: Summary of HELP workflow comparisons. \nComparison of alternative HELP workflows (Groups I and II) with the LoCost protocol. Performance was evaluated across Ct values \nof 21.2, 24.6, 27.9, and 31.4 for SARS -CoV-2 isolate. (Top) Group I: HELP workflows Ia to Ie used M -MLV RT and Platinum \npolymerase for PCR, with variations in end-prep, barcode ligation, and adapter ligation mixes. (Bottom) Group II: HELP workflows \nIIa to IIe used M-MLV RT and EcoDry polymerase with the same variations. \n \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted June 12, 2025. ; https://doi.org/10.1101/2025.06.11.658579doi: bioRxiv preprint \n\n \nFigure 8. Stage 2: Performance of multiple HELP workflows compared to the LoCost protocol. \n(A) Bar plots comparing the performance of  HELP workflows using two polymerase groups (Platinum and EcoDry) against the \nLoCost protocol across four key metrics: Read Count, % of Mapped Reads, Mean Depth, and Amplicon Dropouts, using SARS -\nCoV-2 isolate at Ct values of 21.2, 24.6, 27.9, and 31.4. Group I (Platinum): HELP-Ia to HELP-Ie workflows incorporating M-MLV \nRT and Platinum polymerase with variations in end prep, barcode ligation, and adapter ligation. Group II (EcoDry): HELP -IIa to \nHELP-IIe workflows using M-MLV RT and EcoDry polymerase with the same variations. (B) Heatmap illustrating amplicon depth \ncoverage across various workflows. The x-axis represents amplicon IDs (1 to 99) from the SARS -CoV-2 genome targeted by the \nARTIC primer scheme v4.1. Colour gradient: red (low values) indicates poor or no coverage (depth ≤ 20), representing amplicon \ndropouts. \n \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted June 12, 2025. ; https://doi.org/10.1101/2025.06.11.658579doi: bioRxiv preprint \n\nAmong the Platinum HELP workflows, HELP-Id and HELP-Ie showed the most consistent performance across all Ct \nvalues, making them the closest alternative to the LoCost. The remaining Platinum HELP workflows (Ia, Ib, and Ic) had \nsimilar percentages of mapped reads and genome coverage but exhibited more variability in mean depth. Specific \ndropouts were consistent at amplicon 33 and 90 across all Ct values for all Platinum workflows. Among the EcoDr y \nHELP workflows, HELP-IId and IIe demonstrated relatively stable performance, making them good alternatives, though \nsome dropouts were seen at higher Ct values. The remaining EcoDry HELP workflows (IIa, IIb, and IIc) showed more \nvariability in mean depth and genome coverage, particularly at higher Ct values. Consistent dropouts were observed at \namplicon 51 and 82 across all Ct values for all EcoDry workflows.  \nThe results from the Stage 2 further confirmed the impact of polymerase choice and HELP mix combi nations on \nsequencing yield. In general, HELP workflows using Platinum polymerase in all tested combinations consistently \nachieved genome coverage >95%, with minimal dropouts. In comparison, HELP workflows with EcoDry polymerase \nexhibited genome coverage ranging from >91%, depending on the HELP mix combination used.  \n \nStage 3.  V alidation of the HELP-workflow using clinical samples positive for SARS-CoV-2  \nTo explore the utility of the best performing HELP workflows, HELP-Ie (Platinum) and HELP-IIe (EcoDry) workflows \nwere compared to ARTIC LoCost with 19 SARS -CoV-2 (Delta variant) RNA clinical samples (Ct 20–33). Sample \ncharacteristics and comprehensive sequencing metrics are summarized in Extended data Table S5. For low Ct samples \n(≤24), LoCost and HELP -Ie outperformed HELP-IIe in genome coverage (98.1–99.6% vs. 95.6–98.2%) and fewer \ndropouts (Figure 9). At moderate Ct values (>24 to ≤28), HELP-Ie matched LoCost (95.5–98.9%), while HELP-IIe \nshowed reduced performance (84.0–94.9%). At high Ct values (>28), HELP-Ie excelled (82.9–97.3%), LoCost showed \nmoderate coverage (86.0–89.8%), and HELP-IIe performed worst (55.7–84.1%). \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted June 12, 2025. ; https://doi.org/10.1101/2025.06.11.658579doi: bioRxiv preprint \n\n \nFigure 9. Stage 3: Performance on SARS-CoV-2 clinical samples. \n(A) Scatter plots showing the relationship between Ct values and ( left) % of mapped reads and ( right) amplicon dropouts across \nthree workflows: LoCost (green), HELP-Ie (Platinum, blue), and HELP-IIe (EcoDry , yellow) using SARS-CoV-2 clinical samples. \n(B) Heatmap illustrating amplicon depth coverage for 18 SARS -CoV-2 clinical s amples across workflows. Colour gradient: red \n(low values) indicates poor or no coverage (depth ≤ 20). \n \nWe also examined the amplicon dropout patterns using clinical samples (Figure 9B). Across all Ct values, specific \namplicons (e.g., 21, 22, 66, 90) frequently showed dropouts across all workflows, particularly at higher Ct values. \nAdditionally, each workflow exhibited a unique pattern of amplicon dropouts, suggesting that it may be possible to \nimprove performance by rebalancing primer concentrations f or the poorly performing amplicons ( 30). Amplicons 33 \nand 90 were determined to be unique to the Platinum polymerase, whereas for the LoCost workflow with Q5 polymerase, \namplicons 22, 66, and 90 were the most sensitive sites. The EcoDry exhibited specific amplicon dropouts, such as 31, \n51, and 82. \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted June 12, 2025. ; https://doi.org/10.1101/2025.06.11.658579doi: bioRxiv preprint \n\n \nStage 3.  V alidation of the HELP-workflow using clinical samples positive for Norovirus GII \nTo confirm the potential applicability of the HELP workflow to viruses beyond SARS -CoV-2, we sequenced 12 \nNorovirus genogroup II-positive stool samples comprising eight different genotypes (Ct values 15.6–32.8) (Extended \ndata: Table S6). Ten samples were unique, and two were included as technical replicates to complete a 12-barcode pool \nfor optimal sequencing yield and barc ode performance. We compared HELP -Ie (Platinum) and HELP -IIIe (Q5) \nworkflows (substituting EcoDry with Q5) against the LoCost workflow. Q5, a benchmark polymerase for high-multiplex \ntiling PCR, was included to test its integration with the complete HELP workflow, spanning EP, BL, and AL steps. The \nresults demonstrated that genome coverage and amplicon dropouts were predominantly influenced by genotype \nspecificity rather than workflow or Ct values, highlighting areas for potential primer design improvement ( Figure 10). \nAmong the genotypes GII4P31, GII17P17, GII4P4 (recombinant), and GII4P16 exhibited no amplicon dropouts across \nall workflows, achieving genome coverage between 95.1% and 96.6% (excluding 5’ and 3’ ends as the primer scheme \ndoes not cover these regions). GII4P4 (Ct 24.9) and GII3P12 (Ct 15.6) showed moderate genome coverage (77.3–86.1%) \nwith specific amplicon dropouts. GII6P7 (Ct 21.5) and GII7P7 (Ct 24.4) exhibited the lowest coverage (59.6–65.8% \nand 20.8–31.0%, respectively) with multiple consistent dropouts. These results demonstrated that the HELP workflow \nis robust and adaptable for sequencing Norovirus genogroup II, confirming its applicability beyond SARS -CoV-2. \nCoverage limitations observed for certain genotypes reflect primer scheme const raints rather than workflow \nperformance, underscoring the need for further refinement of the norovirus-gii/800/v1.1.0 scheme.  \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted June 12, 2025. ; https://doi.org/10.1101/2025.06.11.658579doi: bioRxiv preprint \n\n \nFigure 10. Stage 3: Performance on Norovirus GII clinical samples. \n(A) Genome coverage comparison across Norovirus GII genotype s and sequencing workflows. The sample Ct values are shown \nabove the x-axis, with samples ordered from low to high Ct. Coverage is compared between the LoCost protocol and two HELP \nworkflows using different polymerases: Platinum (HELP -Ie) and Q5 (HELP -IIIe). (B) Heatmap illustrating amplicon depth \ncoverage for Norovirus GII sequencing. The workflows compared include the LoCost  and two HELP workflows, utilising two \npolymerases: Platinum and Q5, with samples ordered from low to high Ct. The x-axis represents the amplicon IDs (0 to 9) targeted \nby the norovirus-gii/800/v1.0.0 scheme. Colour gradient: red (low values) indicates poor or no coverage (depth ≤ 20). \n \nComparison between the cost of the HELP and LoCost workflows \nWe compared reagent costs for HELP and LoCost protocols across six countries (UK, India, Mali, Indonesia, the \nPhilippines, Burkina Faso), including only the enzymatic steps of the workflow: RT, PCR, and library preparation (EP, \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted June 12, 2025. ; https://doi.org/10.1101/2025.06.11.658579doi: bioRxiv preprint \n\nBL, AL). Quotes were generated in each country, including additional taxes and delivery charges, to determine the cost \nof purchasing the reagents required for both protocols to the respective national or academic laboratories. As shown in \nTable 1, total costs for the LoCost workflow ranged from £30 in the UK to £57.70 in Indonesia. For the HELP workflow \n(using Platinum or EcoDry polymerase), total costs varied from £12.70 to £33.00. These costs exclude RNA extraction, \nSPRI clean-up, and ONT sequencing kit and flow cell. Despite this, notable price discrepancies were observed between \ncountries. Across countries where complete data were available, the HELP workflow reduced reagent costs by \napproximately 48% to 60% compared to LoCost, for example, 58% in the UK and 60% in India. \nTable 1. Cost comparison of reagent protocols across countries. Reagent costs per reaction are shown for the HELP \nand LoCost protocols, broken down into RT-PCR (using Platinum [A] or EcoDry [B]) and final library preparation \nsteps (end prep, barcode ligation, adapter ligation). Total costs per country are included where both RT-PCR and final \nstep data were available. The Philippines and Burkina Faso provided quotes only for library prep steps. \nSteps Protocol UK India Mali Indonesia Philippines Burkina \nFaso \nRT and \nPCR \nA £4.4 £5.9 £4.3 £14.1 n/a n/a \nB £5.9 £7.6 £4.7 £17.3 n/a n/a \nLoCost  £5.0 £8.0 £5.3 £9.8 n/a n/a \nEP, BL, \nAL \nHELP £8.3 £12.5 £9.5 £15.7 £14.0 £16.9 \nLoCost £25.0 £38.2 £27.6 £47.8 £38.5 £52.1 \nTotal \nHELP A £12.7 £18.4 £13.8 £29.7 - - \nHELP B £14.2 £20.1 £14.2 £33.0 - - \nLoCost £30.0 £46.2 £32.8 £57.7 - - \n \nWe also compared total workflow costs (including ONT reagents, extraction, and clean -up) using UK pricing across \nlibrary sizes of 24, 48, and 96 samples (Extended data: Table S7). \n \nDiscussion  \nThe ARTIC HELP protocol described here provides a viable alternative to the ARTIC LoCost and other amplicon-based \nnative barcoding workflows (ONT), demonstrating comparable performance across a range of conditions. By validating \nalternative reagents and enzymes already available in many molecular biology labs, HELP addresses supply chain issues \nexposed during the COVID-19 pandemic and helps maintain genomic surveillance during future disruptions. While the \nARTIC LoCost protocol has proven its versatility and effectiveness in advancing genomic surveillance ( 9-14, 31-34), \nthe development of the ARTIC HELP workflow further enhances these capabilities. \nIncorporating alternative enzymes and home-brew reagents into molecular diagnostics and sequencing workflows offers \na practical solution for maintaining viral sequencing capabilities during reagent shortages and public health emergencies. \nMatute et al. ( 35) and Page et al . (36) demonstrated reliable home -brew RNA extraction and RT -LAMP diagnostic \nmethods for SARS-CoV-2 detection without dependence on commercial kits. Similarly, Ou et al. showed that nanopore \nsequencing using home-brew components can match the performance of standard commercial mixes (37). In line with \nthese findings, our study validated the effectiveness of HELP mixes with clinical SARS -CoV-2 and Norovirus GII \nsamples, achieving high genome coverage. The integration of HELP alternatives into the LoCost pro tocol were \nimplemented through practical single -step substitutions (stage 1) or combined workflows (stages 2 and 3). For a \ncombined workflow most similar to the LoCost in terms of performance and consistency, HELP -e with Platinum \npolymerase is recommended,  particularly for its balance across metrics and lower dropout rates. This workflow is \navailable on protocol.io https://protocols.io/view/artic-help-protocol-for-amplicon-based-viral-genom-gzsibx6cf.  \nFocusing on single-step substitutions, we noted a number of key findings related to the performance of the HELP mixes. \nThe HELP EP-C mix, which excludes the Klenow fragment, performed comparably to the ARTIC LoCost workflow, \naligning with previous findings by Carøe et al. that simplified single-tube enzyme combinations can improve efficiency \nand accuracy (37). While not directly tested in our study, recent work (38) demonstrated that even T4 Polymerase may \nbe dispensable in end-prep reactions. In that study, a homebrew solution containing only T4 PNK and Taq Polymerase \neffectively replaced commercial end-repair modules, likely due to the proofreading activity of high-fidelity polymerases, \nwhich generate blunt-ended PCR products. The inclusion of PEG in HELP EP mixes to enhance enzyme activity also \nreflects strategies reported by Neiman et al., who used PEG and T4 Ligase buffer in a single reaction to achieve efficient \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted June 12, 2025. ; https://doi.org/10.1101/2025.06.11.658579doi: bioRxiv preprint \n\nlibrary preparation ( 39). For barcode ligati on, HELP BL -A containing 10 % PEG showed the highest efficiency, \nconsistent with reports that this concentration improves ligation, while higher levels such as 15 %, tested in BL -B, \ninhibited performance ( 40). HELP BL -C, which contained 1,2 -PrD, demonstrated moderate efficiency but was less \neffective than HCC and PEG, which is in line with studies reporting 14 -fold improvements with 1,2 -PrD, and even \ngreater gains, up to 50- and 100-fold, with HCC and PEG respectively (40-43). The highest performance was observed \nwith HELP BL-D, which combined 10 % PEG and 1 mM HCC, suggesting a synergistic effect. Overall, the omission \nof Klenow in EP-C, the use of a simplified barcode ligation mix in BL-A, and a reduced ligase concentration in adaptor \nligation mix AL -B wer e sufficient for effective performance in the combined HELP-e workflow. These findings \nreinforce the modularity and adaptability of the HELP mixes, allowing laboratories to customize workflows according \nto reagent availability and specific sequencing needs. \nWe found that the selection of the appropriate reverse transcriptase is particularly crucial at higher Ct values for optimal \nPCR efficiency. LunaScript, an engineered M-MLV RT variant, offers enhanced thermostability and reduced RNase H \nactivity, leading to more efficient cDNA synthesis ( 44). In contrast, wild -type M-MLV RT, with its higher RNase H \nactivity, can affect cDNA quality and yield ( 45, 46). While we showed that a wild -type M-MLV RT can serve as an \nalternative in the LoCost protocol when other o ptions are unavailable, high -performance engineered reverse \ntranscriptases, such as SuperScript IV , are recommended for better results, particularly in high Ct samples (>28).  \nHaving multiple polymerase options provides additional flexibility to adapt to supply chain disruptions. High-multiplex \nPCR, such as the SARS -CoV-2 tiling scheme ( 22), uses around 50 primer pairs per pool to simultaneously amplify \nmultiple regions of the genome, making polymerase selection critical for consistent results. This approach was originally \noptimised for Q5, a Pyrococcus -like (Pfu) B-family polymerase fused to the processivity -enhancing SSo7d domain, \nwhich provides high fidelity and strong template binding ( 47). In our study, all enzymes tested included proofreading \nactivity, yet performance varied markedly depending on structural features and formulation. B -family Pfu-based \npolymerases with a SSo7d DNA -binding domain (e.g., Platinum, Phusion) performed well at low Ct values, but only \nPlatinum sustained efficiency under low-input conditions, suggesting that even among similar polymerases, proprietary \nstabilisers and buffers influence template binding. Blended polymerases combining A-family (Taq-based) and B-family \n(Pfu-based) enzymes without an Sso7d DNA -binding domain (e.g., EcoDry, KAPA) showed variable performance. \nEcoDry consistently outperformed KAPA, potentially due to its lyophilised format and proprietary buffer composition. \nNotably, several polymerases that underperformed at higher Ct values still achieved strong coverage at Ct < 24, \nsupporting their use as emergency alternatives for rapid outbreak-response sequencing when viral input is high and RNA \nquality is sufficient. These findings underscore that successful high -multiplex PCR depends not only on polymerase \nfamily or proofreading ability, but also on the presence of engineered DNA -binding domains a nd optimised buffer \nformulation, which together shape processivity, fidelity, and performance across varying template inputs. \nWe observed that amplicon dropout patterns in SARS-CoV-2 genome amplification varied by polymerase, even though \nmost primers had well-matched melting (Tm) and annealing (Ta) temperature values, suggesting that Tm alone does not \nexplain poor amplification. For example, amplicons 33 and 90 dropped out with Platinum, 51 and 82 with EcoDry, and \n66 and 90 with Q5, all despite good predict ed performance. It is likely that other factors, such as primer competition \nand differences in buffer composition, may have contributed to these dropouts. It is also notable that EcoDry \nrecommends a higher annealing temperature (68°C), which may have affected its performance under the 63°C and 65°C \nconditions used in the ARTIC protocol with optimized primer concentrations and Tm. In contrast, Platinum and Q5 have \nbroader Ta compatibility, making them more adaptable to these settings. Our findings align with  previous studies of \nLambisia et al., showing that rebalancing primer concentrations can help recover underperforming regions (30). While \nwe did not test this directly, optimising primer pools per polymerase could improve consistency and should be \nconsidered.  \nWe demonstrated that the HELP workflow is applicable to viruses beyond SARS -CoV-2 by sequencing Norovirus \ngenogroup II. Our study also represents the first application of the norovirus -gii/800/v1.1.0 scheme (23), assessing its \nperformance with both th e LoCost and HELP workflows. Genome coverage varied across the eight Norovirus GII \ngenotypes, likely due to primer mismatches driven by the high genetic diversity of GII noroviruses, rather than Ct values \nor workflows (48, 49). Still, we achieved genome coverage of >95% for four GII norovirus genotypes (GII3P12, GII4P4-\nrecombinant, GII4P16, GII17P17), and >85% for two others (GII4P4, GII4P31). The lowest coverage was observed for \nGII6P7 (65.8%) and GII7P7 (31%), highlighting the need for improved primer design for these genotypes. There were \nalso notable variations in mean depth for the same amplicon region across workflows (Figure 10B). Since primers were \nused in equal molar concentrations, our findings likely highlight the need for primer balancing for any  given enzyme, \nto address these discrepancies and improve yield (30). To enhance amplification and coverage, targeted optimisations, \nsuch as adjusting concentrations of specific primers in pools, are recommended.  \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted June 12, 2025. ; https://doi.org/10.1101/2025.06.11.658579doi: bioRxiv preprint \n\nOur cost analysis highlights the well -established inequity in global access to molecular reagents ( 50). In some places, \nthe same reagents cost nearly twice as much, with these cost increases arising not due to technical differences in the \nsupplied products, but largely to customs fees, high import  taxes, and shipping costs. This creates real challenges for \nlabs in lower-resource settings, where budgets are limited and every extra cost makes it harder to carry out essential \ngenomic work. The lack of transparency around the additional local added costs, how and when they are implemented, \npresents an additional barrier to the accurate budgeting of laboratory functions and grant proposals. These findings \nhighlight the urgent need for more fair and transparent pricing, and support the value of locally so urced solutions like \nthe ARTIC HELP protocol to make genomic research more accessible and affordable everywhere. \n \nLimitations of the study  \nOur study, while comprehensive, has limitations primarily due to the limited number of generic enzymes explored, the \nvariability in polymerase performance and primer design. However we have provided a generic workflow template to \nidentify other discrepancies in genome coverage and amplicon dropouts that challenge uniform amplification, \nparticularly at high Ct values. Further optimization, including primer balancing and tailored reaction conditions for each \npolymerase, may be necessary to enhance the reliability and robustness of the sequencing workflow. Our results suggest \nthat the norovirus-gii/800/v1.1.0 scheme requires improvements, particularly by designing additional primers that are \nmore specific to GII6P7 and GII7P7 genotypes. Although the current norovirus scheme is still under development, our \nfindings provide insights for further optimisation. \n \nConclusion \nWe developed ARTIC HELP , a practical workflow using alternative enzymes and home-brew buffers that works as well \nas the standard ARTIC LoCost protocol for sequencing viral genomes from clinical samples. The ARTIC HELP \nworkflow offers a practical solution t o supply chain disruptions, supporting the continuity of critical sequencing \nactivities and expanding genomic surveillance and diagnostic capacity during public health emergencies. \n \nAcknowledgements \nThis work was funded by the Wellcome Trust ARTIC Network Collaborative Award (206298/B/17/Z) and the Wellcome \nTrust Award (313694/Z/24/Z). \n \nData availability \nRaw sequencing data (FASTQ) and consensus genomes (FASTA) are available under ENA Project PRJEB89721. This \nproject contains the following underlying datasets: \n- stage1_sars_cov_2_consensus – SARS-CoV-2 consensus sequences from workflows 1–6. Includes a control panel \nof four SARS-CoV-2 RNA concentrations derived from a lab-grown isolate (SARS-CoV-\n2/human/Liverpool/REMRQ001/2020). \n- stage1_sars_cov_2_fastq-raw – Raw sequencing data (FASTQ) from workflows 1–6 for the same control panel. \n- stage2_sars_cov_2_consensus – SARS-CoV-2 consensus sequences from a control panel of four RNA \nconcentrations derived from the lab-grown isolate (SARS-CoV-2/human/Liverpool/REMRQ001/2020). \n- stage2_sars_cov_2_fastq-raw – Raw sequencing data (FASTQ) corresponding to the Stage 2 control panel. \n- stage3_sars_cov_2_consensus – SARS-CoV-2 consensus sequences from a clinical panel of 19 samples processed \nusing three workflows: LoCost, HELP-Ie (Platinum), and HELP-IIe (EcoDry). \n- stage3_sars_cov_2_fastq-raw – Raw sequencing data (FASTQ) for the same SARS-CoV-2 clinical panel. \n- stage3_norovirus_gii_consensus – Norovirus GII consensus sequences from a clinical panel of 12 samples \nprocessed using three workflows: LoCost, HELP-Ie (Platinum), and HELP-IIIe (Q5). \n- stage3_norovirus_gii_fastq-raw – Raw sequencing data (FASTQ) for the same Norovirus GII clinical panel. \n \nExtended data    \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted June 12, 2025. ; https://doi.org/10.1101/2025.06.11.658579doi: bioRxiv preprint \n\nHELP workflows from the Stage 3 are available on protocol.io https://protocols.io/view/artic-help-protocol-for-\namplicon-based-viral-genom-gzsibx6cf \n \nExtended data supporting this study are available at: https://github.com/AnyaKovalenko/ARTIC-HELP  \nThis repository includes the following extended data files: \nARTIC-HELP_Extended-data.xlsx file. This Excel file includes: \n- Table S1. HELP Master Mix Recipes.  \n- Table S2. Reagent List used in the HELP study. \n- Table S3. Characteristics of polymerases used in this study (as provided by the supplier). \n- Table S4. Sequencing quality control metrics (stages 1 and 2: lab-grown isolate SARS-CoV-\n2/human/Liverpool/REMRQ001/2020). \n- Table S5. Sequencing quality control metrics (stage 3: 19 clinical samples SARS-CoV-2). \n- Table S6. Sequencing quality control metrics (stage 3: 10 clinical samples Norovirus Genogroup II). \n- Table S7. Cost comparison per sample for libraries containing 24, 48, and 96 samples. \nARTIC-HELP_Figure_S1.png file : \nFigure S1. Stage 1: Gel electrophoresis results for M-MLV (wf-2) and six polymerases (wf-3) compared to ARTIC \nLoCost (wf-1).  \n \nReferences: \n1. Quick, J., Loman, N. J., Duraffour, S., Simpson, J. T., Severi, E., Cowley , L., Bore, J. A., Koundouno, R., Dudas, G., \nMikhail, A., Ouédraogo, N., Afrough, B., Bah, A., Baum, J. H., Becker-Ziaja, B., Boettcher, J. P ., Cabeza-Cabrerizo, M., \nCamino-Sanchez, A., Carter, L. L., Doerrbecker, J., … Carroll, M. W. (2016). Real-time, portable genome sequencing for \nEbola surveillance. Nature, 530(7589), 228–232. https://doi.org/10.1038/nature16996   \n2. Gardy , J. L., & Loman, N. J. (2018). Towards a genomics-informed, real-time, global pathogen surveillance system. Nature \nreviews. Genetics, 19(1), 9–20. https://doi.org/10.1038/nrg.2017.88   \n3. Grubaugh, N. D., Ladner, J. T., Lemey , P ., Pybus, O. G., Rambaut, A., Holmes, E. C., & Andersen, K. G. (2019). Tracking \nvirus outbreaks in the twenty-first century . Nature microbiology, 4(1), 10–19. https://doi.org/10.1038/s41564-018-0296-2 \n4. Saravanan, K. A., Panigrahi, M., Kumar, H., Rajawat, D., Nayak, S. S., Bhushan, B., & Dutt, T. (2022). Role of genomics in \ncombating COVID-19 pandemic. Gene, 823, 146387. https://doi.org/10.1016/j.gene.2022.146387 \n5. Tosta, S., Moreno, K., Schuab, G., Fonseca, V ., Segovia, F. M. C., Kashima, S., Elias, M. C., Sampaio, S. C., Ciccozzi, M., \nAlcantara, L. C. J., Slavov, S. N., Lourenço, J., Cella, E., & Giovanetti, M. (2023). Global SARS-CoV-2 genomic \nsurveillance: What we have learned (so far). Infection, genetics and evolution : journal of molecular epidemiology and \nevolutionary genetics in infectious diseases, 108, 105405. https://doi.org/10.1016/j.meegid.2023.105405  \n6. Global genomic surveillance strategy for pathogens with pandemic and epidemic potential, 2022–2032. Geneva: World \nHealth Organization; 2022. Licence: CC BY-NC-SA 3.0 IGO \n7. Faria, N. R., Quick, J., Claro, I. M., Thézé, J., de Jesus, J. G., Giovanetti, M., Kraemer, M. U. G., Hill, S. C., Black, A., da \nCosta, A. C., Franco, L. G., Silva, S. P ., Wu, C.-H., Raghwani, J., Cauchemez, S., du Plessis, L., V erotti, M. P ., de Oliveira, \nW. K., Carmo, E. H., Coelho, G. E., Santelli, A. C. F. S., Vinhal, L. C., Henriques, C. M., Simpson, J. T., Loose, M., \nAndersen, K. G., Grubaugh, N. D., Somashekar, N. Y ., Chiu, C. Y ., Muñoz-Medina, J. E., Gonzalez-Bonilla, C. R., Arias, C. \nF., Lewis-Ximenez, L. L., Baylis, S. A., Chieppe, A. O., Aguiar, S. F., Fernandes, C. A., Lemos, P . S., Nascimento, B. L. S., \nMonteiro, H. A. O., Siqueira, I. C., de Queiroz, M. G., de Souza, T. R., Bezerra, J. F., Lemos, M. R., Pereira, G. F., Loudal, \nD., Moura, L. C., Dhalia, R., França, R. F., Magalhães, T., Marques Jr, E. T., Jaenisch, T., Wallau, G. L., Lima, M. C. de, \nV asconcelos, E. M., de Cerqueira, E. M., de Lima, M. M., Mascarenhas, L. M., Moura Neto, J. P ., Levin, A. S., Tozetto-\nMendoza, T. R., Fonseca, S. N., Mendes-Correa, M. C., Milagres, F. P ., Segurado, A. C., Holmes, E. C., Rambaut, A., \nBedford, T., Nunes, M. R. T., Sabino, E. C., Alcantara, L. C. J., Loman, N. J., & Pybus, O. G. (2017). Establishment and \ncryptic transmission of Zika virus in Brazil and the Americas. Nature, 546(7658), 406–410. \nhttps://doi.org/10.1038/nature22401     \n8. Ulhuq, F. R., Barge, M., Falconer, K., Wild, J., Fernandes, G., Gallagher, A., McGinley , S., Sugadol, A., Tariq, M., Maloney , \nD., Kenicer, J., Dewar, R., Templeton, K., & McHugh, M. P . (2023). Analysis of the ARTIC V4 and V4.1 SARS-CoV-2 \nprimers and their impact on the detection of Omicron BA.1 and BA.2 lineage-defining mutations. Microbial genomics, 9(4), \nmgen000991. https://doi.org/10.1099/mgen.0.000991 \n9. Claro, I. M., Romano, C. M., Candido, D. D. S., Lima, E. L., Lindoso, J. A. L., Ramundo, M. S., Moreira, F. R. R., Barra, L. \nA. C., Borges, L. M. S., Medeiros, L. A., Tomishige, M. Y . S., Moutinho, T., Silva, A. J. D. D., Rodrigues, C. C. M., \nAzevedo, L. C. F., Villas-Boas, L. S., Silva, C. A. M. D., Coletti, T. M., Manuli, E. R., O'Toole, A., … Sabino, E. C. (2022). \nShotgun metagenomic sequencing of the first case of monkeypox virus in Brazil, 2022. Revista do Instituto de Medicina \nTropical de Sao Paulo, 64, e48. https://doi.org/10.1590/S1678-9946202264048   \n10. Mboowa, G., Mwesigwa, S., Kateete, D., Wayengera, M., Nasinghe, E., Katagirya, E., Katabazi, A. F., Kigozi, E., \nKirimunda, S., Kamulegeya, R., Kabahita, J. M., Luutu, M. N., Nabisubi, P ., Kanyerezi, S., Bagaya, B. S., & Joloba, M. L. \n(2021). Whole-genome sequencing of SARS-CoV-2 in Uganda: implementation of the low-cost ARTIC protocol in resource-\nlimited settings. F1000Research, 10, 598. https://doi.org/10.12688/f1000research.53567.1 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted June 12, 2025. ; https://doi.org/10.1101/2025.06.11.658579doi: bioRxiv preprint \n\n11. Yakovleva, A., Kovalenko, G., Redlinger, M., Liulchuk, M. G., Bortz, E., Zadorozhna, V . I., Scherbinska, A. M., Wertheim, \nJ. O., Goodfellow, I., Meredith, L., & V asylyeva, T. I. (2022). Tracking SARS-COV-2 variants using Nanopore sequencing in \nUkraine in 2021. Scientific reports, 12(1), 15749. https://doi.org/10.1038/s41598-022-19414-y \n12. Sahadeo, N. S. D., Nicholls, S., Moreira, F. R. R., O'Toole, Á., Ramkissoon, V ., Whittaker, C., Hill, V ., McCrone, J. T., \nMohammed, N., Ramjag, A., Brown Jordan, A., Hill, S. C., Singh, R., Nathaniel-Girdharrie, S. M., Hinds, A., Ramkissoon, \nN., Parag, K. V ., Nandram, N., Parasram, R., Khan-Mohammed, Z., … Carrington, C. V . F. (2023). Implementation of \ngenomic surveillance of SARS-CoV-2 in the Caribbean: Lessons learned for sustainability in resource-limited settings. PLOS \nglobal public health, 3(2), e0001455. https://doi.org/10.1371/journal.pgph.0001455  \n13. Kent, C., Smith, A. D., Tyson, J., Stepniak, D., Kinganda-Lusamaki, E., Lee, T., Weaver, M., Sparks, N., Brier, T., \nLandsdowne, L., Wilkinson, S., Colquhoun, R., O’Toole, A., Mbala-Kingebeni, P ., Goodfellow, I., Rambaut, A., Loman, N., \n& Quick, J. (2024). PrimalScheme: Open-source community resources for low-cost viral genome sequencing [Preprint]. \nbioRxiv preprint doi: https://doi.org/10.1101/2024.12.20.629611 \n14. Josh Quick (2020). nCoV-2019 sequencing protocol v3 (LoCost). protocols.io. \nhttps://dx.doi.org/10.17504/protocols.io.bp2l6n26rgqe/v3  \n15. Tyson, J. R., James, P ., Stoddart, D., Sparks, N., Wickenhagen, A., Hall, G., Choi, J. H., Lapointe, H., Kamelian, K., Smith, \nA. D., Prystajecky , N., Goodfellow, I., Wilson, S. J., Harrigan, R., Snutch, T. P ., Loman, N. J., & Quick, J. (2020). \nImprovements to the ARTIC multiplex PCR method for SARS-CoV-2 genome sequencing using nanopore [Preprint]. bioRxiv. \nhttps://doi.org/10.1101/2020.09.04.283077  \n16. Ellmen, I., Overton, A. K., Knapp, J. J., Nash, D., Ho, H., Hungwe, Y ., Prasla, S., Nissimov, J. I., & Charles, T. C. (2024). \nReconstructing SARS-CoV-2 lineages from mixed wastewater sequencing data. Scientific reports, 14(1), 20273. \nhttps://doi.org/10.1038/s41598-024-70416-4  \n17. Moosavi, J., Fathollahi-Fard, A. M., & Dulebenets, M. A. (2022). Supply chain disruption during the COVID-19 pandemic: \nRecognizing potential disruption management strategies. International journal of disaster risk reduction : IJDRR, 75, \n102983. https://doi.org/10.1016/j.ijdrr.2022.102983    \n18. Akande, O. W ., Carter, L. L., Abubakar, A., Achilla, R., Barakat, A., Gumede, N., Guseinova, A., Inbanathan, F. Y ., Kato, M., \nKoua, E., Leite, J., Marklewitz, M., Mendez-Rico, J., Monamele, C., Musul, B., Nahapetyan, K., Naidoo, D., Ochola, R., \nOzel, M., Raftery , P ., … Samaan, G. (2023). Strengthening pathogen genomic surveillance for health emergencies: insights \nfrom the World Health Organization's regional initiatives. Frontiers in public health, 11, 1146730. \nhttps://doi.org/10.3389/fpubh.2023.1146730   \n19. Hosmillo, M., Chaudhry , Y ., Nayak, K., Sorgeloos, F., Koo, B. K., Merenda, A., Lillestol, R., Drumright, L., Zilbauer, M., & \nGoodfellow, I. (2020). Norovirus Replication in Human Intestinal Epithelial Cells Is Restricted by the Interferon-Induced \nJAK/STA T Signaling Pathway and RNA Polymerase II-Mediated Transcriptional Responses. mBio, 11(2), e00215-20. \nhttps://doi.org/10.1128/mBio.00215-20   \n20. Corman, V . M., Landt, O., Kaiser, M., Molenkamp, R., Meijer, A., Chu, D. K. W., Bleicker, T., Brünink, S., Schneider, J., \nSchmidt, M. L., Mulders, D. G. J. C., Haagmans, B. L., van der V eer, B., van den Brink, S., Wijsman, L., Goderski, G., \nRomette, J.-L., Ellis, J., Zambon, M., Peiris, M., Goossens, H., Reusken, C., Koopmans, M. P . G., & Drosten, C. (2020). \nDetection of 2019 novel coronavirus (2019-nCoV) by real-time RT-PCR. Eurosurveillance, 25(3) \nhttps://doi.org/10.2807/1560-7917.ES.2020.25.3.2000045 \n21. König, K. M. K., Jahun, A. S., Nayak, K., Drumright, L. N., Zibauer, M., Goodfellow, I., & Hosmillo, M. (2021). Design, \ndevelopment, and validation of a strand-specific RT-qPCR assay for GI and GII human noroviruses. W ellcome Open \nResearch, 6, 245. https://doi.org/10.12688/wellcomeopenres.17078.1 \n22. ARTIC Network. (n.d.). SARS-CoV-2 v4.1.0 primer scheme. primalscheme labs. https://labs.primalscheme.com/detail/artic-\nsars-cov-2/400/v4.1.0/  \n23. ARTIC Network. (n.d.). Norovirus pan-GII v1.1.0 primer scheme. primalscheme labs. \nhttps://labs.primalscheme.com/detail/norovirus-gii/800/v1.1.0/ \n24. Loman, N., Rowe, W., & Rambaut, A. (2020). nCoV-2019 novel coronavirus bioinformatics protocol. ARTIC Network. \nhttps://artic.network/ncov-2019/ncov2019-bioinformatics-sop.html   \n25. Simpson, J. (n.d.). ncov-tools: Small collection of tools for performing quality control on coronavirus sequencing data and \ngenomes [Software]. GitHub. https://github.com/jts/ncov-tools   \n26. ARTIC Network. (n.d.). The ARTIC field bioinformatics pipeline [Software]. GitHub. https://github.com/artic-\nnetwork/fieldbioinformatics  \n27. ARTIC Network. (n.d.). RAMP ART: Read Assignment, Mapping, and Phylogenetic Analysis in Real Time [Software]. \nGitHub. https://github.com/artic-network/rampart \n28. Kroneman, A., V ennema, H., Deforche, K., van Avoort, H., Peñaranda, S., Oberste, M. S., Vinjé, J., & Koopmans, M. (2011). \nAn automated genotyping tool for enteroviruses and noroviruses. Journal of Clinical Virology, 51(2), 121–125. \nhttps://doi.org/10.1016/j.jcv.2011.03.006  \n29. Nagai, S., Sildever, S., Nishi, N., Tazawa, S., Basti, L., Kobayashi, T., & Ishino, Y . (2022). Comparing PCR-generated \nartifacts of different polymerases for improved accuracy of DNA metabarcoding. Metabarcoding and Metagenomics, 6, 27-\n39. https://doi.org/10.3897/MBMG.6.77704    \n30. Lambisia, A. W., Mohammed, K. S., Makori, T. O., Ndwiga, L., Mburu, M. W., Morobe, J. M., Moraa, E. O., Musyoki, J., \nMurunga, N., Mwangi, J. N., Nokes, D. J., Agoti, C. N., Ochola-Oyier, L. I., & Githinji, G. (2022). Optimization of the \nSARS-CoV-2 ARTIC Network V4 Primers and Whole Genome Sequencing Protocol. Frontiers in medicine, 9, 836728. \nhttps://doi.org/10.3389/fmed.2022.836728    \n31. Brunker, K., Jaswant, G., Thumbi, S. M., Lushasi, K., Lugelo, A., Czupryna, A. M., Ade, F., Wambura, G., Chuchu, V ., \nSteenson, R., Ngeleja, C., Bautista, C., Manalo, D. L., Gomez, M. R. R., Chu, M. Y . J. V ., Miranda, M. E., Kamat, M., \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted June 12, 2025. ; https://doi.org/10.1101/2025.06.11.658579doi: bioRxiv preprint \n\nRysava, K., Espineda, J., Silo, E. A. V ., … Hampson, K. (2020). Rapid in-country sequencing of whole virus genomes to \ninform rabies elimination programmes. W ellcome open research, 5, 3. https://doi.org/10.12688/wellcomeopenres.15518.2    \n32. Stubbs, S. C. B., Blacklaws, B. A., Yohan, B., Y udhaputri, F. A., Hayati, R. F., Schwem, B., Salvaña, E. M., Destura, R. V ., \nLester, J. S., Myint, K. S., Sasmono, R. T., & Frost, S. D. W. (2020). Assessment of a multiplex PCR and Nanopore-based \nmethod for dengue virus sequencing in Indonesia. Virology journal, 17(1), 24. https://doi.org/10.1186/s12985-020-1294-6    \n33. Maes, M., Khokhar, F., Wilkinson, S. A. J., Smith, A. D., Kovalenko, G., Dougan, G., Quick, J., Loman, N. J., Baker, S., \nCurran, M. D., Skittrall, J. P ., & Houldcroft, C. J. (2023). Multiplex MinION sequencing suggests enteric adenovirus F41 \ngenetic diversity comparable to pre-COVID-19 era. Microbial genomics, 9(1), mgen000920. \nhttps://doi.org/10.1099/mgen.0.000920    \n34. Yakovleva, A., Kovalenko, G., Redlinger, M., Smyrnov, P ., Tymets, O., Korobchuk, A., Kotlyk, L., Kolodiazieva, A., \nPodolina, A., Cherniavska, S., Antonenko, P ., Strathdee, S. A., Friedman, S. R., Goodfellow, I., Wertheim, J. O., Bortz, E., \nMeredith, L., & V asylyeva, T. I. (2023). Hepatitis C Virus in people with experience of injection drug use following their \ndisplacement to Southern Ukraine before 2020. BMC infectious diseases, 23(1), 446. https://doi.org/10.1186/s12879-023-\n08423-5    \n35. Matute, T., Nuñez, I., Rivera, M., Reyes, J., Blázquez-Sánchez, P ., Arce, A., Brown, A. J., Gandini, C., Molloy , J., Ramírez-\nSarmiento, C. A., & Federici, F. (2021). Homebrew reagents for low-cost RT-LAMP . Journal of Biomolecular T echniques, \n32(3), 114–120. https://doi.org/10.7171/jbt.21-3203-006 \n36. Page, R., Scourfield, E., Ficarelli, M., McKellar, S. W., Lee, K. L., Maguire, T. J. A., Bouton, C., Lista, M. J., Neil, S. J. D., \nMalim, M. H., Zuckerman, M., Mischo, H. E., & Martinez-Nunez, R. T. (2022). Homebrew: An economical and sensitive \nglassmilk-based nucleic-acid extraction method for SARS-CoV-2 diagnostics. Cell reports methods, 2(3), 100186. \nhttps://doi.org/10.1016/j.crmeth.2022.100186   \n37. Carøe, C., Gopalakrishnan, S., Vinner, L., Mak, S. S. T., Sinding, M. H. S., Ramos-Madrigal, J., ... Gilbert, M. T. P . (2018). \nSingle-tube library preparation for degraded DNA. Methods in Ecology and Evolution, 9(2), 410–419. \nhttps://doi.org/10.1111/2041-210X.12871 \n38. Jie Hao Ou, Yin-Tse Huang (2024). Economical Nanopore LSK Sequencing: Adapted Protocols with Homebrew Reagents. \nprotocols.io. https://dx.doi.org/10.17504/protocols.io.dm6gp33w5vzp/v2  \n39. Neiman, M., Sundling, S., Grönberg, H., Hall, P ., Czene, K., Lindberg, J., & Klevebring, D. (2012). Library preparation and \nmultiplex capture for massive parallel sequencing applications made efficient and easy . PloS one, 7(11), e48616. \nhttps://doi.org/10.1371/journal.pone.0048616    \n40. Kestemont, D., , Renders, M., , Leonczak, P ., , Abramov, M., , Schepers, G., , Pinheiro, V . B., , Rozenski, J., , & Herdewijn, \nP ., (2018). XNA ligation using T4 DNA ligase in crowding conditions. Chemical communications (Cambridge, England), \n54(49), 6408–6411. https://doi.org/10.1039/c8cc02414f  \n41. Pheiffer, B. H., & Zimmerman, S. B. (1983). Polymer-stimulated ligation: enhanced blunt- or cohesive-end ligation of DNA \nor deoxyribooligonucleotides by T4 DNA ligase in polymer solutions. Nucleic acids research, 11(22), 7853–7871. \nhttps://doi.org/10.1093/nar/11.22.7853   \n42. Rusche, J. R., & Howard-Flanders, P . (1985). Hexamine cobalt chloride promotes intermolecular ligation of blunt end DNA \nfragments by T4 DNA ligase. Nucleic acids research, 13(6), 1997–2008. https://doi.org/10.1093/nar/13.6.1997   \n43. Xiao, Z. X., Cao, H. M., Luan, X. H., Zhao, J. L., Wei, D. Z., & Xiao, J. H. (2007). Effects of additives on efficiency and \nspecificity of ligase detection reaction. Molecular biotechnology, 35(2), 129–133. https://doi.org/10.1007/BF02686107   \n44. Oscorbin, I. P ., & Filipenko, M. L. (2021). M-MuLV reverse transcriptase: Selected properties and improved mutants. \nComputational and structural biotechnology journal, 19, 6315–6327. https://doi.org/10.1016/j.csbj.2021.11.030    \n45. Gerard, G. F., Fox, D. K., Nathan, M., & D'Alessio, J. M. (1997). Reverse transcriptase. The use of cloned Moloney murine \nleukemia virus reverse transcriptase to synthesize DNA from RNA. Molecular biotechnology, 8(1), 61–77. \nhttps://doi.org/10.1007/BF02762340    \n46. Huber, L. B., Betz, K., & Marx, A. (2023). Reverse Transcriptases: From Discovery and Applications to Xenobiology . \nChembiochem : a European journal of chemical biology , 24(5), e202200521. https://doi.org/10.1002/cbic.202200521    \n47. Quick, J., Grubaugh, N. D., Pullan, S. T., Claro, I. M., Smith, A. D., Gangavarapu, K., Oliveira, G., Robles-Sikisaka, R., \nRogers, T. F., Beutler, N. A., Burton, D. R., Lewis-Ximenez, L. L., de Jesus, J. G., Giovanetti, M., Hill, S. C., Black, A., \nBedford, T., Carroll, M. W., Nunes, M., Alcantara, L. C., Jr, … Loman, N. J. (2017). Multiplex PCR method for MinION and \nIllumina sequencing of Zika and other virus genomes directly from clinical samples. Nature protocols, 12(6), 1261–1276. \nhttps://doi.org/10.1038/nprot.2017.066   \n48. Chhabra , P ., de Graaf, M., Parra, G. I., Chan, M. C., Green, K., Martella, V ., Wang, Q., White, P . A., Katayama, K., \nV ennema, H., Koopmans, M. P . G., & Vinjé, J. (2019). Updated classification of norovirus genogroups and genotypes. The \nJournal of general virology, 100(10), 1393–1406. https://doi.org/10.1099/jgv.0.001318    \n49. Fitzpatrick, A. H., Rupnik, A., O’Shea, H., Crispie, F., Cotter, P . D., & Keaveney , S. (2023). Amplicon-based high-\nthroughput sequencing method for genotypic characterization of norovirus in oysters. Applied and Environmental \nMicrobiology, 89(2), e02165-22. https://doi.org/10.1128/aem.02165-22 \n50. Narayanasamy , S., Okware, B., Muttamba, W., Patel, K., Duedu, K. O., Ravi, N., Ellermeier, N., Shey , M., Woods, C. W., \nSabiiti, W., & COVID-19 Clinical Research Coalition, Virology , Immunology , and Diagnostics Working Group (2022). \nGlobal inequity of COVID-19 diagnostics: challenges and opportunities. Journal of epidemiology and community health, \njech-2022-219333. Advance online publication. https://doi.org/10.1136/jech-2022-219333  \n \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted June 12, 2025. ; https://doi.org/10.1101/2025.06.11.658579doi: bioRxiv preprint","source_license":"CC-BY-4.0","license_restricted":false}