Molecular Epidemiology Analysis of Imported Chikungunya Virus Cases Based on Second- and Third-Generation Sequencing

Molecular Epidemiology Analysis of Imported Chikungunya Virus Cases Based on Second- and Third-Generation Sequencing | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Molecular Epidemiology Analysis of Imported Chikungunya Virus Cases Based on Second- and Third-Generation Sequencing Lianghui Wei, Li Zhu, Jianzhong Ye, Ran Zhang, Ying Ye, Chunzhong Zhao, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8545804/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objective: This study aimed to analyze the molecular epidemiological characteristics of imported Chikungunya virus (CHIKV) infections identified during port surveillance and to evaluate the consistency and complementarity of second-generation metagenomic next-generation sequencing (mNGS) and third-generation Oxford Nanopore Technologies (ONT) sequencing in real-world monitoring scenarios. Methods: Three CHIKV-positive serum samples, confirmed by RT-qPCR, were selected as a case series. Viral genomic sequences were obtained using mNGS and amplicon-based ONT sequencing. Sequencing data were assessed for quality, genome coverage, variant distribution, and phylogenetic relationships. Cross-platform concordance was used as an internal validation for molecular epidemiological interpretation. Results: Both sequencing approaches successfully generated near-complete CHIKV genomes and produced highly consistent typing and source-tracing results in phylogenetic analyses. ONT sequencing demonstrated superior performance in genome continuity and coverage of complex regions, identifying more potential variant sites, whereas mNGS exhibited greater stability at the amino acid level. Despite differences in the number of detected variants between platforms, sample origin determination and phylogenetic placement remained highly concordant. Conclusion: This case-oriented study indicates that both second- and third-generation sequencing can reliably support molecular epidemiological investigations of imported CHIKV infections. mNGS and ONT each offer distinct advantages and can be strategically combined in practical applications to achieve efficient and precise molecular surveillance and source tracing of CHIKV. Chikungunya virus molecular epidemiology metagenomic NGS Oxford Nanopore sequencing Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction Chikungunya fever (CHIKF), caused by the Chikungunya virus (CHIKV), is an arthropod-borne disease transmitted primarily by Aedes aegypti and Aedes albopictus mosquitoes. the most common symptoms of CHIKF are fever and joint pain, other symptoms can include rash, head ache and debilitating polyarthralgia. Since its re-emergence and global expansion in early 2000s, CHIKF has became a significant public health concern, causing large-scale outbreaks across Asia, Africa, and the Americas. Over the past 15 years, more than 5 million cases have been recorded across 119 countries and regions. With the continued global spread of Aedes albopictus, the risk of imported epidemics in tropical, and subtropical regions regions continues to rise, posing increasing challenges for border health control and highlighting the urgent need for genomic surveillance of CHIKV [ 1 – 4 ] . Conventional RT-PCR assay is highly sensitive and rapid for CHIKV detection, however,it requirement for known target sequentces, means it may fail to detect novel variants or mixed infections and cannot provide comprehensive genomic information. Moreover, the genetic heterogeneity among CHIKV strains necessitates full-genome data to identify mutational hotspots, track transmission chains, and elucidate viral evolution. However, traditional assays fall short in resolving genome-wide diversity, structural variation, and in distinguishing vaccine strains from wild-type strains [ 5 – 8 ] . Metagenomic next-generation sequencing (mNGS) has emerged as a powerful unbiased tool that can detect all nucleic acid for pathogen discovery and genomic characterization. As an untargeted approach, mNGS enables large-scale parallel sequencing of all nucleic acids in a sample, enabling simultaneous detection of CHIKV and co-infecting pathogens while recovering genome-wide variation. This is especially valuable for febrile cases of unknown etiology and for discovering novel variants. However, mNGS also introduces notables limitations:More than 95% of reads typically originate from the host, resulting in insufficient effective sequencing data for pathogen genomes particularly in low-titer samples. Additionally, short-read sequencing (50–300 bp) also complicates de novo assembly, often yielding fragmented contigs and incomplete mutation profiles [ 9 – 12 ] . Oxford Nanopore sequencing (ONT), a third-generation platform, addresses these limitations by generating ultra-long reads, which enables the reconstruction of near-complete viral genomes without the need for assembly, thereby facilitating direct genotyping and accurate identification of key mutations.Moreover, by employing specific probes or primers to enrich CHIKV nucleic acids, this approach significantly reduces host background interference [ 13 – 15 ] . Currently, mNGS and ONT each have strengths in CHIKV research. mNGS is suitable for broad-spectrum pathogen screening and unknown pathogen identification, while ONT excels in high-precision analysis of known viruses. In port surveillance, sample numbers are limited and time is critical, but empirical data on how to strategically combine sequencing platforms for molecular epidemiology are scarce. Therefore, this study conducted a case series analysis of imported CHIKV infections detected at Shenzhen ports, integrating mNGS and ONT sequencing to comprehensively examine viral genome features and phylogenetic relationships, with a focus on evaluating cross-platform consistency and complementarity for case tracing and typing [ 16 – 18 ] . 2.Materials and Methods 2.1 Sample Collection This study included three serum samples from febrile travelers that were previously screened by RT-qPCR for CHIKV dentection at Shenzhen Customs between July 2024 and January 2025. All participants presented with fever (> 37°C) and at least one additional symptom (rash or arthralgia). 2.2 Nucleic Acid Extraction and RT-PCR Detection of CHIKV Viral RNA was extracted from 200 µL serum using an automated nucleic acid extractor (Generotex96, Tianlong Technology, Xi’an, China) and following the supplier’s protocol, and eluted in 60 µL buffer. RT-PCR was performed using a commercial CHIKV nucleic acid detection kit (Pfizer Biotech, Zhuhai, China) on an ABI 7500 system (Applied Biosystems, USA). 2.3 Metagenomic Sequencing of Chikungunya Virus RNA libraries were prepared with the MGIEasy RNA Library Kit (MGI Tech, China). Libraries underwent dual-barcoding and DNB preparation before sequencing on the MGI200 platform (MGI Tech, China; PE100 mode). FASTQ files were assembled with MEGAHIT using CHIKV strain S27b03 (GenBank: PV685524.1) as reference. Genotyping was performed with BLASTn, and phylogenetic trees were inferred using IQ-TREE. 2.4 Third-Generation Targeted Sequencing of Chikungunya Virus Libraries were prepared using the CHIKV Targeted Sequencing Kit (Oxford Nanopore, SQK-RBK114.24). Following amplification and adapter ligation, libraries were quality-controlled with Qubit. Sequencing was performed on the MinION platform (ONT, FLO-MIN114 chip; Dipinore sequencer, China). Reads were reference-assembled, typed with BLASTn, and analyzed phylogenetically with IQ-TREE. 2.5 Bioinformatics Analysis Sequencing reads were subjected to quality control to remove low-quality bases and adapters. For mNGS, host-derived reads were filtered to obtain CHIKV-related reads. For ONT, platform-recommended quality filtering was applied. CHIKV reads from both platforms were aligned to the reference genome to generate consensus sequences. To ensure cross-platform comparability, the study focused on genome coverage, coding region sequence consistency, and phylogenetic typing, rather than the absolute number of detected variants. Nucleotide identity (NT identity) was calculated as the percentage of matching bases with the reference genome, and amino acid identity (AA identity) was based on translated coding sequences. The same methods were applied for both platforms. 3. Results 3.1 RT-PCR Results Nucleic acids were extracted from the blood samples and tested for three vector-borne viruses using fluorescent RT-PCR. The results for Dengue virus and Zika virus were negative, while Chikungunya virus was positive. The Ct values for the positive samples were as follows(Fig. 1 ): Sample 1: Ct 20.65; Sample 2: Ct 31.75; Sample 3: Ct 25.06, which can be categorized as high, low, and medium viral load, respectively. The positive control showed expected amplification, and the negative control showed no amplification signal, indicating no contamination during the experiment. The experimental data is reliable. 3.2 Metagenomic Sequencing Results Paired-end (PE100) mNGS generated high-quality data for all samples (Table 1 ). Average Q20 and Q30 values were 97.89 ± 0.20% and 94.67 ± 0.32%, respectively, exceeding industry thresholds.In terms of sequence composition, the mean proportion of host-derived sequences in the original sequencing sequences was (95.73 ± 1.63)%, while the mean proportion of valid sequences was only (1.89 ± 1.57)%. This result further confirmed that the high proportion of host sequences in macro-genome sequencing is prone to cause strong interference in the analysis of target sequences.Additionally, it is worth noting that although sample 1 had the lowest Ct value (typically indicating a relatively higher viral load), it only identified 884 sequences of chikungunya virus. The potential mechanism behind this phenomenon needs further in-depth analysis [ 19 – 20 ] . Table 1 mNGS results Sample Raw Sequences Q20 Q30 Host Sequences Host Percentage Effective Sequences Effective Percentage CHIKV Sequences Sample 1 51482297 97.78% 94.47% 50233032 97.5% 99871 0.19% 884 Sample 2 54024210 97.77% 94.5% 51581597 95.4% 1189375 2.2% 171155 Sample 3 29357713 98.12% 95.04% 27705241 94.3% 965519.5 3.29% 38784 3.3 Third-Generation Targeted Sequencing Results Third-generation targeted sequencing of Chikungunya virus yielded high-quality data for all samples. Sample 1 generated 78.41 Mb of raw sequencing data (1 Mb = 10⁶ bases), with 94.5% passing quality control. A total of 148.32 K reads (1 K = 10³ reads) were obtained, of which 92.3% met quality standards, indicating both high sequencing quality and sufficient effective coverage. Sample 2 produced 40.62 Mb of raw data, with 91.8% passing quality control, and 70.2 K reads, 93.9% of which were of acceptable quality. Sample 3 yielded 53.92 Mb of raw data, with 93.1% passing quality control, and 104.45 K reads, 92.7% of which were high-quality (Table 2 ). The Q score distributions exhibited single, sharp peaks without noticeable fluctuations or multiple modes, reflecting a stable sequencing process, effective removal of low-quality reads, and the absence of sample contamination or chip obstruction. Peaks were predominantly centered around Q20(Figure 2 ), confirming that the majority of reads had high-quality scores and that the results are reliable [ 21 – 22 ] . Table 2 Results of ONT Sample Total Raw Data (Mb) Quality Data (%) Total Reads (K) Reads Meeting Quality Standards (%) Sample 1 78.41 94.5 148.32 92.3 Sample 2 40.62 91.8 70.2 93.9 Sample 3 53.92 93.1 104.45 92.7 3.4 Genomic Characteristics of Cases 3.4.1 Time and Cost Comparison The sequencing workflows of the two platforms were compared across three main stages: nucleic acid processing, library construction, and sequencing(Table 3 ). In the nucleic acid processing stage, second-generation mNGS workflow involved rRNA removal, RNA enrichment, fragmentation, and reverse transcription using commercial kits, taking approximately 3 hours. In contrast, third-generation CHIKV-targeted sequencing involves nucleic acid extraction and reverse transcription to cDNA, requiring only 0.5 hours. Fof library construction, mNGS involves double-strand synthesis, purification, adapter ligation, amplification, circularization, and DNB preparation, which collectively take around 6.5 hours. Third-generation targeted sequencing requires only amplification, adapter ligation, and purification, taking approximately 4 hours. For the sequencing stage, second-generation mNGS employs a PE100 protocol, requiring roughly 30 hours for sequencing plus an additional ~ 2 hours for data analysis, which may increase with larger sample numbers. In contrast, third-generation targeted sequencing completes sequencing and data acquisition within approximately 8 hours. Regarding cost per sample, the mNGS approach was estimated at approximately $ 110, while the targeted ONT method cost about $ 140. Table 3 mNGS and ONT Tim and Cost Comparison Metric mNGS ONT Nucleic Acid Processing Time 3h 0.5h Library Construction Time 6.5h 4h Sequencing Analysis Time 32h 8h Cost per Sample 110 $ 140 $ 3.4.2 Genome Features by Sequencing Method Metagenomic sequencing (mNGS) and nanopore sequencing (ONT) were performed on all three samples to obtain the CHIKV genome sequences. The results indicated differences among samples in genome coverage and nucleotide concordance. Samples 1 and 3 exhibited slightly higher coverage under ONT sequencing, whereas Sample 2 showed minimal differences between the two sequencing methods. The total sequencing length was comparable across both methods, indicating that overall genome integrity was well preserved. In terms of amino acid concordance, Sample 1 displayed higher stability in mNGS-derived sequences, while Samples 2 and 3 showed only minor differences. Overall, both sequencing approaches reliably provided genomic information suitable for case typing and epidemiological tracing, and offered a solid foundation for subsequent analyses of genetic variation and phylogeny. Table 4 Genome Comparison Table of mNGS and ONT Sample Sequencing_Type Coverage(%) Total_length NT_Identity(%) AA_Identity(%) Overall_Identity(%) 1 mNGS 96.5% 11216 90.5 89.6 90.0 ONT 99.9% 11230 92.8 63.8 78.3 2 mNGS 100.0% 11237 96.5 95.5 96.0 ONT 100.0% 11237 96.5 95.5 96.0 3 mNGS 97.6% 11237 91.3 89.9 90.6 ONT 99.9% 11225 92.9 91.2 92.1 3.4.3 Regional Coverage This study further compared the coverage performance of second-generation metagenomic sequencing (mNGS) and third-generation nanopore targeted sequencing (ONT) across different functional regions of the CHIKV genome. The CHIKV genome primarily comprises non-structural protein–coding regions (P1, nsP2, nsP3, and nsP4), which are mainly involved in viral replication; structural protein–coding regions (E3, E2, 6K, and E1), which constitute the viral particle; and non-coding regions (3′ untranslated region [3′ UTR]), which regulate viral RNA stability. As shown in Fig. 3 , both sequencing approaches achieved overall genome coverage exceeding 90% and effectively covered key functional regions of the CHIKV genome. Although certain differences in coverage depth and local completeness were observed among individual cases, the overall coverage was sufficient to meet the requirements for genotyping and phylogenetic analysis. These findings indicate that, under real-world surveillance sample conditions, both sequencing strategies can provide adequate genomic information to support case-level molecular epidemiological investigations. 3.4.4 Key Variant Sites Across the three clinical samples, the number of variant sites identified differed between sequencing approaches, while also demonstrating complementary detection profiles. As illustrated by the circos plot analysis(Figure 4), ONT sequencing identified 155 potential variant sites in Sample 1 and 78 sites in Sample 3, whereas Sample 2 showed only minor differences in the number of variants detected by the two methods. These additional variant sites were predominantly located within non-structural protein–coding regions. Despite these differences, both sequencing approaches showed overall concordance in the core variant patterns within key functional regions, providing reliable information for downstream genome-wide variant analysis and phylogenetic studies. Overall, ONT sequencing appears advantageous for improving the comprehensiveness of variant detection in samples with higher genomic complexity or variability, while mNGS alone is sufficient for efficient variant identification in samples with simpler mutation profiles and high sequence homology. Therefore, sequencing strategies should be tailored to the complexity of genomic variation in target samples to achieve accurate and comprehensive variant detection. 3.4.5 Mutation Site Genome Distribution Analysis By analyzing the density distribution of shared variants and ONT-specific mutation sites, the differences in mutation detection between mNGS and ONT across the CHIKV genome were clearly illustrated(Fig. 5 ). In Sample 1, shared mutations were evenly distributed in regions such as P1 and nsP2, whereas ONT-specific mutations were notably enriched in the E1 region, suggesting that ONT’s long-read capability enables the detection of complex structural variations that short-read mNGS may miss. In Sample 2, mutation profiles from both platforms were largely comparable, with ONT’s long-read advantage less pronounced. In Sample 3, ONT-specific mutations exhibited minor peaks in nsP3 and E1. Overall, mNGS provides stable detection of common mutations and consistent genome-wide coverage, while ONT is superior for identifying complex structural regions and specific mutations. The two methods exhibit clear complementarity in characterizing viral genetic diversity.. 3.4.6 Source Tracking Analysis Based on the reconstructed phylogenetic tree, sequences generated by ONT and mNGS showed a high degree of concordance in clustering patterns for the same samples. Specifically, ONT1 clustered closely with mNGS1, ONT2 with mNGS2, and ONT3 with mNGS3, each forming paired branches with extremely short branch lengths, indicating minimal sequence divergence between the two sequencing approaches. Notably, all analyzed samples were consistently assigned to the selected reference clade without evidence of cross-lineage placement or misclassification. Phylogenetic analysis further demonstrated that all three samples clustered within the Asian lineage, confirming their genetic relatedness to Asian CHIKV strains. These results indicate that both sequencing methods reliably capture the phylogenetic signal of the samples. Overall, evolutionary tree reconstruction showed strong consistency between ONT and mNGS in whole-genome evolutionary inference, supporting their use as complementary approaches. While ONT offers advantages in sequence continuity, mNGS remains robust for high-coverage short-read detection. 4. Discussion 4.1 Core Performance Differences and Application Scenario Adaptability of Sequencing Technologies As a globally significant arbovirus, rapid genotyping and source tracing of Chikungunya virus (CHIKV) rely heavily on accurate and efficient sequencing technologies [ 23 ] .. In this study, three imported CHIKV-positive cases collected by Shenzhen Customs were analyzed. Based on whole-genome sequencing data, the molecular epidemiological characteristics of the viruses associated with these cases were investigated, with the aim of providing data support for different application scenarios and contributing evidence for the optimization of infectious disease surveillance and control systems [ 24 ] . The results showed that both metagenomic sequencing (mNGS) and nanopore sequencing (ONT) were able to generate near-complete CHIKV genome sequences sufficient for genotyping and phylogenetic analysis in this case series, although differences were observed in genome coverage characteristics and sequence continuity [ 25 ] . Taking Case 1 as an example, ONT sequencing demonstrated advantages in overall coverage and sequence continuity, particularly in structurally complex non-structural protein–coding regions (such as nsP3 and nsP4), where local coverage gaps were reduced. In contrast, due to the inherent short-read nature of mNGS, sequence fragmentation or discontinuous coverage was observed in certain genomic regions (Fig. 5 ) [ 26 ] . These findings suggest that different sequencing strategies may provide complementary information at the genome resolution level. Differences were also observed between sequencing approaches in variant site detection across cases. Again, in Case 1, ONT sequencing identified a higher number of potential variant sites than mNGS, with some variants concentrated in the E1 coding region. It should be emphasized that the number of detected variant sites was not used in this study as a metric for evaluating the sensitivity or accuracy of sequencing methods, but rather as an observational outcome reflecting case-level genomic variation. Importantly, these differences did not affect branch assignment or source attribution in phylogenetic analyses, indicating that consensus sequences generated across platforms show good concordance for molecular epidemiological interpretation [ 27 ] . 4.2 Improving Detection Performance via Technological Complementarity Case-level analyses indicated that mNGS and ONT exhibit complementary characteristics in variant site detection, with these differences being more pronounced in samples with relatively complex genomic structures. For example, in Case 1, ONT sequencing identified a greater number of potential variant sites than mNGS, and a similar pattern was also observed in Case 3. These findings suggest that, in samples with complex genomic architecture or in regions where coverage is challenging, reliance on a single sequencing strategy may be insufficient to capture the full spectrum of genomic variation. Integrating data generated by different sequencing approaches may therefore facilitate a more comprehensive characterization of case-associated variant distributions, thereby reducing the risk of information loss attributable to platform-specific biases.From a technical perspective, mNGS demonstrates stable performance in routine variant detection and base-level concordance, whereas ONT, benefiting from its long-read capability, can provide supplementary sequence information in regions that are difficult to be continuously covered by short-read sequencing. Consequently, the two sequencing strategies offer complementary insights in case-based genomic analyses [ 28 ] . 4.3Limitations of the Study and Future Directions This study has certain limitations in the analysis of the association between third-generation ONT targeted sequencing data characteristics and sample Ct values, which should be objectively acknowledged and may inform directions for future optimization. First, the small sample size limits the generalizability of the conclusions. Only three samples were included in this analysis, and although a potential association between Ct values and raw data yield or total read counts was preliminarily observed, results derived from such a limited dataset are susceptible to the influence of inter-sample variability and stochastic errors introduced during library preparation. Consequently, these findings are insufficient to comprehensively characterize sequencing data patterns across different viral load gradients. Future studies should expand the sample size and include samples from diverse sources and across a broader range of viral loads to validate these associations and improve the robustness and applicability of the conclusions. In addition, the data characteristics observed in individual samples suggest that sequencing outcomes may be influenced by multiple technical factors. For example, although Sample 1 exhibited the lowest Ct value by RT-qPCR, indicating a relatively high viral load, a smaller number of CHIKV-related sequences was identified in the metagenomic sequencing results. This discrepancy may be attributable to fluctuations in host nucleic acid abundance, differences in RNA integrity, and stochastic effects during library construction in the mNGS workflow. As metagenomic sequencing does not rely on targeted enrichment, samples with a high host background or uneven RNA fragmentation may yield fewer target pathogen reads despite high viral loads, due to insufficient allocation of effective sequencing depth. These observations indicate that, at the case level, the relationship between Ct values and the number of target pathogen reads obtained by mNGS is not strictly linear and should be interpreted in the context of sequencing strategy characteristics and sample quality. Based on these limitations, future studies should expand the sample cohort and systematically include samples spanning different Ct value ranges and sources, enabling stratified and multivariate analyses to more comprehensively assess the impact of viral load on ONT targeted sequencing data characteristics. Moreover, it would be beneficial to integrate raw sequencing metrics with downstream analytical outcomes, such as genome assembly completeness, coverage depth distribution, and functional annotation adequacy, to more thoroughly evaluate the practical implications of Ct values on sequencing data utility. Finally, to address potential confounding factors such as host sequence abundance and amplification bias, further optimization of library preparation and targeted enrichment strategies may improve the efficiency of target pathogen sequence recovery in low viral load samples, thereby providing a more robust technical foundation for the application of ONT targeted sequencing in low-load pathogen detection and genome characterization [ 29 – 30 ] . 5. Conclusion This study compared the performance of second-generation metagenomic sequencing (mNGS) and third-generation nanopore sequencing (ONT) in molecular epidemiological analyses based on three imported Chikungunya virus (CHIKV) cases. The results demonstrated that both sequencing strategies were able to generate near-complete CHIKV genome sequences sufficient for case genotyping and phylogenetic source tracing, showing a high level of concordance in phylogenetic placement and source attribution. The observed differences between the two approaches were mainly related to sequencing strategy, sequence continuity, and modes of information representation [31] . In port-of-entry surveillance settings where rapid turnaround is critical, amplicon-based ONT sequencing shows practical potential by shortening sequencing time and improving genome continuity, thereby supporting rapid case-level genotyping and preliminary source tracing. In contrast, mNGS, owing to its untargeted nature, remains valuable for broad-spectrum pathogen screening, detection of mixed infections, and surveillance of unknown or unexpected pathogens. Overall, the case-based analyses indicate that mNGS and ONT provide complementary strengths in CHIKV molecular epidemiological investigations [32] . In routine surveillance practice, flexible selection or combined application of these sequencing strategies according to sample characteristics and analytical requirements may enhance genome reconstruction completeness and result robustness, providing more reliable technical support for the monitoring and source tracing of imported CHIKV cases [33] . Declarations Ethics approval and consent to participate： This study was reviewed and approved by the Shenzhen International Travel Health Care Centre Ethics Committee (Approval No: [BJZX20250008]). Written informed consent was obtained from all participants. The study was conducted in accordance with the principles of the Declaration of Helsinki. Consent for publication： Not applicable. No identifiable personal or clinical details, images, or videos of any individual person are included in this manuscript. Availability of data and materials： The datasets generated and/or analyzed during the current study have been deposited in the Genome Sequence Archive (GSA) under the accession number CRA030563 (submission ID: subCRA049893, title: Chikungunya virus). The data are publicly accessible at https://ngdc.cncb.ac.cn/gsa/browse/CRA030563 Authors Contributions : Lianghui Wei was responsible for article writing and experiments, Li Zhu for data analysis,Jianzhong Ye for reagent purchasing, Ran Zhang and Chunchong Zhao for instrument management, Ying Ye for organizing experimental literature, and Ye Yang and Jian'an He for technical guidance and experimental scheme design. Funding: This work was supported by (2024YFC2310205): Research and Application of Rapid Multi-pathogen Identification Technology Based on Real-time Sequencing; General Administration of Customs Research Project (2024HK058): Research and Development of Intelligent Rapid Recognition Technology for Port Vectors. Informed Consent Statement: The informed consent of all the research subjects has been obtained. Data Availability Statement: The data that has been used is confidential. Co mpeting Interest s: All authors declare that there are no conflicts of interest. Acknowledgements: Thanks are extended to all participants for their valuable advice and support during this study. This article is © 2026 by Lianghui Wei. All rights reserved. References Javaid A, Ijaz A, Ashfaq UA, Arshad M, Irshad S, Saif S. 2022. An overview of chikungunya virus molecular biology, epidemiology, pathogenesis, treatment and prevention strategies. Future Virology 17:593-606. Ni J, Li ZF, Hu XW, Zhou H, Gong ZY. 2025. Chikungunya's global rebound and Asia's growing vulnerability: Implications for integrated vector control and pandemic preparedness. Bioscience Trends doi:10.5582/bst.2025.01239. Webb EM, Compton A, Rai P, Chuong C, Paulson SL, Tu ZJ, Weger-Lucarelli J. 2023. Expression of anti-chikungunya single-domain antibodies in transgenic reduces vector competence for chikungunya virus and Mayaro virus. Frontiers in Microbiology 14. Resck MEB, Camara DCP, dos Santos FB, dos Santos JPC, Alto BW, Honório NA. 2024. Spatial-temporal distribution of chikungunya virus in Brazil: a review on the circulating viral genotypes and as a potential vector. Frontiers in Public Health 12. Dieng I, Ndiaye M, Kane M, Balde D, Mbanne M, Diop SMBS, Sankhe S, Dia M, Dieng M, Doukanda SFM, Faye O, Sall AA, Dia N, Fall G, Faye O, Diagne MM. 2023. An amplicon-based Illumina and nanopore sequencing workflow for Chikungunya virus West Africa genotype. medRxiv doi:10.1101/2023.12.07.23299611. Cardona-Trujillo MC, Ocampo-Cárdenas T, Tabares-Villa FA, Zuluaga-Vélez A, Sepúlveda-Arias JC. 2022. Recent molecular techniques for the diagnosis of Zika and Chikungunya infections: A systematic review. Heliyon - 8:e10225. Schmitz KS, Comvalius AD, Nieuwkoop NJ, Geers D, Weiskopf D, Ramsauer K, Sette A, Tschismarov R, de Vries RD, de Swart RL. 2023. A measles virus-based vaccine induces robust chikungunya virus-specific CD4+T-cell responses in a phase II clinical trial. Vaccine 41:6495-6504. Belem LRW, Ibemgbo SA, Gomgnimbou MK, Verma DK, Kaboré A, Kumar A, Sangaré I, Sunil S. 2024. Development of Multiplex Molecular Assays for Simultaneous Detection of Dengue Serotypes and Chikungunya Virus for Arbovirus Surveillance. Current Issues in Molecular Biology 46:2093-2104. Olearo F, El Zein S, Portillo ME, Zapf A, Rohde H, Berbari EF, Wouthuyzen-Bakker M, ESGIAI ESGIAI, Mol Working Grp Unified PJIDTF. 2025. Diagnostic accuracy of 16S rDNA PCR, multiplex PCR and metagenomic next-generation sequencing in periprosthetic joint infections: a systematic review and meta-analysis. Clinical Microbiology and Infection 31:1115-1125. Bolloré K, Tinto B, Charriat F, Pisoni A, Exbrayat A, Gutierrez S, Simonin Y, Tuaillon E. 2025. Investigation of the viral causes of febrile jaundice in Burkina Faso through metagenomic sequencing. Journal of Infection 91. Zheng N, Yu HL, Zhang BJ, Wang D, Ji YL, Dai LL, Li W, Li SH, Hu ZL, Zheng YS. 2025. Metagenomic next-generation sequencing-based characterization of the viral spectrum in clinical pulmonary and peripheral blood samples of patients. Frontiers in Cellular and Infection Microbiology 15. Bai Y, Li Y, Liu W, Liu L, Tong Y. 2022. Application of next-generation sequencing in the detection of emerging outbreak of virus infection. Chinese Journal of Experimental and Clinical Virology 36:739-747. Wei-ying Chen, Yang-sen Qin, Ting-fu Zhang, Jian Zou, Jun Yang, Zhen-yong Chen.2025.A chromosome-level genome assembly of Termitomyces fuliginosus using Oxford Nanopore and Hi-C sequencing.Genomics.ISSN 0888-7543. Javaran VJ, Moffett P, Lemoyne P, Xu D, Adkar-Purushothama CR, Fall ML. 2021. Grapevine Virology in the Third-Generation Sequencing Era: From Virus Detection to Viral Epitranscriptomics. Plants-Basel 10. Brinkmann A, Pape K, Uddin S, Woelk N, Förster S, Jessen H, Michel J, Kohl C, Schaade L, Nitsche A. 2024. Genome sequencing of the mpox virus 2022 outbreak with amplicon-based Oxford Nanopore MinION sequencing. Journal of Virological Methods 325. Dabernig-Heinz J, Lohde M, Hölzer M, Cabal A, Conzemius R, Brandt C, Kohl M, Halbedel S, Hyden P, Fischer MA, Pietzka A, Daza B, Idelevich EA, Stöger A, Becker K, Fuchs S, Ruppitsch W, Steinmetz I, Kohler C, Wagner GE. 2024. A multicenter study on accuracy and reproducibility of nanopore sequencing-based genotyping of bacterial pathogens. Journal of Clinical Microbiology 62. Stefan CP, Hall AT, Graham AS, Minogue TD. 2022. Comparison of Illumina and Oxford Nanopore Sequencing Technologies for Pathogen Detection from Clinical Matrices Using Molecular Inversion Probes. Journal of Molecular Diagnostics 24:395-405. Wang J, Zhang R, Sun Z, Ren H, Chen C, Dong X, Ma X. 2020. Detection of the Chikungunya Virus and Sindbis Viruses in Simulated Clinical Samples Using Metagenomic Nanopore Sequencing. Chinese Journal of Virology 36:377-384. Takemae N, Kuba Y, Oba K, Kageyama T. 2024. Direct genome sequencing of respiratory viruses from low viral load clinical specimens using the target capture sequencing technology. Microbiology Spectrum 12. Ning XH, Xia BH, Wang JQ, Gao R, Ren H. 2024. Host-adaptive mutations in Chikungunya virus genome. Virulence 15. Da H, Meng T, Xu YH. 2025. Application of targeted next-generation sequencing for detecting respiratory pathogens in the sputum of patients with pulmonary infections. Infection Genetics and Evolution 128. Zhang SS, Li X, Li X, Fu Y, Chen L, Wang W, Lin QQ, Lou H, Yao YK, Chen WY, Zhong C, Ye J, Yao YN, Guo HM, Yu YS, Zhou H. 2025. Optimisation and clinical validation of a metagenomic third-generation sequencing approach for aetiological diagnosis in bronchoalveolar lavage fluid of patients with. Ebiomedicine 116. Govender KN, Street TL, Sanderson ND, Leach L, Morgan M, Eyre DW. 2025. Rapid clinical diagnosis and treatment of common, undetected, and uncultivable bloodstream infections using metagenomic sequencing from routine blood cultures with Oxford Nanopore. medRxiv doi:10.1101/2025.01.08.25320182. de Souza LM, de Oliveira ID, Sales FCS, da Costa AC, Campos KR, Abbud A, Guerra JM, Borges CDC, Takahashi C, de Araújo LJT. 2023. Technical comparison of MinIon and Illumina technologies for genotyping Chikungunya virus in clinical samples. Journal of Genetic Engineering and Biotechnology 21. Peng MF, Davis ML, Bentz ML, Burgin A, Burroughs M, Padilla J, Nobles S, Unoarumhi Y, Tang K. 2025. Short-Read and Long-Read Whole Genome Sequencing for SARS-CoV-2 Variants Identification. Viruses-Basel 17. Peng SY, Liang R, Liu P, Hu L, Ai YP, Wang JF, Hong GQ, Qu SL. 2023. 557. Cost-effectiveness of Introducing Metagenomic Next-generation Sequencing (mNGS) for the Diagnosis of Suspected Neurological Infections Patients in China. Open Forum Infectious Diseases 10. Rodino KG, Simner PJ. 2024. Status check: next-generation sequencing for infectious-disease diagnostics. Journal of Clinical Investigation 134. Fuhrmann L, Langer B, Topolsky I, Beerenwinkel N. 2024. VILOCA: sequencing quality-aware viral haplotype reconstruction and mutation calling for short-read and long-read data. Nar Genomics and Bioinformatics 6. Li YJ, Cao JB, Wang J. 2023. MetaSVs: A pipeline combining long and short reads for analysis and visualization of structural variants in metagenomes. Imeta 2. Jia HX, Tan SJ, Cai YA, Guo YY, Shen JY, Zhang YQ, Ma HJ, Zhang QZ, Chen JF, Qiao GX, Ruan J, Zhang YE. 2024. Low-input PacBio sequencing generates high-quality individual fly genomes and characterizes mutational processes. Nature Communications 15. Bogaerts B, Van den Bossche A, Verhaegen B, Delbrassinne L, Mattheus W, Nouws S, Godfroid M, Hoffman S, Roosens NHC, De Keersmaecker SCJ, Vanneste K. 2024. Closing the gap: Oxford Nanopore Technologies R10 sequencing allows comparable results to Illumina sequencing for SNP-based outbreak investigation of bacterial pathogens. Journal of Clinical Microbiology 62. Salazar C, Ferrés I, Paz M, Costábile A, Moratorio G, Moreno P, Iraola G. 2023. Fast and cost- effective SARS- CoV-2 variant detection using Oxford Nanopore full- length spike gene sequencing. Microbial Genomics 9. Cook R, Telatin A, Hsieh SY, Newberry F, Tariq MA, Baker DJ, Carding SR, Adriaenssens EM. 2024. Nanopore and Illumina sequencing reveal different viral populations from human gut samples. Microbial Genomics 10. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8545804","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":573418900,"identity":"204b67db-9913-48bc-a380-4fd0b170b939","order_by":0,"name":"Lianghui Wei","email":"","orcid":"","institution":"Yangtze University","correspondingAuthor":false,"prefix":"","firstName":"Lianghui","middleName":"","lastName":"Wei","suffix":""},{"id":573418901,"identity":"969f36b4-43fb-45b8-a4a2-4993f76c0382","order_by":1,"name":"Li Zhu","email":"","orcid":"","institution":"Shenzhen International Travel Health Care Centre","correspondingAuthor":false,"prefix":"","firstName":"Li","middleName":"","lastName":"Zhu","suffix":""},{"id":573418902,"identity":"09e0f658-9bf0-4d72-ab37-b9e162e143a1","order_by":2,"name":"Jianzhong Ye","email":"","orcid":"","institution":"Shenzhen International Travel Health Care Centre","correspondingAuthor":false,"prefix":"","firstName":"Jianzhong","middleName":"","lastName":"Ye","suffix":""},{"id":573418903,"identity":"bb3a53ff-9947-4c04-8d15-1e17e3f64821","order_by":3,"name":"Ran Zhang","email":"","orcid":"","institution":"Shenzhen International Travel Health Care Centre","correspondingAuthor":false,"prefix":"","firstName":"Ran","middleName":"","lastName":"Zhang","suffix":""},{"id":573418904,"identity":"ff150ac1-a101-4b82-b3bf-9894410c9095","order_by":4,"name":"Ying Ye","email":"","orcid":"","institution":"Shenzhen International Travel Health Care Centre","correspondingAuthor":false,"prefix":"","firstName":"Ying","middleName":"","lastName":"Ye","suffix":""},{"id":573418905,"identity":"f87a07b3-e290-4902-acc5-d4e8e230a0f7","order_by":5,"name":"Chunzhong Zhao","email":"","orcid":"","institution":"Shenzhen International Travel Health Care Centre","correspondingAuthor":false,"prefix":"","firstName":"Chunzhong","middleName":"","lastName":"Zhao","suffix":""},{"id":573418906,"identity":"19d8c115-64d3-4222-aea9-02771ce9ae0e","order_by":6,"name":"Ye Yang","email":"","orcid":"","institution":"Yangtze University","correspondingAuthor":false,"prefix":"","firstName":"Ye","middleName":"","lastName":"Yang","suffix":""},{"id":573418907,"identity":"684c292d-36af-47e6-924e-ffec51e24317","order_by":7,"name":"Jianan He","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7klEQVRIiWNgGAWjYBACAwaGhAMgBj9/88EHHwxs7IjXIjnjWLLhjIK0ZGK0QBkNOWbSPB8OMTYQ0mLO3vDwwM8ddnkGDGeMjW0MDjAzsB8+ugGfFsueAwkHe88kF5sztxU+zjG4w8fAk5Z2A6/DbiQkHOBtY07c2XB4s3GOwTNmBgkeM/xa7j9IOPi3rT5xw4EEM2kLg8OMDQS13GBIOMzbdhioJcVMmoEoLWcSEg7Lth1PnAkK5B6DtGQ2gn45fib549u26sR+UFT++GNjx89++BheLQwMPAmofDb8ykGA/QBhNaNgFIyCUTCyAQAEp1ZthvalbAAAAABJRU5ErkJggg==","orcid":"","institution":"Shenzhen International Travel Health Care Centre","correspondingAuthor":true,"prefix":"","firstName":"Jianan","middleName":"","lastName":"He","suffix":""}],"badges":[],"createdAt":"2026-01-08 01:23:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8545804/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8545804/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":100373038,"identity":"a29e73ab-7f17-4a1a-be6e-b1032f45c457","added_by":"auto","created_at":"2026-01-16 08:13:32","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1231456,"visible":true,"origin":"","legend":"","description":"","filename":"MolecularEpidemiologyAnalysisofImportedChikun.docx","url":"https://assets-eu.researchsquare.com/files/rs-8545804/v1/a4aa1241fcb141be0d767f2b.docx"},{"id":100343139,"identity":"23dc9757-94af-4205-af9e-82e320ffb2c2","added_by":"auto","created_at":"2026-01-16 00:08:45","extension":"json","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":9159,"visible":true,"origin":"","legend":"","description":"","filename":"520240db808d4ebdb00cab0c4eb17397.json","url":"https://assets-eu.researchsquare.com/files/rs-8545804/v1/e9aae4d710267df8532d0ce0.json"},{"id":100343135,"identity":"1f4dd202-dae8-4c21-a884-f5bf5429a88b","added_by":"auto","created_at":"2026-01-16 00:08:45","extension":"xml","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":110202,"visible":true,"origin":"","legend":"","description":"","filename":"520240db808d4ebdb00cab0c4eb173971enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-8545804/v1/6f30de4deac05559d4892d3f.xml"},{"id":100373571,"identity":"2d98dcd4-11da-471f-80c0-b0b03d057f4b","added_by":"auto","created_at":"2026-01-16 08:15:01","extension":"png","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":778988,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8545804/v1/b4510d9712d78fdb8ced7f39.png"},{"id":100343155,"identity":"a190d6c5-4acf-446a-a064-a72f7d70fc4f","added_by":"auto","created_at":"2026-01-16 00:08:46","extension":"png","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":15344,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8545804/v1/1e022ddfb3d603939c7aaac6.png"},{"id":100343143,"identity":"19204e51-d5e2-4b36-8677-0b0ae6fecc2b","added_by":"auto","created_at":"2026-01-16 00:08:45","extension":"jpeg","order_by":5,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":511924,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8545804/v1/6e8389ecb585a0afb6a642dc.jpeg"},{"id":100343141,"identity":"7adc7d79-55d9-49f6-ad62-7a4f3124d676","added_by":"auto","created_at":"2026-01-16 00:08:45","extension":"jpeg","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":29990,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8545804/v1/e0763be0e60ecb2ef2a2a355.jpeg"},{"id":100343152,"identity":"f62738ef-4efc-412b-a9a5-453e0b694b68","added_by":"auto","created_at":"2026-01-16 00:08:46","extension":"jpeg","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":85206,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8545804/v1/105382f7ca2932e93997b813.jpeg"},{"id":100343149,"identity":"72b764d7-38d7-4b2e-818d-68bbaf498e26","added_by":"auto","created_at":"2026-01-16 00:08:46","extension":"png","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":245332,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-8545804/v1/71a75e578b36facbaa267519.png"},{"id":100379747,"identity":"79ea3a24-721c-4e72-9157-944a887a9fe3","added_by":"auto","created_at":"2026-01-16 09:17:00","extension":"png","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":79934,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8545804/v1/31a958c536e59439214de91f.png"},{"id":100343153,"identity":"3b9a454d-e532-4237-b83c-b9b2132ab6ee","added_by":"auto","created_at":"2026-01-16 00:08:46","extension":"png","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":5779,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8545804/v1/dc672a4ec986a41b8a92ac7d.png"},{"id":100343147,"identity":"17a7d909-212c-4f23-af1a-0ead38689f90","added_by":"auto","created_at":"2026-01-16 00:08:45","extension":"png","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":84019,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8545804/v1/86d9372c11661a213adcf1f8.png"},{"id":100373583,"identity":"178c660d-e122-47be-bb59-8f0c07d646e6","added_by":"auto","created_at":"2026-01-16 08:15:04","extension":"png","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":20174,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8545804/v1/2437b7440bf6a5ca3d0d5989.png"},{"id":100343140,"identity":"f59294e0-78b2-431a-a8c4-7e30e810634b","added_by":"auto","created_at":"2026-01-16 00:08:45","extension":"png","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":64576,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-8545804/v1/1ba23df279e894c4b9a4c121.png"},{"id":100378230,"identity":"a136c2f0-9a2b-4b59-9aa7-fe8d50872c63","added_by":"auto","created_at":"2026-01-16 08:51:11","extension":"png","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":30036,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-8545804/v1/510eb13c07242a02220ff4e3.png"},{"id":100373328,"identity":"a6fef745-236d-4ce9-b55b-f44b6446d732","added_by":"auto","created_at":"2026-01-16 08:14:05","extension":"xml","order_by":15,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":109112,"visible":true,"origin":"","legend":"","description":"","filename":"520240db808d4ebdb00cab0c4eb173971structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8545804/v1/b583a98ac76852b974727309.xml"},{"id":100343144,"identity":"43d21894-e77a-42a3-aff3-5f540307b397","added_by":"auto","created_at":"2026-01-16 00:08:45","extension":"html","order_by":16,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":117879,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8545804/v1/76475324b98ab5a8980b8b50.html"},{"id":100343134,"identity":"98146627-512d-4af6-934f-7513a249162f","added_by":"auto","created_at":"2026-01-16 00:08:45","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":778988,"visible":true,"origin":"","legend":"\u003cp\u003eqPCR results of three samples\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8545804/v1/09dcd24de1b2c92ab3b7abe4.png"},{"id":100343133,"identity":"c83ac6bb-549e-41a6-a53c-627b29fcb865","added_by":"auto","created_at":"2026-01-16 00:08:45","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":15344,"visible":true,"origin":"","legend":"\u003cp\u003eQ-Score Histogram\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8545804/v1/140ec7027c88a08fbccb5ce5.png"},{"id":100373400,"identity":"a324d291-c35f-49a8-a0b3-22b870929c84","added_by":"auto","created_at":"2026-01-16 08:14:16","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":511924,"visible":true,"origin":"","legend":"\u003cp\u003eComparison chart of regional coverage\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8545804/v1/68cc78093f67800e5a0050cc.jpeg"},{"id":100379467,"identity":"46f6c0cb-3b5f-4826-b907-2f52eaf9d2d0","added_by":"auto","created_at":"2026-01-16 09:13:36","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":29990,"visible":true,"origin":"","legend":"\u003cp\u003eMutation Overlap Pie Chart\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8545804/v1/b13527bc5a8e89c41c15ad3a.jpeg"},{"id":100379751,"identity":"1fb48df0-c3eb-43e3-8c6f-f2aaffa22817","added_by":"auto","created_at":"2026-01-16 09:17:09","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":85206,"visible":true,"origin":"","legend":"\u003cp\u003eMutation Density Distribution\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8545804/v1/49534e56d87c0e25203f829a.jpeg"},{"id":100343151,"identity":"cd677f52-fae5-49b8-bb62-f8e0fbb6776f","added_by":"auto","created_at":"2026-01-16 00:08:46","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":275929,"visible":true,"origin":"","legend":"\u003cp\u003ePhylogenetic Tree Source Tracking Analysis\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-8545804/v1/5778725eff5ad5adec53941d.png"},{"id":103019093,"identity":"08683e48-f227-4f13-a5ad-91ac2f2c9557","added_by":"auto","created_at":"2026-02-19 17:26:11","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2627588,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8545804/v1/38f177c1-cbab-4639-864e-4b1b3938d0d1.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Molecular Epidemiology Analysis of Imported Chikungunya Virus Cases Based on Second- and Third-Generation Sequencing","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eChikungunya fever (CHIKF), caused by the Chikungunya virus (CHIKV), is an arthropod-borne disease transmitted primarily by Aedes aegypti and Aedes albopictus mosquitoes. the most common symptoms of CHIKF are fever and joint pain, other symptoms can include rash, head ache and debilitating polyarthralgia. Since its re-emergence and global expansion in early 2000s, CHIKF has became a significant public health concern, causing large-scale outbreaks across Asia, Africa, and the Americas. Over the past 15 years, more than 5\u0026nbsp;million cases have been recorded across 119 countries and regions. With the continued global spread of Aedes albopictus, the risk of imported epidemics in tropical, and subtropical regions regions continues to rise, posing increasing challenges for border health control and highlighting the urgent need for genomic surveillance of CHIKV\u003csup\u003e[\u003cspan additionalcitationids=\"CR2 CR3\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eConventional RT-PCR assay is highly sensitive and rapid for CHIKV detection, however,it requirement for known target sequentces, means it may fail to detect novel variants or mixed infections and cannot provide comprehensive genomic information. Moreover, the genetic heterogeneity among CHIKV strains necessitates full-genome data to identify mutational hotspots, track transmission chains, and elucidate viral evolution. However, traditional assays fall short in resolving genome-wide diversity, structural variation, and in distinguishing vaccine strains from wild-type strains\u003csup\u003e[\u003cspan additionalcitationids=\"CR6 CR7\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eMetagenomic next-generation sequencing (mNGS) has emerged as a powerful unbiased tool that can detect all nucleic acid for pathogen discovery and genomic characterization. As an untargeted approach, mNGS enables large-scale parallel sequencing of all nucleic acids in a sample, enabling simultaneous detection of CHIKV and co-infecting pathogens while recovering genome-wide variation. This is especially valuable for febrile cases of unknown etiology and for discovering novel variants. However, mNGS also introduces notables limitations:More than 95% of reads typically originate from the host, resulting in insufficient effective sequencing data for pathogen genomes particularly in low-titer samples. Additionally, short-read sequencing (50\u0026ndash;300 bp) also complicates de novo assembly, often yielding fragmented contigs and incomplete mutation profiles\u003csup\u003e[\u003cspan additionalcitationids=\"CR10 CR11\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eOxford Nanopore sequencing (ONT), a third-generation platform, addresses these limitations by generating ultra-long reads, which enables the reconstruction of near-complete viral genomes without the need for assembly, thereby facilitating direct genotyping and accurate identification of key mutations.Moreover, by employing specific probes or primers to enrich CHIKV nucleic acids, this approach significantly reduces host background interference\u003csup\u003e[\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eCurrently, mNGS and ONT each have strengths in CHIKV research. mNGS is suitable for broad-spectrum pathogen screening and unknown pathogen identification, while ONT excels in high-precision analysis of known viruses. In port surveillance, sample numbers are limited and time is critical, but empirical data on how to strategically combine sequencing platforms for molecular epidemiology are scarce. Therefore, this study conducted a case series analysis of imported CHIKV infections detected at Shenzhen ports, integrating mNGS and ONT sequencing to comprehensively examine viral genome features and phylogenetic relationships, with a focus on evaluating cross-platform consistency and complementarity for case tracing and typing\u003csup\u003e[\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e"},{"header":"2.Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Sample Collection\u003c/h2\u003e \u003cp\u003eThis study included three serum samples from febrile travelers that were previously screened by RT-qPCR for CHIKV dentection at Shenzhen Customs between July 2024 and January 2025. All participants presented with fever (\u0026gt;\u0026thinsp;37\u0026deg;C) and at least one additional symptom (rash or arthralgia).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Nucleic Acid Extraction and RT-PCR Detection of CHIKV\u003c/h2\u003e \u003cp\u003eViral RNA was extracted from 200 \u0026micro;L serum using an automated nucleic acid extractor (Generotex96, Tianlong Technology, Xi\u0026rsquo;an, China) and following the supplier\u0026rsquo;s protocol, and eluted in 60 \u0026micro;L buffer. RT-PCR was performed using a commercial CHIKV nucleic acid detection kit (Pfizer Biotech, Zhuhai, China) on an ABI 7500 system (Applied Biosystems, USA).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Metagenomic Sequencing of Chikungunya Virus\u003c/h2\u003e \u003cp\u003eRNA libraries were prepared with the MGIEasy RNA Library Kit (MGI Tech, China). Libraries underwent dual-barcoding and DNB preparation before sequencing on the MGI200 platform (MGI Tech, China; PE100 mode). FASTQ files were assembled with MEGAHIT using CHIKV strain S27b03 (GenBank: PV685524.1) as reference. Genotyping was performed with BLASTn, and phylogenetic trees were inferred using IQ-TREE.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Third-Generation Targeted Sequencing of Chikungunya Virus\u003c/h2\u003e \u003cp\u003eLibraries were prepared using the CHIKV Targeted Sequencing Kit (Oxford Nanopore, SQK-RBK114.24). Following amplification and adapter ligation, libraries were quality-controlled with Qubit. Sequencing was performed on the MinION platform (ONT, FLO-MIN114 chip; Dipinore sequencer, China). Reads were reference-assembled, typed with BLASTn, and analyzed phylogenetically with IQ-TREE.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Bioinformatics Analysis\u003c/h2\u003e \u003cp\u003eSequencing reads were subjected to quality control to remove low-quality bases and adapters. For mNGS, host-derived reads were filtered to obtain CHIKV-related reads. For ONT, platform-recommended quality filtering was applied. CHIKV reads from both platforms were aligned to the reference genome to generate consensus sequences.\u003c/p\u003e \u003cp\u003eTo ensure cross-platform comparability, the study focused on genome coverage, coding region sequence consistency, and phylogenetic typing, rather than the absolute number of detected variants. Nucleotide identity (NT identity) was calculated as the percentage of matching bases with the reference genome, and amino acid identity (AA identity) was based on translated coding sequences. The same methods were applied for both platforms.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1 RT-PCR Results\u003c/h2\u003e\n \u003cp\u003eNucleic acids were extracted from the blood samples and tested for three vector-borne viruses using fluorescent RT-PCR. The results for Dengue virus and Zika virus were negative, while Chikungunya virus was positive. The Ct values for the positive samples were as follows(Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e): Sample 1: Ct 20.65; Sample 2: Ct 31.75; Sample 3: Ct 25.06, which can be categorized as high, low, and medium viral load, respectively. The positive control showed expected amplification, and the negative control showed no amplification signal, indicating no contamination during the experiment. The experimental data is reliable.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2 Metagenomic Sequencing Results\u003c/h2\u003e\n \u003cp\u003ePaired-end (PE100) mNGS generated high-quality data for all samples (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Average Q20 and Q30 values were 97.89\u0026thinsp;\u0026plusmn;\u0026thinsp;0.20% and 94.67\u0026thinsp;\u0026plusmn;\u0026thinsp;0.32%, respectively, exceeding industry thresholds.In terms of sequence composition, the mean proportion of host-derived sequences in the original sequencing sequences was (95.73\u0026thinsp;\u0026plusmn;\u0026thinsp;1.63)%, while the mean proportion of valid sequences was only (1.89\u0026thinsp;\u0026plusmn;\u0026thinsp;1.57)%. This result further confirmed that the high proportion of host sequences in macro-genome sequencing is prone to cause strong interference in the analysis of target sequences.Additionally, it is worth noting that although sample 1 had the lowest Ct value (typically indicating a relatively higher viral load), it only identified 884 sequences of chikungunya virus. The potential mechanism behind this phenomenon needs further in-depth analysis\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003emNGS results\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSample\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRaw Sequences\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eQ20\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eQ30\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHost Sequences\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHost Percentage\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEffective Sequences\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEffective Percentage\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCHIKV Sequences\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSample 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e51482297\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e97.78%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e94.47%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e50233032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e97.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e99871\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.19%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e884\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSample 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e54024210\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e97.77%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e94.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e51581597\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e95.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1189375\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e171155\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSample 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e29357713\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e98.12%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e95.04%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27705241\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e94.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e965519.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.29%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e38784\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3 Third-Generation Targeted Sequencing Results\u003c/h2\u003e\n \u003cp\u003eThird-generation targeted sequencing of Chikungunya virus yielded high-quality data for all samples. Sample 1 generated 78.41 Mb of raw sequencing data (1 Mb\u0026thinsp;=\u0026thinsp;10⁶ bases), with 94.5% passing quality control. A total of 148.32 K reads (1 K\u0026thinsp;=\u0026thinsp;10\u0026sup3; reads) were obtained, of which 92.3% met quality standards, indicating both high sequencing quality and sufficient effective coverage. Sample 2 produced 40.62 Mb of raw data, with 91.8% passing quality control, and 70.2 K reads, 93.9% of which were of acceptable quality. Sample 3 yielded 53.92 Mb of raw data, with 93.1% passing quality control, and 104.45 K reads, 92.7% of which were high-quality (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). The Q score distributions exhibited single, sharp peaks without noticeable fluctuations or multiple modes, reflecting a stable sequencing process, effective removal of low-quality reads, and the absence of sample contamination or chip obstruction. Peaks were predominantly centered around Q20(Figure\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e), confirming that the majority of reads had high-quality scores and that the results are reliable\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eResults of ONT\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSample\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTotal Raw Data\u003c/p\u003e\n \u003cp\u003e(Mb)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eQuality Data\u003c/p\u003e\n \u003cp\u003e(%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTotal Reads\u003c/p\u003e\n \u003cp\u003e(K)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eReads Meeting Quality Standards\u003c/p\u003e\n \u003cp\u003e(%)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSample 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e78.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e94.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e148.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e92.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSample 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e40.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e91.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e70.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e93.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSample 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e53.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e93.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e104.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e92.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003e3.4 Genomic Characteristics of Cases\u003c/h2\u003e\n \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e\n \u003ch2\u003e3.4.1 Time and Cost Comparison\u003c/h2\u003e\n \u003cp\u003eThe sequencing workflows of the two platforms were compared across three main stages: nucleic acid processing, library construction, and sequencing(Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). In the nucleic acid processing stage, second-generation mNGS workflow involved rRNA removal, RNA enrichment, fragmentation, and reverse transcription using commercial kits, taking approximately 3 hours. In contrast, third-generation CHIKV-targeted sequencing involves nucleic acid extraction and reverse transcription to cDNA, requiring only 0.5 hours. Fof library construction, mNGS involves double-strand synthesis, purification, adapter ligation, amplification, circularization, and DNB preparation, which collectively take around 6.5 hours. Third-generation targeted sequencing requires only amplification, adapter ligation, and purification, taking approximately 4 hours. For the sequencing stage, second-generation mNGS employs a PE100 protocol, requiring roughly 30 hours for sequencing plus an additional\u0026thinsp;~\u0026thinsp;2 hours for data analysis, which may increase with larger sample numbers. In contrast, third-generation targeted sequencing completes sequencing and data acquisition within approximately 8 hours. Regarding cost per sample, the mNGS approach was estimated at approximately \u003cspan\u003e$\u003c/span\u003e110, while the targeted ONT method cost about \u003cspan\u003e$\u003c/span\u003e140.\u003c/p\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003emNGS and ONT Tim and Cost Comparison\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003eMetric\n \u003c/div\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003emNGS\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eONT\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNucleic Acid Processing Time\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3h\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5h\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLibrary Construction Time\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.5h\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4h\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSequencing Analysis Time\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32h\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8h\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCost per Sample\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e110\u003cspan\u003e$\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e140\u003cspan\u003e$\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e\n \u003ch2\u003e3.4.2 Genome Features by Sequencing Method\u003c/h2\u003e\n \u003cp\u003eMetagenomic sequencing (mNGS) and nanopore sequencing (ONT) were performed on all three samples to obtain the CHIKV genome sequences. The results indicated differences among samples in genome coverage and nucleotide concordance. Samples 1 and 3 exhibited slightly higher coverage under ONT sequencing, whereas Sample 2 showed minimal differences between the two sequencing methods. The total sequencing length was comparable across both methods, indicating that overall genome integrity was well preserved. In terms of amino acid concordance, Sample 1 displayed higher stability in mNGS-derived sequences, while Samples 2 and 3 showed only minor differences. Overall, both sequencing approaches reliably provided genomic information suitable for case typing and epidemiological tracing, and offered a solid foundation for subsequent analyses of genetic variation and phylogeny.\u003c/p\u003e\n \u003ctable id=\"Tab4\" border=\"1\" class=\"fr-table-selection-hover\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eGenome Comparison Table of mNGS and ONT\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSample\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSequencing_Type\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCoverage(%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTotal_length\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNT_Identity(%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAA_Identity(%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOverall_Identity(%)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emNGS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e96.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11216\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e90.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e89.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e90.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eONT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e99.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11230\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e92.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e63.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e78.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emNGS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e100.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11237\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e96.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e95.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e96.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eONT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e100.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11237\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e96.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e95.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e96.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emNGS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e97.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11237\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e91.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e89.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e90.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eONT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e99.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11225\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e92.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e91.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e92.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e\n \u003ch2\u003e3.4.3 Regional Coverage\u003c/h2\u003e\n \u003cp\u003eThis study further compared the coverage performance of second-generation metagenomic sequencing (mNGS) and third-generation nanopore targeted sequencing (ONT) across different functional regions of the CHIKV genome. The CHIKV genome primarily comprises non-structural protein\u0026ndash;coding regions (P1, nsP2, nsP3, and nsP4), which are mainly involved in viral replication; structural protein\u0026ndash;coding regions (E3, E2, 6K, and E1), which constitute the viral particle; and non-coding regions (3\u0026prime; untranslated region [3\u0026prime; UTR]), which regulate viral RNA stability. As shown in Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e, both sequencing approaches achieved overall genome coverage exceeding 90% and effectively covered key functional regions of the CHIKV genome. Although certain differences in coverage depth and local completeness were observed among individual cases, the overall coverage was sufficient to meet the requirements for genotyping and phylogenetic analysis. These findings indicate that, under real-world surveillance sample conditions, both sequencing strategies can provide adequate genomic information to support case-level molecular epidemiological investigations.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e\n \u003ch2\u003e3.4.4 Key Variant Sites\u003c/h2\u003e\n \u003cp\u003eAcross the three clinical samples, the number of variant sites identified differed between sequencing approaches, while also demonstrating complementary detection profiles. As illustrated by the circos plot analysis(Figure 4), ONT sequencing identified 155 potential variant sites in Sample 1 and 78 sites in Sample 3, whereas Sample 2 showed only minor differences in the number of variants detected by the two methods. These additional variant sites were predominantly located within non-structural protein\u0026ndash;coding regions. Despite these differences, both sequencing approaches showed overall concordance in the core variant patterns within key functional regions, providing reliable information for downstream genome-wide variant analysis and phylogenetic studies. Overall, ONT sequencing appears advantageous for improving the comprehensiveness of variant detection in samples with higher genomic complexity or variability, while mNGS alone is sufficient for efficient variant identification in samples with simpler mutation profiles and high sequence homology. Therefore, sequencing strategies should be tailored to the complexity of genomic variation in target samples to achieve accurate and comprehensive variant detection.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e\n \u003ch2\u003e3.4.5 Mutation Site Genome Distribution Analysis\u003c/h2\u003e\n \u003cp\u003eBy analyzing the density distribution of shared variants and ONT-specific mutation sites, the differences in mutation detection between mNGS and ONT across the CHIKV genome were clearly illustrated(Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e). In Sample 1, shared mutations were evenly distributed in regions such as P1 and nsP2, whereas ONT-specific mutations were notably enriched in the E1 region, suggesting that ONT\u0026rsquo;s long-read capability enables the detection of complex structural variations that short-read mNGS may miss. In Sample 2, mutation profiles from both platforms were largely comparable, with ONT\u0026rsquo;s long-read advantage less pronounced. In Sample 3, ONT-specific mutations exhibited minor peaks in nsP3 and E1. Overall, mNGS provides stable detection of common mutations and consistent genome-wide coverage, while ONT is superior for identifying complex structural regions and specific mutations. The two methods exhibit clear complementarity in characterizing viral genetic diversity..\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec18\" class=\"Section3\"\u003e\n \u003ch2\u003e3.4.6 Source Tracking Analysis\u003c/h2\u003e\n \u003cp\u003eBased on the reconstructed phylogenetic tree, sequences generated by ONT and mNGS showed a high degree of concordance in clustering patterns for the same samples. Specifically, ONT1 clustered closely with mNGS1, ONT2 with mNGS2, and ONT3 with mNGS3, each forming paired branches with extremely short branch lengths, indicating minimal sequence divergence between the two sequencing approaches. Notably, all analyzed samples were consistently assigned to the selected reference clade without evidence of cross-lineage placement or misclassification. Phylogenetic analysis further demonstrated that all three samples clustered within the Asian lineage, confirming their genetic relatedness to Asian CHIKV strains. These results indicate that both sequencing methods reliably capture the phylogenetic signal of the samples. Overall, evolutionary tree reconstruction showed strong consistency between ONT and mNGS in whole-genome evolutionary inference, supporting their use as complementary approaches. While ONT offers advantages in sequence continuity, mNGS remains robust for high-coverage short-read detection.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Core Performance Differences and Application Scenario Adaptability of Sequencing Technologies\u003c/h2\u003e \u003cp\u003eAs a globally significant arbovirus, rapid genotyping and source tracing of Chikungunya virus (CHIKV) rely heavily on accurate and efficient sequencing technologies\u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e.. In this study, three imported CHIKV-positive cases collected by Shenzhen Customs were analyzed. Based on whole-genome sequencing data, the molecular epidemiological characteristics of the viruses associated with these cases were investigated, with the aim of providing data support for different application scenarios and contributing evidence for the optimization of infectious disease surveillance and control systems\u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe results showed that both metagenomic sequencing (mNGS) and nanopore sequencing (ONT) were able to generate near-complete CHIKV genome sequences sufficient for genotyping and phylogenetic analysis in this case series, although differences were observed in genome coverage characteristics and sequence continuity\u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e. Taking Case 1 as an example, ONT sequencing demonstrated advantages in overall coverage and sequence continuity, particularly in structurally complex non-structural protein\u0026ndash;coding regions (such as nsP3 and nsP4), where local coverage gaps were reduced. In contrast, due to the inherent short-read nature of mNGS, sequence fragmentation or discontinuous coverage was observed in certain genomic regions (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003e)\u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e. These findings suggest that different sequencing strategies may provide complementary information at the genome resolution level.\u003c/p\u003e \u003cp\u003eDifferences were also observed between sequencing approaches in variant site detection across cases. Again, in Case 1, ONT sequencing identified a higher number of potential variant sites than mNGS, with some variants concentrated in the E1 coding region. It should be emphasized that the number of detected variant sites was not used in this study as a metric for evaluating the sensitivity or accuracy of sequencing methods, but rather as an observational outcome reflecting case-level genomic variation. Importantly, these differences did not affect branch assignment or source attribution in phylogenetic analyses, indicating that consensus sequences generated across platforms show good concordance for molecular epidemiological interpretation\u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Improving Detection Performance via Technological Complementarity\u003c/h2\u003e \u003cp\u003eCase-level analyses indicated that mNGS and ONT exhibit complementary characteristics in variant site detection, with these differences being more pronounced in samples with relatively complex genomic structures. For example, in Case 1, ONT sequencing identified a greater number of potential variant sites than mNGS, and a similar pattern was also observed in Case 3. These findings suggest that, in samples with complex genomic architecture or in regions where coverage is challenging, reliance on a single sequencing strategy may be insufficient to capture the full spectrum of genomic variation. Integrating data generated by different sequencing approaches may therefore facilitate a more comprehensive characterization of case-associated variant distributions, thereby reducing the risk of information loss attributable to platform-specific biases.From a technical perspective, mNGS demonstrates stable performance in routine variant detection and base-level concordance, whereas ONT, benefiting from its long-read capability, can provide supplementary sequence information in regions that are difficult to be continuously covered by short-read sequencing. Consequently, the two sequencing strategies offer complementary insights in case-based genomic analyses\u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e4.3Limitations of the Study and Future Directions\u003c/h2\u003e \u003cp\u003eThis study has certain limitations in the analysis of the association between third-generation ONT targeted sequencing data characteristics and sample Ct values, which should be objectively acknowledged and may inform directions for future optimization. First, the small sample size limits the generalizability of the conclusions. Only three samples were included in this analysis, and although a potential association between Ct values and raw data yield or total read counts was preliminarily observed, results derived from such a limited dataset are susceptible to the influence of inter-sample variability and stochastic errors introduced during library preparation. Consequently, these findings are insufficient to comprehensively characterize sequencing data patterns across different viral load gradients. Future studies should expand the sample size and include samples from diverse sources and across a broader range of viral loads to validate these associations and improve the robustness and applicability of the conclusions.\u003c/p\u003e \u003cp\u003eIn addition, the data characteristics observed in individual samples suggest that sequencing outcomes may be influenced by multiple technical factors. For example, although Sample 1 exhibited the lowest Ct value by RT-qPCR, indicating a relatively high viral load, a smaller number of CHIKV-related sequences was identified in the metagenomic sequencing results. This discrepancy may be attributable to fluctuations in host nucleic acid abundance, differences in RNA integrity, and stochastic effects during library construction in the mNGS workflow. As metagenomic sequencing does not rely on targeted enrichment, samples with a high host background or uneven RNA fragmentation may yield fewer target pathogen reads despite high viral loads, due to insufficient allocation of effective sequencing depth. These observations indicate that, at the case level, the relationship between Ct values and the number of target pathogen reads obtained by mNGS is not strictly linear and should be interpreted in the context of sequencing strategy characteristics and sample quality.\u003c/p\u003e \u003cp\u003eBased on these limitations, future studies should expand the sample cohort and systematically include samples spanning different Ct value ranges and sources, enabling stratified and multivariate analyses to more comprehensively assess the impact of viral load on ONT targeted sequencing data characteristics. Moreover, it would be beneficial to integrate raw sequencing metrics with downstream analytical outcomes, such as genome assembly completeness, coverage depth distribution, and functional annotation adequacy, to more thoroughly evaluate the practical implications of Ct values on sequencing data utility. Finally, to address potential confounding factors such as host sequence abundance and amplification bias, further optimization of library preparation and targeted enrichment strategies may improve the efficiency of target pathogen sequence recovery in low viral load samples, thereby providing a more robust technical foundation for the application of ONT targeted sequencing in low-load pathogen detection and genome characterization\u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis study compared the performance of second-generation metagenomic sequencing (mNGS) and third-generation nanopore sequencing (ONT) in molecular epidemiological analyses based on three imported Chikungunya virus (CHIKV) cases. The results demonstrated that both sequencing strategies were able to generate near-complete CHIKV genome sequences sufficient for case genotyping and phylogenetic source tracing, showing a high level of concordance in phylogenetic placement and source attribution. The observed differences between the two approaches were mainly related to sequencing strategy, sequence continuity, and modes of information representation\u003csup\u003e[31]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eIn port-of-entry surveillance settings where rapid turnaround is critical, amplicon-based ONT sequencing shows practical potential by shortening sequencing time and improving genome continuity, thereby supporting rapid case-level genotyping and preliminary source tracing. In contrast, mNGS, owing to its untargeted nature, remains valuable for broad-spectrum pathogen screening, detection of mixed infections, and surveillance of unknown or unexpected pathogens. Overall, the case-based analyses indicate that mNGS and ONT provide complementary strengths in CHIKV molecular epidemiological investigations\u003csup\u003e[32]\u003c/sup\u003e. In routine surveillance practice, flexible selection or combined application of these sequencing strategies according to sample characteristics and analytical requirements may enhance genome reconstruction completeness and result robustness, providing more reliable technical support for the monitoring and source tracing of imported CHIKV cases\u003csup\u003e[33]\u003c/sup\u003e.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate：\u003c/strong\u003eThis study was reviewed and approved by the Shenzhen International Travel Health Care Centre Ethics Committee (Approval No: [BJZX20250008]). Written informed consent was obtained from all participants. The study was conducted in accordance with the principles of the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication：\u003c/strong\u003eNot applicable. No identifiable personal or clinical details, images, or videos of any individual person are included in this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials：\u003c/strong\u003eThe datasets generated and/or analyzed during the current study have been deposited in the Genome Sequence Archive (GSA) under the accession number CRA030563 (submission ID: subCRA049893, title: Chikungunya virus). The data are publicly accessible at https://ngdc.cncb.ac.cn/gsa/browse/CRA030563\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors Contributions\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003e Lianghui Wei was responsible for article writing and experiments, Li Zhu for data analysis,Jianzhong Ye for reagent purchasing, Ran Zhang and Chunchong Zhao for instrument management, Ying Ye for organizing experimental literature, and Ye Yang and Jian\u0026apos;an He for technical guidance and experimental scheme design.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e This work was supported by (2024YFC2310205): Research and Application of Rapid Multi-pathogen Identification Technology Based on Real-time Sequencing; General Administration of Customs Research Project (2024HK058): Research and Development of Intelligent Rapid Recognition Technology for Port Vectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed Consent Statement:\u0026nbsp;\u003c/strong\u003eThe informed consent of all the research subjects has been obtained.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement:\u003c/strong\u003e The data that has been used is confidential.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCo\u003c/strong\u003e\u003cstrong\u003empeting\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eInterest\u003c/strong\u003e\u003cstrong\u003es:\u003c/strong\u003e All authors declare that there are no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u003c/strong\u003e Thanks are extended to all participants for their valuable advice and support during this study. This article is \u0026copy; 2026 by Lianghui Wei. All rights reserved.\u003c/p\u003e"},{"header":"References","content":"\u003col class=\"decimal_type\"\u003e\n \u003cli\u003eJavaid A, Ijaz A, Ashfaq UA, Arshad M, Irshad S, Saif S.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e2022. An overview of chikungunya virus molecular biology, epidemiology, pathogenesis, treatment and prevention strategies. Future Virology 17:593-606.\u003c/li\u003e\n \u003cli\u003eNi J, Li ZF, Hu XW, Zhou H, Gong ZY. 2025. Chikungunya\u0026apos;s global rebound and Asia\u0026apos;s growing vulnerability: Implications for integrated vector control and pandemic preparedness. Bioscience Trends doi:10.5582/bst.2025.01239.\u003c/li\u003e\n \u003cli\u003eWebb EM, Compton A, Rai P, Chuong C, Paulson SL, Tu ZJ, Weger-Lucarelli J. 2023. Expression of anti-chikungunya single-domain antibodies in transgenic reduces vector competence for chikungunya virus and Mayaro virus. Frontiers in Microbiology 14.\u003c/li\u003e\n \u003cli\u003eResck MEB, Camara DCP, dos Santos FB, dos Santos JPC, Alto BW, Hon\u0026oacute;rio NA. 2024. Spatial-temporal distribution of chikungunya virus in Brazil: a review on the circulating viral genotypes and as a potential vector. Frontiers in Public Health 12.\u003c/li\u003e\n \u003cli\u003eDieng I, Ndiaye M, Kane M, Balde D, Mbanne M, Diop SMBS, Sankhe S, Dia M, Dieng M, Doukanda SFM, Faye O, Sall AA, Dia N, Fall G, Faye O, Diagne MM. 2023. An amplicon-based Illumina and nanopore sequencing workflow for Chikungunya virus West Africa genotype. medRxiv doi:10.1101/2023.12.07.23299611.\u003c/li\u003e\n \u003cli\u003eCardona-Trujillo MC, Ocampo-C\u0026aacute;rdenas T, Tabares-Villa FA, Zuluaga-V\u0026eacute;lez A, Sep\u0026uacute;lveda-Arias JC. 2022. Recent molecular techniques for the diagnosis of Zika and Chikungunya infections: A systematic review. Heliyon - 8:e10225.\u003c/li\u003e\n \u003cli\u003eSchmitz KS, Comvalius AD, Nieuwkoop NJ, Geers D, Weiskopf D, Ramsauer K, Sette A, Tschismarov R, de Vries RD, de Swart RL. 2023. A measles virus-based vaccine induces robust chikungunya virus-specific CD4+T-cell responses in a phase II clinical trial. Vaccine 41:6495-6504.\u003c/li\u003e\n \u003cli\u003eBelem LRW, Ibemgbo SA, Gomgnimbou MK, Verma DK, Kabor\u0026eacute; A, Kumar A, Sangar\u0026eacute; I, Sunil S. 2024. Development of Multiplex Molecular Assays for Simultaneous Detection of Dengue Serotypes and Chikungunya Virus for Arbovirus Surveillance. Current Issues in Molecular Biology 46:2093-2104.\u003c/li\u003e\n \u003cli\u003eOlearo F, El Zein S, Portillo ME, Zapf A, Rohde H, Berbari EF, Wouthuyzen-Bakker M, ESGIAI ESGIAI, Mol Working Grp Unified PJIDTF. 2025. Diagnostic accuracy of 16S rDNA PCR, multiplex PCR and metagenomic next-generation sequencing in periprosthetic joint infections: a systematic review and meta-analysis. Clinical Microbiology and Infection 31:1115-1125.\u003c/li\u003e\n \u003cli\u003eBollor\u0026eacute; K, Tinto B, Charriat F, Pisoni A, Exbrayat A, Gutierrez S, Simonin Y, Tuaillon E. 2025. Investigation of the viral causes of febrile jaundice in Burkina Faso through metagenomic sequencing. Journal of Infection 91.\u003c/li\u003e\n \u003cli\u003eZheng N, Yu HL, Zhang BJ, Wang D, Ji YL, Dai LL, Li W, Li SH, Hu ZL, Zheng YS. 2025. Metagenomic next-generation sequencing-based characterization of the viral spectrum in clinical pulmonary and peripheral blood samples of patients. Frontiers in Cellular and Infection Microbiology 15.\u003c/li\u003e\n \u003cli\u003eBai Y, Li Y, Liu W, Liu L, Tong Y. 2022. Application of next-generation sequencing in the detection of emerging outbreak of virus infection. Chinese Journal of Experimental and Clinical Virology 36:739-747.\u003c/li\u003e\n \u003cli\u003eWei-ying Chen, Yang-sen Qin, Ting-fu Zhang, Jian Zou, Jun Yang, Zhen-yong Chen.2025.A chromosome-level genome assembly of Termitomyces fuliginosus using Oxford Nanopore and Hi-C sequencing.Genomics.ISSN 0888-7543.\u003c/li\u003e\n \u003cli\u003eJavaran VJ, Moffett P, Lemoyne P, Xu D, Adkar-Purushothama CR, Fall ML. 2021. Grapevine Virology in the Third-Generation Sequencing Era: From Virus Detection to Viral Epitranscriptomics. Plants-Basel 10.\u003c/li\u003e\n \u003cli\u003eBrinkmann A, Pape K, Uddin S, Woelk N, F\u0026ouml;rster S, Jessen H, Michel J, Kohl C, Schaade L, Nitsche A. 2024. Genome sequencing of the mpox virus 2022 outbreak with amplicon-based Oxford Nanopore MinION sequencing. Journal of Virological Methods 325.\u003c/li\u003e\n \u003cli\u003eDabernig-Heinz J, Lohde M, H\u0026ouml;lzer M, Cabal A, Conzemius R, Brandt C, Kohl M, Halbedel S, Hyden P, Fischer MA, Pietzka A, Daza B, Idelevich EA, St\u0026ouml;ger A, Becker K, Fuchs S, Ruppitsch W, Steinmetz I, Kohler C, Wagner GE. 2024. A multicenter study on accuracy and reproducibility of nanopore sequencing-based genotyping of bacterial pathogens. Journal of Clinical Microbiology 62.\u003c/li\u003e\n \u003cli\u003eStefan CP, Hall AT, Graham AS, Minogue TD. 2022. Comparison of Illumina and Oxford Nanopore Sequencing Technologies for Pathogen Detection from Clinical Matrices Using Molecular Inversion Probes. Journal of Molecular Diagnostics 24:395-405.\u003c/li\u003e\n \u003cli\u003eWang J, Zhang R, Sun Z, Ren H, Chen C, Dong X, Ma X. 2020. Detection of the Chikungunya Virus and Sindbis Viruses in Simulated Clinical Samples Using Metagenomic Nanopore Sequencing. Chinese Journal of Virology 36:377-384.\u003c/li\u003e\n \u003cli\u003eTakemae N, Kuba Y, Oba K, Kageyama T. 2024. Direct genome sequencing of respiratory viruses from low viral load clinical specimens using the target capture sequencing technology. Microbiology Spectrum 12.\u003c/li\u003e\n \u003cli\u003eNing XH, Xia BH, Wang JQ, Gao R, Ren H. 2024. Host-adaptive mutations in Chikungunya virus genome. Virulence 15.\u003c/li\u003e\n \u003cli\u003eDa H, Meng T, Xu YH. 2025. Application of targeted next-generation sequencing for detecting respiratory pathogens in the sputum of patients with pulmonary infections. Infection Genetics and Evolution 128.\u003c/li\u003e\n \u003cli\u003eZhang SS, Li X, Li X, Fu Y, Chen L, Wang W, Lin QQ, Lou H, Yao YK, Chen WY, Zhong C, Ye J, Yao YN, Guo HM, Yu YS, Zhou H. 2025. Optimisation and clinical validation of a metagenomic third-generation sequencing approach for aetiological diagnosis in bronchoalveolar lavage fluid of patients with. Ebiomedicine 116.\u003c/li\u003e\n \u003cli\u003eGovender KN, Street TL, Sanderson ND, Leach L, Morgan M, Eyre DW. 2025. Rapid clinical diagnosis and treatment of common, undetected, and uncultivable bloodstream infections using metagenomic sequencing from routine blood cultures with Oxford Nanopore. medRxiv doi:10.1101/2025.01.08.25320182.\u003c/li\u003e\n \u003cli\u003ede Souza LM, de Oliveira ID, Sales FCS, da Costa AC, Campos KR, Abbud A, Guerra JM, Borges CDC, Takahashi C, de Ara\u0026uacute;jo LJT. 2023. Technical comparison of MinIon and Illumina technologies for genotyping Chikungunya virus in clinical samples. Journal of Genetic Engineering and Biotechnology 21.\u003c/li\u003e\n \u003cli\u003ePeng MF, Davis ML, Bentz ML, Burgin A, Burroughs M, Padilla J, Nobles S, Unoarumhi Y, Tang K. 2025. Short-Read and Long-Read Whole Genome Sequencing for SARS-CoV-2 Variants Identification. Viruses-Basel 17.\u003c/li\u003e\n \u003cli\u003ePeng SY, Liang R, Liu P, Hu L, Ai YP, Wang JF, Hong GQ, Qu SL. 2023. 557. Cost-effectiveness of Introducing Metagenomic Next-generation Sequencing (mNGS) for the Diagnosis of Suspected Neurological Infections Patients in China. Open Forum Infectious Diseases 10.\u003c/li\u003e\n \u003cli\u003eRodino KG, Simner PJ. 2024. Status check: next-generation sequencing for infectious-disease diagnostics. Journal of Clinical Investigation 134.\u003c/li\u003e\n \u003cli\u003eFuhrmann L, Langer B, Topolsky I, Beerenwinkel N. 2024. VILOCA: sequencing quality-aware viral haplotype reconstruction and mutation calling for short-read and long-read data. Nar Genomics and Bioinformatics 6.\u003c/li\u003e\n \u003cli\u003eLi YJ, Cao JB, Wang J. 2023. MetaSVs: A pipeline combining long and short reads for analysis and visualization of structural variants in metagenomes. Imeta 2.\u003c/li\u003e\n \u003cli\u003eJia HX, Tan SJ, Cai YA, Guo YY, Shen JY, Zhang YQ, Ma HJ, Zhang QZ, Chen JF, Qiao GX, Ruan J, Zhang YE. 2024. Low-input PacBio sequencing generates high-quality individual fly genomes and characterizes mutational processes. Nature Communications 15.\u003c/li\u003e\n \u003cli\u003eBogaerts B, Van den Bossche A, Verhaegen B, Delbrassinne L, Mattheus W, Nouws S, Godfroid M, Hoffman S, Roosens NHC, De Keersmaecker SCJ, Vanneste K. 2024. Closing the gap: Oxford Nanopore Technologies R10 sequencing allows comparable results to Illumina sequencing for SNP-based outbreak investigation of bacterial pathogens. Journal of Clinical Microbiology 62.\u003c/li\u003e\n \u003cli\u003eSalazar C, Ferr\u0026eacute;s I, Paz M, Cost\u0026aacute;bile A, Moratorio G, Moreno P, Iraola G. 2023. Fast and cost- effective SARS- CoV-2 variant detection using Oxford Nanopore full- length spike gene sequencing. Microbial Genomics 9.\u003c/li\u003e\n \u003cli\u003eCook R, Telatin A, Hsieh SY, Newberry F, Tariq MA, Baker DJ, Carding SR, Adriaenssens EM. 2024. Nanopore and Illumina sequencing reveal different viral populations from human gut samples. Microbial Genomics 10.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Chikungunya virus, molecular epidemiology, metagenomic NGS, Oxford Nanopore sequencing","lastPublishedDoi":"10.21203/rs.3.rs-8545804/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8545804/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective:\u003c/strong\u003e This study aimed to analyze the molecular epidemiological characteristics of imported Chikungunya virus (CHIKV) infections identified during port surveillance and to evaluate the consistency and complementarity of second-generation metagenomic next-generation sequencing (mNGS) and third-generation Oxford Nanopore Technologies (ONT) sequencing in real-world monitoring scenarios.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e Three CHIKV-positive serum samples, confirmed by RT-qPCR, were selected as a case series. Viral genomic sequences were obtained using mNGS and amplicon-based ONT sequencing. Sequencing data were assessed for quality, genome coverage, variant distribution, and phylogenetic relationships. Cross-platform concordance was used as an internal validation for molecular epidemiological interpretation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e Both sequencing approaches successfully generated near-complete CHIKV genomes and produced highly consistent typing and source-tracing results in phylogenetic analyses. ONT sequencing demonstrated superior performance in genome continuity and coverage of complex regions, identifying more potential variant sites, whereas mNGS exhibited greater stability at the amino acid level. Despite differences in the number of detected variants between platforms, sample origin determination and phylogenetic placement remained highly concordant.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eThis case-oriented study indicates that both second- and third-generation sequencing can reliably support molecular epidemiological investigations of imported CHIKV infections. mNGS and ONT each offer distinct advantages and can be strategically combined in practical applications to achieve efficient and precise molecular surveillance and source tracing of CHIKV.\u003c/p\u003e","manuscriptTitle":"Molecular Epidemiology Analysis of Imported Chikungunya Virus Cases Based on Second- and Third-Generation Sequencing","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-16 00:08:40","doi":"10.21203/rs.3.rs-8545804/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e308e36c-7bcd-459f-8f20-f22e63a235da","owner":[],"postedDate":"January 16th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-19T17:24:46+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-16 00:08:40","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8545804","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8545804","identity":"rs-8545804","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}