Evidence map›Paper›PMID 41983465›Full record

ArticleEmerging microbes & infections2026

Rapid and precise amplicon-based genome sequencing to preparedness and response chikungunya virus outbreaks.

Kaiying Wang, Jinhui Li, Tianyuan Liu, Peihan Li, Ning Wang, Yuqi Liu, Yufeng Liang, Fenxiang Li, Chuhong Wei, Huacheng Yan and 1 more

Abstract read
In one paragraph

Article in Emerging microbes & infections, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Kaiying WangChinese PLA Center for Disease Control and Prevention, Beijing, People's Republic of China.
Jinhui LiChinese PLA Center for Disease Control and Prevention, Beijing, People's Republic of China.
Tianyuan LiuChinese PLA Center for Disease Control and Prevention, Beijing, People's Republic of China.
Peihan LiChinese PLA Center for Disease Control and Prevention, Beijing, People's Republic of China.
Ning WangChinese PLA Center for Disease Control and Prevention, Beijing, People's Republic of China.
Yuqi LiuChinese PLA Center for Disease Control and Prevention, Beijing, People's Republic of China.
Yufeng LiangSouthern China Center for Disease Control and Prevention, Guangzhou, People's Republic of China.
Fenxiang LiSouthern China Center for Disease Control and Prevention, Guangzhou, People's Republic of China.
Chuhong WeiLecong Hospital of Shunde, Foshan, People's Republic of China.
Huacheng YanSouthern China Center for Disease Control and Prevention, Guangzhou, People's Republic of China.
Peng LiChinese PLA Center for Disease Control and Prevention, Beijing, People's Republic of China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Chikungunya virus (CHIKV) poses a significant global health threat. Effective genomic surveillance of CHIKV is critical for tracking transmission and evolution. To define an optimal framework, we systematically evaluated four next-generation sequencing (NGS) configurations combining second-generation (DNBSEQ-G99) and third-generation (QPursue-6k) chemistries with 200-bp or 400-bp amplicon schemes using 13 clinical samples (Ct 17.41-38.14). While second-generation sequencing, particularly with 400-bp amplicons, yielded the highest sequencing depth (mean 152,430×), third-generation platforms provided more uniform genome coverage (99.9%) and were resilient to amplification biases, especially in challenging regions like the capsid domain. Crucially, third-generation sequencing uncovered substantially greater intra-host viral diversity, detecting 1,553 and 756 unique variants with 200-bp and 400-bp amplicons, respectively, compared to only 416-417 variants from second-generation platforms. This included a broad spectrum of low-frequency variants (VAF < 0.5), which were largely missed by short-read methods. However, we observed that region-specific coverage biases in second-generation sequencing may cause false negatives, underscoring the importance of coverage-aware interpretation. Both platforms reliably identified known epidemiologically relevant mutations, such as E1-A226V. However, variant detection sensitivity correlated inversely with viral load for second-generation sequencing but showed a paradoxical positive correlation for third-generation sequencing, indicating platform-dependent performance at low template concentrations. Ct-stratified analysis revealed that in low-coverage, ultra-low viral load samples, third-generation (44.54%-77.32%) sequencing outperforms 2nd-seq (0.85%-27.88%) in variant detection. Our results establish that third-generation sequencing with amplicon-based enrichment is superior for comprehensive variant recovery and uniform coverage, providing a robust solution for high-resolution CHIKV genomic surveillance even in ultra-low viral load samples.

Indexed as

Chikungunya FeverChikungunya virusDisease OutbreaksGenome, ViralHigh-Throughput Nucleotide SequencingGenetic VariationHumansChikungunya virusgenomic surveillancesecond-generation sequencingthird-generation sequencingvariant detection

Identifiers

PMID41983465
PMCPMC13123059

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.