Evidence map›Paper›PMID 39399153›Full record

ArticleVirus evolution2024

Comprehensive detection and dissection of interlineage recombination events in the SARS-CoV-2 pandemic.

Jia-Ying Li, Hao-Yang Wang, Ye-Xiao Cheng, Chengyang Ji, Shenghui Weng, Na Han, Rong Yang, Hang-Yu Zhou, Aiping Wu

Abstract read
In one paragraph

Article in Virus evolution, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Article
  2. A Pandemic-Scale Ancestral Recombination Graph for SARS-CoV-2.bioRxiv : the preprint server for biology · 2025
    Article
  3. Review
  4. Article
  5. Article
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

9 authors.

Jia-Ying LiState Key Laboratory of Common Mechanism Research for Major Diseases, Suzhou Institute of Systems Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, No. 100 Chongwen Road, Suzhou Industrial Park, Suzhou, Jiangsu 215123, China.ORCID https://orcid.org/0000-0002-5252-5322
Hao-Yang WangState Key Laboratory of Common Mechanism Research for Major Diseases, Suzhou Institute of Systems Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, No. 100 Chongwen Road, Suzhou Industrial Park, Suzhou, Jiangsu 215123, China.ORCID https://orcid.org/0009-0009-8086-7010
Ye-Xiao ChengState Key Laboratory of Common Mechanism Research for Major Diseases, Suzhou Institute of Systems Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, No. 100 Chongwen Road, Suzhou Industrial Park, Suzhou, Jiangsu 215123, China.
Chengyang JiState Key Laboratory of Common Mechanism Research for Major Diseases, Suzhou Institute of Systems Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, No. 100 Chongwen Road, Suzhou Industrial Park, Suzhou, Jiangsu 215123, China.
Shenghui WengState Key Laboratory of Common Mechanism Research for Major Diseases, Suzhou Institute of Systems Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, No. 100 Chongwen Road, Suzhou Industrial Park, Suzhou, Jiangsu 215123, China.
Na HanState Key Laboratory of Common Mechanism Research for Major Diseases, Suzhou Institute of Systems Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, No. 100 Chongwen Road, Suzhou Industrial Park, Suzhou, Jiangsu 215123, China.
Rong YangState Key Laboratory of Common Mechanism Research for Major Diseases, Suzhou Institute of Systems Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, No. 100 Chongwen Road, Suzhou Industrial Park, Suzhou, Jiangsu 215123, China.
Hang-Yu ZhouState Key Laboratory of Common Mechanism Research for Major Diseases, Suzhou Institute of Systems Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, No. 100 Chongwen Road, Suzhou Industrial Park, Suzhou, Jiangsu 215123, China.ORCID https://orcid.org/0000-0002-3762-2319
Aiping WuState Key Laboratory of Common Mechanism Research for Major Diseases, Suzhou Institute of Systems Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, No. 100 Chongwen Road, Suzhou Industrial Park, Suzhou, Jiangsu 215123, China.ORCID https://orcid.org/0000-0002-5869-651X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The global prevalence of the XBB lineage presents a formidable challenge posed by the recombinant SARS-CoV-2 virus. The understanding of SARS-CoV-2's recombination preference assumes utmost significance in predicting future recombinant variants and adequately preparing for subsequent pandemics. Thus, an urgent need arises to establish a comprehensive landscape concerning SARS-CoV-2 recombinants worldwide and elucidate their evolutionary mechanisms. However, the initial step, involving the detection of potential recombinants from a vast pool of over 10 million sequences, presents a significant obstacle. In this study, we present CovRecomb, a lightweight methodology specifically designed to effectively identify and dissect interlineage SARS-CoV-2 recombinants. Leveraging CovRecomb, we successfully detected 135,567 putative recombinants across the entirety of 14.5 million accessed SARS-CoV-2 genomes. These putative recombinants could be classified into 1451 distinct recombination events, of which 206 demonstrated transmission spanning multiple countries, continents, or globally. Hotspot regions were identified in six specific areas, with prominence observed in the latter halves of the N-terminal domain and receptor-binding domain within the

Indexed as

breakpointfeature mutationlineagepreferencerecombinationSARS-CoV-2transmission

Identifiers

PMID39399153
PMCPMC11470760

What Socratic 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 Socratic graph.