Evidence map›Paper›PMID 37698427›Full record

ArticleMicrobiology spectrum2023

Identifying featured indels associated with SARS-CoV-2 fitness.

Xiang Li, Hongliang Yan, Gary Wong, Wanli Ouyang, Jie Cui

Abstract read
In one paragraph

Article in Microbiology spectrum, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

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

5 authors.

Xiang Li *CAS Key Laboratory of Molecular Virology & Immunology, Shanghai Institute of Immunity and Infection, Chinese Academy of Sciences , Shanghai, China.ORCID 0000-0003-0983-8339
Hongliang Yan *AI for Science, Shanghai Artificial Intelligence Laboratory , Shanghai, China.
Gary WongCAS Key Laboratory of Molecular Virology & Immunology, Shanghai Institute of Immunity and Infection, Chinese Academy of Sciences , Shanghai, China.
Wanli OuyangAI for Science, Shanghai Artificial Intelligence Laboratory , Shanghai, China.
Jie CuiCAS Key Laboratory of Molecular Virology & Immunology, Shanghai Institute of Immunity and Infection, Chinese Academy of Sciences , Shanghai, China.ORCID 0000-0001-8176-9951

Funding

National Key Research and Development Projects of the Ministry of Science and Technology of China 2021YFC2301300
6 · The paper itself

Abstract

importanceThe fitness of indels in pathogen genome evolution has rarely been studied. We developed a computational methodology to investigate the severe acute respiratory coronavirus 2 genomes and analyze over 33 million records of indels systematically, ultimately proposing the concept of featured indels that can represent specific Pango lineages and identifying 65 featured indels. Machine learning model based on Bayesian inference and viral lineage growth rate evaluation suggests that these featured indels exhibit selection pressure comparable to replacement mutations. In conclusion, indels are not negligible for evaluating viral fitness.

Indexed as

evolutionfeatured indelsgenomic epidemiologymachine learningSARS-CoV-2

Identifiers

PMID37698427
PMCPMC10580940

What OpenQuestion holds

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LicenceCC BY
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.