Evidence map›Paper›PMID 35632828›Full record

ArticleViruses2022

Detecting Potentially Adaptive Mutations from the Parallel and Fixed Patterns in SARS-CoV-2 Evolution.

Cheng-Yang Ji, Na Han, Ye-Xiao Cheng, Jingzhe Shang, Shenghui Weng, Rong Yang, Hang-Yu Zhou, Aiping Wu

Abstract read
In one paragraph

Article in Viruses, 2022. 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. Review
  2. Article
  3. Review
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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

8 authors.

Cheng-Yang JiInstitute of Systems Medicine, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing 100005, China.ORCID 0000-0001-9258-5453
Na HanInstitute of Systems Medicine, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing 100005, China.
Ye-Xiao ChengInstitute of Systems Medicine, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing 100005, China.
Jingzhe ShangInstitute of Systems Medicine, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing 100005, China.
Shenghui WengInstitute of Systems Medicine, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing 100005, China.
Rong YangInstitute of Systems Medicine, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing 100005, China.
Hang-Yu ZhouInstitute of Systems Medicine, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing 100005, China.ORCID 0000-0002-3762-2319
Aiping WuInstitute of Systems Medicine, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing 100005, China.ORCID 0000-0002-5869-651X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Early identification of adaptive mutations could provide timely help for the control and prevention of the COVID-19 pandemic. The fast accumulation of SARS-CoV-2 sequencing data provides important support, while also raising a great challenge for the recognition of adaptive mutations. Here, we proposed a computational strategy to detect potentially adaptive mutations from their fixed and parallel patterns in the phylogenetic trajectory. We found that the biological meanings of fixed substitution and parallel mutation are highly complementary, and can reasonably be integrated as a fixed and parallel (paraFix) mutation, to identify potentially adaptive mutations. Tracking the dynamic evolution of SARS-CoV-2, 37 sites in spike protein were identified as having experienced paraFix mutations. Interestingly, 70% (26/37) of them have already been experimentally confirmed as adaptive mutations. Moreover, most of the mutations could be inferred as paraFix mutations one month earlier than when they became regionally dominant. Overall, we believe that the concept of paraFix mutations will help researchers to identify potentially adaptive mutations quickly and accurately, which will provide invaluable clues for disease control and prevention.

Indexed as

COVID-19SARS-CoV-2HumansMutationPandemicsPhylogenyCOVID-19evolutionmutationSARS-CoV-2

Identifiers

PMID35632828
PMCPMC9147038

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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.