Evidence map›Paper›PMID 34512928›Full record

ArticleComputational and structural biotechnology journal2021

AutoVEM2: A flexible automated tool to analyze candidate key mutations and epidemic trends for virus.

Binbin Xi, Zixi Chen, Shuhua Li, Wei Liu, Dawei Jiang, Yunmeng Bai, Yimo Qu, Jerome Rumdon Lon, Lizhen Huang, Hongli Du

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Computational and structural biotechnology journal, 2021. 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
0.3field-weighted citation impact, top 42% of its field
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, 4 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Applying the digital data and the bioinformatics tools in SARS-CoV-2 research.Computational and structural biotechnology journal · 2023
    Review
  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

10 authors at 1 institution in 1 country.

Binbin XiSchool of Biology and Biological Engineering, South China University of Technology, Guangzhou 510006, China.
Zixi ChenSchool of Biology and Biological Engineering, South China University of Technology, Guangzhou 510006, China.
Shuhua LiSchool of Biology and Biological Engineering, South China University of Technology, Guangzhou 510006, China.
Wei LiuSchool of Biology and Biological Engineering, South China University of Technology, Guangzhou 510006, China.
Dawei JiangSchool of Biology and Biological Engineering, South China University of Technology, Guangzhou 510006, China.
Yunmeng BaiSchool of Biology and Biological Engineering, South China University of Technology, Guangzhou 510006, China.
Yimo QuSchool of Biology and Biological Engineering, South China University of Technology, Guangzhou 510006, China.
Jerome Rumdon LonSchool of Biology and Biological Engineering, South China University of Technology, Guangzhou 510006, China.
Lizhen HuangSchool of Biology and Biological Engineering, South China University of Technology, Guangzhou 510006, China.
Hongli DuSchool of Biology and Biological Engineering, South China University of Technology, Guangzhou 510006, China.
South China University of Technology · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In our previous work, we developed an automated tool, AutoVEM, for real-time monitoring the candidate key mutations and epidemic trends of SARS-CoV-2. In this research, we further developed AutoVEM into AutoVEM2. AutoVEM2 is composed of three modules, including call module, analysis module, and plot module, which can be used modularly or as a whole for any virus, as long as the corresponding reference genome is provided. Therefore, it's much more flexible than AutoVEM. Here, we analyzed three existing viruses by AutoVEM2, including SARS-CoV-2, HBV and HPV-16, to show the functions, effectiveness and flexibility of AutoVEM2. We found that the N501Y locus was almost completely linked to the other 16 loci in SARS-CoV-2 genomes from the UK and Europe. Among the 17 loci, 5 loci were on the S protein and all of the five mutations cause amino acid changes, which may influence the epidemic traits of SARS-CoV-2. And some candidate key mutations of HBV and HPV-16, including T350G of HPV-16 and C659T of HBV, were detected. In brief, we developed a flexible automated tool to analyze candidate key mutations and epidemic trends for any virus, which would become a standard process for virus analysis based on genome sequences in the future.

Indexed as

Automated toolCandidate key mutationsEpidemic trendsHaplotypesHBVHPV-16SARS-CoV-2Virus

Identifiers

PMID34512928
PMCPMC8416686
OpenAlexW3197075760

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
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.