Evidence map›Paper›PMID 35640977›Full record

ArticleBioinformatics (Oxford, England)2022

VOC-alarm: mutation-based prediction of SARS-CoV-2 variants of concern.

Hongyu Zhao, Kun Han, Chao Gao, Vithal Madhira, Umit Topaloglu, Yong Lu, Guangxu Jin

Abstract read
In one paragraph

Article in Bioinformatics (Oxford, England), 2022. 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.

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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

7 authors.

Hongyu ZhaoDepartment of Cancer Biology, Wake Forest School of Medicine, Winston-Salem, NC 27157, USA.
Kun HanDepartment of Microbiology and Immunology, Wake Forest School of Medicine, Winston-Salem, NC 27101, USA.
Chao GaoDepartment of Gynecology and Obstetrics, Tianjin Medical University General Hospital, Tianjin, China.
Vithal MadhiraPalila Software LLC, Reno, NV 89521, USA.
Umit TopalogluDepartment of Cancer Biology, Wake Forest School of Medicine, Winston-Salem, NC 27157, USA.
Yong LuDepartment of Microbiology and Immunology, Wake Forest School of Medicine, Winston-Salem, NC 27101, USA.
Guangxu JinDepartment of Cancer Biology, Wake Forest School of Medicine, Winston-Salem, NC 27157, USA.ORCID 0000-0001-9451-4886

Funding

Tumor Tissue CoreP30CA012197 · NCI · WAKE FOREST UNIVERSITY HEALTH SCIENCES · PI Ruben A. Mesa · 1985 to 2026
$55.4M
NCI NIH HHS P30 CA012197
6 · The paper itself

Abstract

summaryMutation is the key for a variant of concern (VOC) to overcome selective pressures, but this process is still unclear. Understanding the association of the mutational process with VOCs is an unmet need. Motivation: Here, we developed VOC-alarm, a method to predict VOCs and their caused COVID surges, using mutations of about 5.7 million SARS-CoV-2 complete sequences. We found that VOCs rely on lineage-level entropy value of mutation numbers to compete with other variants, suggestive of the importance of population-level mutations in the virus evolution. Thus, we hypothesized that VOCs are a result of a mutational process across the globe. Results: Analyzing the mutations from January 2020 to December 2021, we simulated the mutational process by estimating the pace of evolution, and thus divided the time period, January 2020-March 2022, into eight stages. We predicted Alpha, Delta, Delta Plus (AY.4.2) and Omicron (B.1.1.529) by their mutational entropy values in the Stages I, III, V and VII with accelerated paces, respectively. In late November 2021, VOC-alarm alerted that Omicron strongly competed with Delta and Delta plus to become a highly transmissible variant. Using simulated data, VOC-alarm also predicted that Omicron could lead to another COVID surge from January 2022 to March 2022. AVAILABILITY AND IMPLEMENTATION: Our software implementation is available at https://github.com/guangxujin/VOC-alarm. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Indexed as

COVID-19SARS-CoV-2HumansMutationSoftware

Identifiers

PMID35640977
PMCPMC9272809

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

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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.