Evidence map›Paper›PMID 37018331›Full record

ReviewPLoS pathogens2023

Developing an appropriate evolutionary baseline model for the study of SARS-CoV-2 patient samples.

John W Terbot, Parul Johri, Schuyler W Liphardt, Vivak Soni, Susanne P Pfeifer, Brandon S Cooper, Jeffrey M Good, Jeffrey D Jensen

Open access · goldAbstract readReview
In one paragraph

Review in PLoS pathogens, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.

0numbers the graph read from it
0cells of the map it votes in
22citing papers in PubMed
4.7field-weighted citation impact, top 4% 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

22 citing papers in PubMed, 24 citations in OpenAlex.

  1. Article
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  9. Inferring the demographic history of aye-ayes (bioRxiv : the preprint server for biology · 2024
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  19. Innate immunity decreases pathogen diversity during infection.Proceedings of the National Academy of Sciences of the United States of America · 2023
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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 at 2 institutions in 1 country.

John W TerbotUniversity of Montana, Division of Biological Sciences, Missoula, Montana, United States of America.
Parul JohriArizona State University, School of Life Sciences, Center for Evolution & Medicine, Tempe, Arizona, United States of America.
Schuyler W LiphardtUniversity of Montana, Division of Biological Sciences, Missoula, Montana, United States of America.
Vivak SoniArizona State University, School of Life Sciences, Center for Evolution & Medicine, Tempe, Arizona, United States of America.
Susanne P PfeiferArizona State University, School of Life Sciences, Center for Evolution & Medicine, Tempe, Arizona, United States of America.
Brandon S CooperUniversity of Montana, Division of Biological Sciences, Missoula, Montana, United States of America.ORCID 0000-0002-8269-7731
Jeffrey M GoodUniversity of Montana, Division of Biological Sciences, Missoula, Montana, United States of America.ORCID 0000-0003-0707-5374
Jeffrey D JensenArizona State University, School of Life Sciences, Center for Evolution & Medicine, Tempe, Arizona, United States of America.ORCID 0000-0002-4786-8064
Arizona State University · USUniversity of Montana · US

Funding

Surveillance genome sequencing to detect SARS-CoV-2 virus variants in MontanaP20GM103546 · NIGMS · UNIVERSITY OF MONTANA · PI BOWLER, BRUCE E · 2012 to 2021
$19.2M
Surveillance genome sequencing to detect SARS-CoV-2 virus variants in MontanaP30GM140963 · NIGMS · UNIVERSITY OF MONTANA · PI BOWLER, BRUCE E · 2021 to 2025
$6.9M
The evolutionary genetics and genomics of Wolbachia effects on host physiologyR35GM124701 · NIGMS · UNIVERSITY OF MONTANA · PI Brandon S. Cooper · 2017 to 2026
$3.9M
On differentiating selective and neutral evolutionary processesR35GM139383 · NIGMS · ARIZONA STATE UNIVERSITY-TEMPE CAMPUS · PI JENSEN, JEFFREY D · 2021 to 2025
$3.0M
Determination of Markers of High Pathogenicity in InfluenzaR01GM102546 · NIGMS · UNIVERSITY OF GEORGIA · PI DLUHY, RICHARD A. · 2012 to 2015
$1.1M
NIGMS NIH HHS P20 GM103546NIGMS NIH HHS P30 GM140963NIGMS NIH HHS R01 GM102546NIGMS NIH HHS R35 GM124701NIGMS NIH HHS R35 GM139383
6 · The paper itself

Abstract

Over the past 3 years, Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has spread through human populations in several waves, resulting in a global health crisis. In response, genomic surveillance efforts have proliferated in the hopes of tracking and anticipating the evolution of this virus, resulting in millions of patient isolates now being available in public databases. Yet, while there is a tremendous focus on identifying newly emerging adaptive viral variants, this quantification is far from trivial. Specifically, multiple co-occurring and interacting evolutionary processes are constantly in operation and must be jointly considered and modeled in order to perform accurate inference. We here outline critical individual components of such an evolutionary baseline model-mutation rates, recombination rates, the distribution of fitness effects, infection dynamics, and compartmentalization-and describe the current state of knowledge pertaining to the related parameters of each in SARS-CoV-2. We close with a series of recommendations for future clinical sampling, model construction, and statistical analysis.

Indexed as

COVID-19SARS-CoV-2GenomicsHumans

Identifiers

PMID37018331
PMCPMC10075409
OpenAlexW4362602554

What OpenQuestion holds

Textmetadata
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.