Evidence map›Paper›PMID 35479940›Full record

ArticleFrontiers in medicine2022

Advanced Functions Embedded in the Second Version of Database, Global Evaluation of SARS-CoV-2/hCoV-19 Sequences 2.

Kailing Li, Audrey K Y Wang, Sheng Liu, Shuyi Fang, Alex Z Lu, Jikui Shen, Lei Yang, Chang-Deng Hu, Kai Yang, Jun Wan

Open access · goldAbstract read
In one paragraph

Article in Frontiers in medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact, top 96% 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

0 citing papers in PubMed, 0 citations in OpenAlex.

No citing paper in PubMed yet.

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 6 institutions in 1 country.

Kailing LiDepartment of BioHealth Informatics, Indiana University School of Informatics and Computing, Indiana University-Purdue University Indianapolis, Indianapolis, IN, United States.
Audrey K Y WangPark Tudor School, Indianapolis, IN, United States.
Sheng LiuDepartment of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, United States.
Shuyi FangDepartment of BioHealth Informatics, Indiana University School of Informatics and Computing, Indiana University-Purdue University Indianapolis, Indianapolis, IN, United States.
Alex Z LuPark Tudor School, Indianapolis, IN, United States.
Jikui ShenThe Wilmer Eye Institute, Johns Hopkins University School of Medicine, Baltimore, MD, United States.
Lei YangHerman B Wells Center for Pediatric Research, Department of Pediatrics, Indiana University School of Medicine, Indianapolis, IN, United States.
Chang-Deng HuDepartment of Medicinal Chemistry and Molecular Pharmacology, Purdue University, West Lafayette, IN, United States.
Kai YangHerman B Wells Center for Pediatric Research, Department of Pediatrics, Indiana University School of Medicine, Indianapolis, IN, United States.
Jun WanDepartment of BioHealth Informatics, Indiana University School of Informatics and Computing, Indiana University-Purdue University Indianapolis, Indianapolis, IN, United States.
Indiana University School of MedicineIndiana University – Purdue University Indianapolis · USTudor Place · USIndiana University · USJohns Hopkins Medicine · USPurdue University West Lafayette · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The Global Evaluation of SARS-CoV-2/hCoV-19 Sequences 2 (GESS v2 https://shiny.ph.iu.edu/GESS_v2/) is an updated version of GESS, which has offered a handy query platform to analyze single-nucleotide variants (SNVs) on millions of high coverages and high-quality severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) complete genomes provided by the Global Initiative on Sharing Avian Influenza Data (GISAID). Including the tools in the first version, the GESS v2 is embedded with new functions, which allow users to search SNVs, given the viral nucleotide or amino acid sequence. The GESS v2 helps users to identify SNVs or SARS-CoV-2 lineages enriched in countries of user's interest and show the migration path of a selected lineage on a world map during specific time periods chosen by the users. In addition, the GESS v2 can recognize the dynamic variations of newly emerging SNVs in each month to help users monitor SNVs, which will potentially become dominant soon. More importantly, multiple sets of analyzed results about SNVs can be downloaded directly from the GESS v2 by which users can conduct their own independent research. With these significant updates, the GESS v2 will continue to serve as a public open platform for researchers to explore SARS-CoV-2 evolutionary patterns from the perspectives of the prevalence and impact of SNVs.

Indexed as

COVID-19databaseenrichmentlineagenewly emergingSARS-CoV-2SNV

Identifiers

PMID35479940
PMCPMC9037956
OpenAlexW4223440969

What OpenQuestion holds

Textmetadata
LicenceCC BY
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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.