Evidence map›Paper›PMID 34909771›Full record

ArticlebioRxiv : the preprint server for biology2021

Insights on the mutational landscape of the SARS-CoV-2 Omicron variant.

Nathaniel L Miller, Thomas Clark, Rahul Raman, Ram Sasisekharan

Open access · greenAbstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in bioRxiv : the preprint server for biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed, 37 citations in OpenAlex.

  1. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

4 authors at 3 institutions in 2 countries.

Nathaniel L MillerHarvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
Thomas ClarkDepartment of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
Rahul RamanDepartment of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
Ram SasisekharanDepartment of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
Massachusetts Institute of Technology · USHarvard–MIT Division of Health Sciences and Technology · USSingapore-MIT Alliance for Research and Technology · SG

Funding

TRAINING GRANTS IN ENVIROMENTAL TOXICOLOGYT32ES007020 · NIEHS · MASSACHUSETTS INSTITUTE OF TECHNOLOGY · PI ESSIGMANN, JOHN M · 1985 to 2024
$13.1M
NIEHS NIH HHS T32 ES007020
6 · The paper itself

Abstract

The SARS-COV2 Omicron variant has sparked global concern due to the possibility of enhanced transmissibility and escape from vaccines and therapeutics. In this study, we describe the mutational landscape of the Omicron variant using amino acid interaction (AAI) networks. AAI network analysis is particularly well suited for interrogating the impact of constellations of mutations as occur on Omicron that may function in an epistatic manner. Our analyses suggest that as compared to previous variants of concern, the Omicron variant has increased antibody escape

Indexed as

Antibody EscapeAntigenic DriftEpistasisMutationNetwork AnalysisOmicronRBD Interface StabilitySARS-CoV-2Variant of Concern

Identifiers

PMID34909771
PMCPMC8669838
OpenAlexW4200490230

What OpenQuestion 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 OpenQuestion graph.