Evidence map›Paper›PMID 34202172›Full record

ArticleGenes2021

Uncovering Signals from the Coronavirus Genome.

Enrique Canessa

Abstract read
In one paragraph

Article in Genes, 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
–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.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Review
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

1 author.

Enrique CanessaThe Abdus Salam International Centre for Theoretical Physics (ICTP), Science Dissemination Unit (SDU), 34151 Trieste, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

A signal analysis of the complete genome sequenced for coronavirus variants of concern-B.1.1.7 (Alpha), B.1.135 (Beta) and P1 (Gamma)-and coronavirus variants of interest-B.1.429-B.1.427 (Epsilon) and B.1.525 (Eta)-is presented using open GISAID data. We deal with a certain new type of finite alternating sum series having independently distributed terms associated with binary (0,1) indicators for the nucleotide bases. Our method provides additional information to conventional similarity comparisons via alignment methods and Fourier Power Spectrum approaches. It leads to uncover distinctive patterns regarding the intrinsic data organization of complete genomics sequences according to its progression along the nucleotide bases position. The present new method could be useful for the bioinformatics surveillance and dynamics of coronavirus genome variants.

Indexed as

Genome, ViralComputational BiologyCoronavirusHumansSARS-CoV-2Viral ProteinsViral Proteinsalternating seriescomparative genomics variantsSARS-CoV-2sequence analysis

Identifiers

PMID34202172
PMCPMC8303286

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.