Evidence map›Paper›PMID 38259032›Full record

ArticleGenetics and molecular biology2024

Broken silence: 22,841 predicted deleterious synonymous variants identified in the human exome through computational analysis.

Ana Carolina Mello, Delva Leao, Luis Dias, Felipe Colombelli, Mariana Recamonde-Mendoza, Andreia Carina Turchetto-Zolet, Ursula Matte

Open access · goldAbstract read
In one paragraph

Article in Genetics and molecular biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.6field-weighted citation impact, top 22% 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

2 citing papers in PubMed, 2 citations in OpenAlex.

  1. Article
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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 at 2 institutions in 2 countries.

Ana Carolina MelloHospital de Clínicas de Porto Alegre, Núcleo de Bioinformática, Porto Alegre, RS, Brazil.ORCID http://orcid.org/0000-0002-5028-7682
Delva LeaoUniversidade Federal do Rio Grande do Sul, Programa de Pós-Graduação em Ciências Biológicas: Bioquímica, Porto Alegre, RS, Brazil.
Luis DiasHospital de Clínicas de Porto Alegre, Núcleo de Bioinformática, Porto Alegre, RS, Brazil.
Felipe ColombelliHospital de Clínicas de Porto Alegre, Núcleo de Bioinformática, Porto Alegre, RS, Brazil.
Mariana Recamonde-MendozaHospital de Clínicas de Porto Alegre, Núcleo de Bioinformática, Porto Alegre, RS, Brazil.
Andreia Carina Turchetto-ZoletUniversidade Federal do Rio Grande do Sul, Programa de Pós-Graduação em Genética e Biologia Molecular, Porto Alegre, RS, Brazil.ORCID http://orcid.org/0000-0002-7365-1360
Ursula MatteHospital de Clínicas de Porto Alegre, Núcleo de Bioinformática, Porto Alegre, RS, Brazil.ORCID http://orcid.org/0000-0003-4977-6662
Universidade Federal do Rio Grande do Sul · BRWeatherford College · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Synonymous single nucleotide variants (sSNVs) do not alter the primary structure of a protein, thus it was previously accepted that they were neutral. Recently, several studies demonstrated their significance to a range of diseases. Still, variant prioritization strategies lack focus on sSNVs. Here, we identified 22,841 deleterious synonymous variants in 125,748 human exomes using two in silico predictors (SilVA and CADD). While 98.2% of synonymous variants are classified as neutral, 1.8% are predicted to be deleterious, yielding an average of 9.82 neutral and 0.18 deleterious sSNVs per exome. Further investigation of prediction features via Heterogeneous Ensemble Feature Selection revealed that impact on amino acid sequence and conservation carry the most weight for a deleterious prediction. Thirty nine detrimental sSNVs are not rare and are located on disease associated genes. Ten distinct putatively non-deleterious sSNVs are likely to be under positive selection in the North-Western European and East Asian populations. Taken together our analysis gives voice to the so-called silent mutations as we propose a robust framework for evaluating the deleteriousness of sSNVs in variant prioritization studies.

Identifiers

PMID38259032
PMCPMC10804382
OpenAlexW4391095933

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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.