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Review
. 2022 Nov 29:13:1010327.
doi: 10.3389/fgene.2022.1010327. eCollection 2022.

Insights on variant analysis in silico tools for pathogenicity prediction

Felipe Antonio de Oliveira Garcia  1 , Edilene Santos de Andrade  1 , Edenir Inez Palmero  1   2
Affiliations
Review

Insights on variant analysis in silico tools for pathogenicity prediction

Felipe Antonio de Oliveira Garcia et al. Front Genet. .

Abstract

Molecular biology is currently a fast-advancing science. Sequencing techniques are getting cheaper, but the interpretation of genetic variants requires expertise and computational power, therefore is still a challenge. Next-generation sequencing releases thousands of variants and to classify them, researchers propose protocols with several parameters. Here we present a review of several in silico pathogenicity prediction tools involved in the variant prioritization/classification process used by some international protocols for variant analysis and studies evaluating their efficiency.

Keywords: bioinformatics; in silico; next generating sequencing; pathogenicity prediction; variant classification.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

FIGURE 1
FIGURE 1
Evidences proposed by the international consortiums: (A) Summarized evidence of the criteria proposed by them. (B) Flowchart for filtering variants; in silico tools scores may vary, here we present the one proposed by the authors (Ioannidis et al., 2016), although ClinGen suggests higher than 0.800 for oncogenicity (Horak et al., 2022).
FIGURE 2
FIGURE 2
Timeline of the described in silico tools methods and the criteria implemented suggesting their use.
FIGURE 3
FIGURE 3
Graphic illustrating new citation number per year (according to Google Scholar) of the top three most cited tools (PolyPhen-2, SIFT, and CADD) versus the four tools that had frequent outperforming analysis (VEST3, REVEL, FATHMM, and BayesDel).
See this image and copyright information in PMC

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