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Insights on variant analysis in silico tools for pathogenicity prediction.
Garcia, Felipe Antonio de Oliveira; de Andrade, Edilene Santos; Palmero, Edenir Inez.
Affiliation
  • Garcia FAO; Molecular Oncology Research Center-Barretos Cancer Hospital, Barretos, Brazil.
  • de Andrade ES; Molecular Oncology Research Center-Barretos Cancer Hospital, Barretos, Brazil.
  • Palmero EI; Molecular Oncology Research Center-Barretos Cancer Hospital, Barretos, Brazil.
Front Genet ; 13: 1010327, 2022.
Article in En | MEDLINE | ID: mdl-36568376
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.
Key words

Full text: 1 Collection: 01-internacional Database: MEDLINE Type of study: Prognostic_studies / Risk_factors_studies Language: En Journal: Front Genet Year: 2022 Document type: Article Affiliation country: Brazil Country of publication: Switzerland

Full text: 1 Collection: 01-internacional Database: MEDLINE Type of study: Prognostic_studies / Risk_factors_studies Language: En Journal: Front Genet Year: 2022 Document type: Article Affiliation country: Brazil Country of publication: Switzerland