Evaluation of in silico predictors on short nucleotide variants in
computational biology
genetics
genomics
globin genes
haemoglobinopathies
human
in silico prediction
systems biology
thalassaemia
variant classification
Journal
eLife
ISSN: 2050-084X
Titre abrégé: Elife
Pays: England
ID NLM: 101579614
Informations de publication
Date de publication:
01 12 2022
01 12 2022
Historique:
received:
23
04
2022
accepted:
31
10
2022
pubmed:
2
12
2022
medline:
15
12
2022
entrez:
1
12
2022
Statut:
epublish
Résumé
Haemoglobinopathies are the commonest monogenic diseases worldwide and are caused by variants in the globin gene clusters. With over 2400 variants detected to date, their interpretation using the American College of Medical Genetics and Genomics (ACMG)/Association for Molecular Pathology (AMP) guidelines is challenging and computational evidence can provide valuable input about their functional annotation. While many in silico predictors have already been developed, their performance varies for different genes and diseases. In this study, we evaluate 31 in silico predictors using a dataset of 1627 variants in
Identifiants
pubmed: 36453528
doi: 10.7554/eLife.79713
pii: 79713
pmc: PMC9731569
doi:
pii:
Substances chimiques
Nucleotides
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Informations de copyright
© 2022, Tamana, Xenophontos et al.
Déclaration de conflit d'intérêts
ST, MX, AM, CS, CH, CB, JT, IF, NY, FA, EE, HH, BZ, AK, MK, PK No competing interests declared
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