Assessing predictions on fitness effects of missense variants in HMBS in CAGI6.
Journal
Human genetics
ISSN: 1432-1203
Titre abrégé: Hum Genet
Pays: Germany
ID NLM: 7613873
Informations de publication
Date de publication:
07 Aug 2024
07 Aug 2024
Historique:
received:
18
11
2023
accepted:
17
05
2024
medline:
7
8
2024
pubmed:
7
8
2024
entrez:
7
8
2024
Statut:
aheadofprint
Résumé
This paper presents an evaluation of predictions submitted for the "HMBS" challenge, a component of the sixth round of the Critical Assessment of Genome Interpretation held in 2021. The challenge required participants to predict the effects of missense variants of the human HMBS gene on yeast growth. The HMBS enzyme, critical for the biosynthesis of heme in eukaryotic cells, is highly conserved among eukaryotes. Despite the application of a variety of algorithms and methods, the performance of predictors was relatively similar, with Kendall's tau correlation coefficients between predictions and experimental scores around 0.3 for a majority of submissions. Notably, the median correlation (≥ 0.34) observed among these predictors, especially the top predictions from different groups, was greater than the correlation observed between their predictions and the actual experimental results. Most predictors were moderately successful in distinguishing between deleterious and benign variants, as evidenced by an area under the receiver operating characteristic (ROC) curve (AUC) of approximately 0.7 respectively. Compared with the recent two rounds of CAGI competitions, we noticed more predictors outperformed the baseline predictor, which is solely based on the amino acid frequencies. Nevertheless, the overall accuracy of predictions is still far short of positive control, which is derived from experimental scores, indicating the necessity for considerable improvements in the field. The most inaccurately predicted variants in this round were associated with the insertion loop, which is absent in many orthologs, suggesting the predictors still heavily rely on the information from multiple sequence alignment.
Identifiants
pubmed: 39110250
doi: 10.1007/s00439-024-02680-3
pii: 10.1007/s00439-024-02680-3
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Subventions
Organisme : Welch Foundation
ID : I-2095-20220331
Organisme : Welch Foundation
ID : I-2095-20220331
Organisme : Welch Foundation
ID : I-1505
Organisme : Cancer Prevention and Research Institute of Texas
ID : RP210041
Organisme : NIH HHS
ID : R35-GM134922
Pays : United States
Organisme : NIH HHS
ID : R35-GM134922
Pays : United States
Organisme : NIH HHS
ID : R35GM124952
Pays : United States
Organisme : NIH HHS
ID : R35GM124952
Pays : United States
Organisme : NIH HHS
ID : HG012022
Pays : United States
Organisme : NIH HHS
ID : U24 HG007346
Pays : United States
Organisme : NIH HHS
ID : GM127390
Pays : United States
Organisme : Ministero dell'Istruzione e del Merito
ID : MIUR-PRIN-201744NR8S
Organisme : Ministero dell'Istruzione e del Merito
ID : MIUR-PRIN-201744NR8S
Organisme : National Science Foundation
ID : 2224128
Informations de copyright
© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.
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