Artículo
CAGI, the Critical Assessment of Genome Interpretation, establishes progress and prospects for computational genetic variant interpretation methods
Jain, Shantanu; Bakolitsa, Constantina; Brenner, Steven E.; Radivojac, Predrag; Moult, John; Repo, Susanna; Hoskins, Roger A.; Andreoletti, Gaia; Barsky, Daniel; Chellapan, Ajithavalli; Chu, Hoyin; Dabbiru, Navya; Kollipara, Naveen K.; Ly, Melissa; Neumann, Andrew J.; Pal, Lipika R.; Odell, Eric; Pandey, Gaurav; Peters Petrulewicz, Robin C.; Srinivasan, Rajgopal; Yee, Stephen F.; Yeleswarapu, Sri Jyothsna; Zuhl, Maya; Adebali, Ogun; Fornasari, Maria Silvina
; Patra, Ayoti; O'Donnell Luria, Anne; Ng, Pauline C.; Shon, John; Veltman, Joris; Zook, Justin M.
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Fecha de publicación:
02/2024
Editorial:
BioMed Central
Revista:
Genome Biology
ISSN:
1474-760X
Idioma:
Inglés
Tipo de recurso:
Artículo publicado
Clasificación temática:
Resumen
Background The Critical Assessment of Genome Interpretation (CAGI) aims to advance the state-of-the-art for computational prediction of genetic variant impact, particularly where relevant to disease. The five complete editions of the CAGI community experiment comprised 50 challenges, in which participants made blind predictions of phenotypes from genetic data, and these were evaluated by independent assessors. Results Performance was particularly strong for clinical pathogenic variants, including some difficult-to-diagnose cases, and extends to interpretation of cancer-related variants. Missense variant interpretation methods were able to estimate biochemical effects with increasing accuracy. Assessment of methods for regulatory variants and complex trait disease risk was less definitive and indicates performance potentially suitable for auxiliary use in the clinic. Conclusions Results show that while current methods are imperfect, they have major utility for research and clinical applications. Emerging methods and increasingly large, robust datasets for training and assessment promise further progress ahead.
Palabras clave:
Variant impact
,
Disease
,
Computational Biology
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Articulos(SEDE CENTRAL)
Articulos de SEDE CENTRAL
Articulos de SEDE CENTRAL
Citación
Jain, Shantanu; Bakolitsa, Constantina; Brenner, Steven E.; Radivojac, Predrag; Moult, John; et al.; CAGI, the Critical Assessment of Genome Interpretation, establishes progress and prospects for computational genetic variant interpretation methods; BioMed Central; Genome Biology; 25; 1; 2-2024; 1-46
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