Associated Papers
ACMG/AMP interpretation of BRCA1 missense variants: Structure-informed scores add evidence strength granularity to the PP3/BP4 computational evidence.
Ramadane-Morchadi L et al.
American Journal of Human Genetics, 2025
40233743 (PubMed )
Supplement: mmc2.xlsx › Hoja1
Crucial roles of the BRCA1-BARD1 E3 ubiquitin ligase activity in homology-directed DNA repair.
Wang M et al.
Molecular Cell, 2023
DNA repair function scores for 2172 variants in the BRCA1 amino-terminus.
Diabate M et al.
Plos Genetics, 2023
37578980 (PubMed )
Supplement: pgen.1010739.s005.xlsx › Sheet1
Artificial intelligence-based recognition for variant pathogenicity of BRCA1 using AlphaFold2-predicted structures.
Li C et al.
Theranostics, 2023
36593954 (PubMed )
Supplement: thnov13p0391s1.pdf
Refinement of the assignment to the ACMG/AMP BS3 and PS3 criteria of eight BRCA1 variants of uncertain significance by integrating available functional data with protein interaction assays.
Caleca L et al.
Frontiers in Oncology, 2023
37168384 (PubMed )
Supplement: Table_1.xlsx › ST3_Discordant_Variants
Quantifying prediction of pathogenicity for within-codon concordance (PM5) using 7541 functional classifications of BRCA1 and MSH2 missense variants.
Loong L et al.
Genetics in Medicine : Official Journal of the American College of Medical Genetics, 2022
34906453 (PubMed )
Supplement: mmc1.xlsx › Sup_Table_3_Variantdata
Pathogenic genetic variants from highly connected cancer susceptibility genes confer the loss of structural stability.
Reza MN et al.
Scientific Reports, 2021
34584144 (PubMed )
Supplement: 41598_2021_98547_MOESM1_ESM.zip › Supplementary_files-5-8-21 › Supplementary_file_13.xlsx › Sheet1
Towards a New, Endophenotype-Based Strategy for Pathogenicity Prediction in BRCA1 and BRCA2: In Silico Modeling of the Outcome of HDR/SGE Assays for Missense Variants.
Özkan S et al.
International Journal of Molecular Sciences, 2021
34207612 (PubMed )
Supplement: ijms-22-06226-s001.zip › SUPP_FIGURES_TABLES › SupplementaryTable4.xlsx › supp_table4
Predicting the Disease Risk of Protein Mutation Sequences With Pre-training Model.
Li K et al.
Frontiers in Genetics, 2020
33408741 (PubMed )
Supplement: Table_2.XLSX › Sheet1
REVEL and BayesDel outperform other in silico meta-predictors for clinical variant classification.
Tian Y et al.
Scientific Reports, 2019
31484976 (PubMed )
Supplement: 41598_2019_49224_MOESM2_ESM.xlsx › Sheet1
Accurate classification of BRCA1 variants with saturation genome editing.
Findlay GM et al.
Nature, 2018
30209399 (PubMed )
Supplement: NIHMS1501643-supplement-2.xlsx › Sheet1
A Bayesian framework for efficient and accurate variant prediction.
Qian D et al.
Plos One, 2018
30212499 (PubMed )
Supplement: pone.0203553.s008.xlsx › Variants
Evaluation of unclassified variants in the breast cancer susceptibility genes BRCA1 and BRCA2 using five methods: results from a population-based study of young breast cancer patients.
Lee E et al.
Breast Cancer Research : Bcr, 2008
18284688 (PubMed )
Supplement: bcr1865-S3.doc
Genetic analysis of BRCA1 ubiquitin ligase activity and its relationship to breast cancer susceptibility.
Morris JR et al.
Human Molecular Genetics, 2006
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