Associated Papers
BRCA1-specific machine learning model predicts variant pathogenicity with high accuracy.
Khandakji M et al.
Physiological Genomics, 2023
Benchmarking of SpCas9 variants enables deeper base editor screens of BRCA1 and BCL2.
Sangree AK et al.
Nature Communications, 2022
35288574 (PubMed )
Supplement: 41467_2022_28884_MOESM9_ESM.xlsx › WT-CBE_library_annotation
Identification of pathogenic variants in cancer genes using base editing screens with editing efficiency correction.
Huang C et al.
Genome Biology, 2021
33691754 (PubMed )
Supplement: 13059_2021_2305_MOESM7_ESM.xlsx › Table_S6
Classification of variants of uncertain significance in BRCA1 and BRCA2 using personal and family history of cancer from individuals in a large hereditary cancer multigene panel testing cohort.
Li H et al.
Genetics in Medicine : Official Journal of the American College of Medical Genetics, 2020
31853058 (PubMed )
Supplement: 41436_2019_729_MOESM2_ESM.xls › Sheet1
Benchmarking mutation effect prediction algorithms using functionally validated cancer-related missense mutations.
Martelotto LG et al.
Genome Biology, 2014
25348012 (PubMed )
Supplement: 13059_2014_484_MOESM2_ESM.xlsx › Additional_file_2
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