Varaico: Literature-Extracted Genetic Variants

Gene:BRCA1
Variant:p.E1694K
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BRCA1

NM_007294.4:p.E1694K

Associated Papers

Precision in prediction: tailoring machine learning models for breast cancer missense variants pathogenicity prediction.

Ahmad RM et al.

Briefings in Bioinformatics, 2025

41263939 (PubMed )
Supplement: supplementary_materials_bbaf611_1_.zip › Supplementary_materials_bbaf611 › Supplementary_Table_S8.xlsx › Sheet1

Profiling BRCA1-BRCT interactions and their functional relevance at amino acid resolution.

Mangkusaputra V et al.

Nucleic Acids Research, 2025

40966517 (PubMed )
Supplement: gkaf848_supplemental_files.zip › Sup_table_2___ABX_CtIP_complete_count_and_scores.xlsx › BRCT1_ABX

Scalable approaches for generating, validating and incorporating data from high-throughput functional assays to improve clinical variant classification.

Padigepati SR et al.

Human Genetics, 2024

39085601 (PubMed )
Supplement: 439_2024_2691_MOESM2_ESM.xlsx › BRCA1

Protein-folding chaperones predict structure-function relationships and cancer risk in BRCA1 mutation carriers.

Gracia B et al.

Cell Reports, 2024

38368609 (PubMed )
Supplement: NIHMS1970903-supplement-3.xlsx › Table_S2

APPLICATION OF THE ACMG/AMP FRAMEWORK TO CAPTURE EVIDENCE RELEVANT TO PREDICTED AND OBSERVED IMPACT ON SPLICING: RECOMMENDATIONS FROM THE CLINGEN SVI SPLICING SUBGROUP.

Walker LC et al.

Medrxiv : the Preprint Server for Health Sciences, 2023

36865205 (PubMed )
Supplement: media-10.xlsx › Table_S2

Variomes: a high recall search engine to support the curation of genomic variants.

Pasche E et al.

Bioinformatics (oxford, England), 2022

35274687 (PubMed )
Supplement: btac146_supplementary_data.pdf

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

Role of Splicing Regulatory Elements and In Silico Tools Usage in the Identification of Deep Intronic Splicing Variants in Hereditary Breast/Ovarian Cancer Genes.

Moles-Fernández A et al.

Cancers, 2021

34283047 (PubMed )
Supplement: cancers-13-03341-s001.zip › 2_Supplementary_table_S2A_2B.xlsx › Suppl_Table_2A

Variant effect on splicing regulatory elements, branchpoint usage, and pseudoexonization: Strategies to enhance bioinformatic prediction using hereditary cancer genes as exemplars.

Canson D et al.

Human Mutation, 2020

Systematic misclassification of missense variants in BRCA1 and BRCA2 "coldspots".

Dines JN et al.

Genetics in Medicine : Official Journal of the American College of Medical Genetics, 2020

31911673 (PubMed )
Supplement: 41436_2019_740_MOESM2_ESM.xlsx › 1._Used_in_analysis

MutPred Splice: machine learning-based prediction of exonic variants that disrupt splicing.

Mort M et al.

Genome Biology, 2014

24451234 (PubMed )
Supplement: gb-2014-15-1-r19-S1.pdf

"Similarity trap" in protein-protein interactions could be carcinogenic: simulations of p53 core domain complexed with 53BP1 and BRCA1 BRCT domains.

Liu J et al.

Structure (london, England : 1993), 2006

An increased specificity score matrix for the prediction of SF2/ASF-specific exonic splicing enhancers.

Smith PJ et al.

Human Molecular Genetics, 2006

Listening to silence and understanding nonsense: exonic mutations that affect splicing.

Cartegni L et al.

Nature Reviews. Genetics, 2002

Structure of the 53BP1 BRCT region bound to p53 and its comparison to the Brca1 BRCT structure.

Joo WS et al.

Genes & Development, 2002

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Regarding the variants extracted from papers and shown text snippets from research papers, the following holds: "Some rights reserved. This work permits non-commercial use, distribution and reproduction in any medium, provided the original author and source are credited."