Abstract：
A comprehensive analysis of germline and somatic variants requires complex computational approaches that combine next-generation sequencing (NGS)-based omics data with curated annotations from public repositories. Here, we describe Structure-PPi, which facilitates the analysis of cancer-related variants onto protein 3D structures, interaction interfaces, and other important functional sites (i.e., catalytic, ligand-binding, posttranslational modification). Our approach relies on features extracted from Interactome3D, UniProtKB, InterPro, APPRIS, dbNSFP, and COSMIC databases and provides complementary information to pathogenicity prediction methods. Thus, Structure-PPi helps in the discrimination of false-positive predictions and adds both mechanistic and biological insights into the role of variants in a given cancer. An online version of the tools is available at https://rbbt.bsc.es/StructurePPI/ .
摘要：
种系和体细胞变异的综合分析需要复杂的计算方法，将基于下一代测序（NGS）的组学数据与来自公共存储库的精选注释相结合。这里,我们描述了结构PPi，这有助于将癌症相关变异分析到蛋白质3D结构上，交互接口，和其他重要的功能站点(即，催化,配体结合，翻译后修饰)。我们的方法依赖于从Interactome3D中提取的特征，UniProtKB,InterPro,APPRIS,dbNSFP,和COSMIC数据库，并提供致病性预测方法的补充信息。因此，Structure-PPi有助于鉴别假阳性预测，并增加了对变异在给定癌症中的作用的机制和生物学见解。这些工具的在线版本可在https://rbbt获得。bsc.ES/结构PPI/。
