• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Human mutation >Improved, ACMG‐compliant, in silico prediction of pathogenicity for missense substitutions encoded by TP53 TP53 variants
【24h】

Improved, ACMG‐compliant, in silico prediction of pathogenicity for missense substitutions encoded by TP53 TP53 variants

机译：改进，符合ACMG标准，在致命致密的致命替代物的致病性致命的致命替代品中的致命替代品

获取原文
获取原文并翻译 | 示例
       
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Abstract Clinical interpretation of germline missense variants represents a major challenge, including those in the TP53 Li–Fraumeni syndrome gene. Bioinformatic prediction is a key part of variant classification strategies. We aimed to optimize the performance of the Align‐GVGD tool used for p53 missense variant prediction, and compare its performance to other bioinformatic tools (SIFT, PolyPhen‐2) and ensemble methods (REVEL, BayesDel). Reference sets of assumed pathogenic and assumed benign variants were defined using functional and/or clinical data. Area under the curve and Matthews correlation coefficient (MCC) values were used as objective functions to select an optimized protein multisequence alignment with best performance for Align‐GVGD. MCC comparison of tools using binary categories showed optimized Align‐GVGD (C15 cut‐off) combined with BayesDel (0.16 cut‐off), or with REVEL (0.5 cut‐off), to have the best overall performance. Further, a semi‐quantitative approach using multiple tiers of bioinformatic prediction, validated using an independent set of nonfunctional and functional variants, supported use of Align‐GVGD and BayesDel prediction for different strength of evidence levels in ACMG/AMP rules. We provide rationale for bioinformatic tool selection for TP53 variant classification, and have also computed relevant bioinformatic predictions for every possible p53 missense variant to facilitate their use by the scientific and medical community.
机译：摘要种系畸形变种的临床解释是一项重大挑战，包括TP53 Li-Fraumeni综合征基因的主要挑战。生物信息化预测是变体分类策略的关键部分。我们的旨在优化用于P53密误型变形预测的对齐-GVGD工具的性能，并将其性能与其他生物信息工具（SIFT，Polyphen-2）和集合方法（Revel，Bayesdel）进行比较。使用功能和/或临床数据定义假定致病和假定良性变体的参考组。曲线下的区域和马修斯相关系数（MCC）值用作客观函数，以选择优化的蛋白质多验性对齐，与对准-GVD的最佳性能。 MCC使用二进制类别的工具比较显示优化的对齐 - GVGD（C15截止）与贝塞尔德（0.16截止）或陶醉器（0.5截止）相结合，以获得最佳整体性能。此外，使用多层的生物信息预测的半定量方法，使用独立的非功能和功能变体进行验证，支持使用对准-GVGD和贝叶斯模拟的对准ACMG / AMP规则中的不同证据水平强度。我们为TP53变体分类提供了生物信息刀具选择的基本原理，并且还针对各种可能的P53密义变化计算了相关的生物信息预测，以便于科学和医学界的使用。

著录项

  • 来源
    《Human mutation》 |2018年第8期|共9页
  • 作者

    Fortuno Cristina; James Paul A.; Young Erin L.; Feng Bing; Olivier Magali; Pesaran Tina; Tavtigian Sean V.; Spurdle Amanda B.;

  • 作者单位

    Genetics and Computational DivisionQIMR Berghofer Medical Research InstituteHerston Queensland;

    Parkville Familial Cancer Centre ParkvilleMelbourne Victoria Australia;

    Department of Oncological Sciences Huntsman Cancer InstituteUniversity of Utah School of;

    Department of Dermatology and Huntsman Cancer InstituteUniversity of Utah School of MedicineSalt;

    Molecular Mechanisms and Biomarkers GroupInternational Agency for Research on CancerLyon France;

    Ambry GeneticsAliso Viejo California;

    Department of Oncological Sciences Huntsman Cancer InstituteUniversity of Utah School of;

    Genetics and Computational DivisionQIMR Berghofer Medical Research InstituteHerston Queensland;

  • 收录信息
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 医学遗传学;
  • 关键词

    ACMG; bioinformatics; classification; TP53; variant;

    机译：ACMG;生物信息学;分类;TP53;变体;

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号