Suppr超能文献

应得的荣誉:一种信息含量评分,用于捕捉变异效应多重分析的临床价值。

Assigning credit where it is due: an information content score to capture the clinical value of multiplexed assays of variant effect.

机构信息

Ambry Genetics, Aliso Viejo, CA, USA.

Department of Genome Sciences, University of Washington, Seattle, WA, USA.

出版信息

BMC Bioinformatics. 2024 Sep 6;25(1):295. doi: 10.1186/s12859-024-05920-5.

Abstract

BACKGROUND

A variant can be pathogenic or benign with relation to a human disease. Current classification categories from benign to pathogenic reflect a probabilistic summary of the current understanding. A primary metric of clinical utility for multiplexed assays of variant effect (MAVE) is the number of variants that can be reclassified from uncertain significance (VUS). However, a gap in this measure of utility is that it underrepresents the information gained from MAVEs. The aim of this study was to develop an improved quantification metric for MAVE utility. We propose adopting an information content approach that includes data that does not reclassify variants will better reflect true information gain. We adopted an information content approach to evaluate the information gain, in bits, for MAVEs of BRCA1, PTEN, and TP53. Here, one bit represents the amount of information required to completely classify a single variant starting from no information.

RESULTS

BRCA1 MAVEs produced a total of 831.2 bits of information, 6.58% of the total missense information in BRCA1 and a 22-fold increase over the information that only contributed to VUS reclassification. PTEN MAVEs produced 2059.6 bits of information which represents 32.8% of the total missense information in PTEN and an 85-fold increase over the information that contributed to VUS reclassification. TP53 MAVEs produced 277.8 bits of information which represents 6.22% of the total missense information in TP53 and a 3.5-fold increase over the information that contributed to VUS reclassification.

CONCLUSIONS

An information content approach will more accurately portray information gained through MAVE mapping efforts than by counting the number of variants reclassified. This information content approach may also help define the impact of guideline changes that modify the information definitions used to classify groups of variants.

摘要

背景

变体与人类疾病有关,可以是致病性的,也可以是良性的。当前从良性到致病性的分类类别反映了当前理解的概率总结。用于变体效应多重分析(MAVE)的临床效用的主要度量标准是可以从不确定意义(VUS)重新分类的变体数量。然而,该效用度量标准的一个不足之处在于,它没有充分体现从 MAVE 中获得的信息。本研究旨在开发一种用于 MAVE 效用的改进量化度量标准。我们建议采用信息内容方法,该方法包括不会重新分类变体的数据,这将更好地反映真实的信息增益。我们采用信息内容方法来评估 BRCA1、PTEN 和 TP53 的 MAVE 的信息增益,以位为单位。在这里,一位代表从没有信息开始完全分类单个变体所需的信息量。

结果

BRCA1 的 MAVEs 总共产生了 831.2 位信息,占 BRCA1 错义信息总量的 6.58%,是仅有助于 VUS 重新分类的信息量的 22 倍。PTEN 的 MAVEs 产生了 2059.6 位信息,占 PTEN 错义信息总量的 32.8%,是有助于 VUS 重新分类的信息量的 85 倍。TP53 的 MAVEs 产生了 277.8 位信息,占 TP53 错义信息总量的 6.22%,是有助于 VUS 重新分类的信息量的 3.5 倍。

结论

与通过计算重新分类的变体数量相比,信息内容方法将更准确地描述通过 MAVE 映射工作获得的信息。这种信息内容方法还可以帮助定义改变用于对变体组进行分类的信息定义的指南变更的影响。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4ea9/11380199/08c68837fb0a/12859_2024_5920_Fig1_HTML.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验