Performance of in silico tools for the evaluation of p16INK4a (CDKN2A) variants in CAGI.
Performance of in silico tools for the evaluation of p16INK4a (CDKN2A) variants in CAGI.
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DOI：
10.1002/humu.23235
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发表时间：
2017-09
期刊：
影响因子：
3.9
通讯作者：
Tosatto SCE
中科院分区：
文献类型：
--
作者：
Carraro M;Minervini G;Giollo M;Bromberg Y;Capriotti E;Casadio R;Dunbrack R;Elefanti L;Fariselli P;Ferrari C;Gough J;Katsonis P;Leonardi E;Lichtarge O;Menin C;Martelli PL;Niroula A;Pal LR;Repo S;Scaini MC;Vihinen M;Wei Q;Xu Q;Yang Y;Yin Y;Zaucha J;Zhao H;Zhou Y;Brenner SE;Moult J;Tosatto SCE
在下一代测序时代，随着遗传筛查的普及，对癌症相关基因未知意义的变异的正确表型解释是一项诊断挑战。基因组解读的关键评估（CAGI）实验旨在测试和定义基因型-表型解读的最新技术水平。在这里，我们提出了CAGI p16 INK 4a挑战的评估。参与者被要求预测p16 INK 4a肿瘤抑制因子（一种由CDKN 2A基因编码的细胞周期蛋白依赖性激酶抑制剂）的10种变体对细胞增殖的影响。22个致病性预测因素进行了评估，在医学背景下的可靠性的各种准确性措施。不同的评估措施结合在一个整体排名，以提供更强大的结果。用于评估的R脚本可从GitHub存储库中公开获取，以供将来在类似的评估练习中使用。尽管测试集大小有限，但我们的研究结果显示了各种结果，其中一些方法表现得更好。结合不同策略的方法往往优于简单的方法。最好的预测器Yang&Zhou实验室使用了一种机器学习方法，将测量蛋白质稳定性的经验能量函数与进化守恒项相结合。p16 INK 4a的挑战突出了微妙的结构效应如何中和其他有害的变异。
Correct phenotypic interpretation of variants of unknown significance for cancer-associated genes is a diagnostic challenge as genetic screenings gain in popularity in the next-generation sequencing era. The Critical Assessment of Genome Interpretation (CAGI) experiment aims to test and define the state of the art of genotype-phenotype interpretation. Here, we present the assessment of the CAGI p16INK4a challenge. Participants were asked to predict the effect on cellular proliferation of ten variants for the p16INK4a tumor suppressor, a cyclin-dependent kinase inhibitor encoded by the CDKN2A gene. Twenty-two pathogenicity predictors were assessed with a variety of accuracy measures for reliability in a medical context. Different assessment measures were combined in an overall ranking to provide more robust results. The R scripts used for assessment are publicly available from a GitHub repository for future use in similar assessment exercises. Despite a limited test-set size, our findings show a variety of results, with some methods performing significantly better. Methods combining different strategies frequently outperform simpler approaches. The best predictor, Yang&Zhou lab, uses a machine learning method combining an empirical energy function measuring protein stability with an evolutionary conservation term. The p16INK4a challenge highlights how subtle structural effects can neutralize otherwise deleterious variants.
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影响因子：
3.9
作者：
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通讯作者：
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