Better prediction of functional effects for sequence variants.
Better prediction of functional effects for sequence variants.
复制标题
更好地预测序列变体功能效应。
DOI：
10.1186/1471-2164-16-s8-s1
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发表时间：
2015
期刊：
影响因子：
4.4
通讯作者：
Rost B
中科院分区：
文献类型：
--
作者：
Hecht M;Bromberg Y;Rost B
阐明自然发生的遗传变异的影响是个性化健康和个性化医疗的主要挑战之一。在这里，我们介绍SNAP 2，一种新的基于神经网络的分类器，它在区分效果和中性变体方面比最先进的方法有所改进。我们的方法的改进性能来自于筛选许多潜在相关的蛋白质特征和改进我们的开发数据集。在超过100 k个实验注释的变体上进行交叉验证，SNAP 2显著优于其他方法，达到83%的双状态准确度（效应/中性）。SNAP 2也优于其他方法的组合。人类变体的性能增加，但对其他生物体来说更是如此。我们的方法经过仔细校准的可靠性指数为实验后续的变量选择提供了信息，所有效应变量中预测最强的一半预测准确率超过96%。正如预期的那样，来自自动生成的多序列比对的进化信息给出了最强的预测信号。然而，我们还优化了我们的新方法，即使没有比对也能表现得令人惊讶。该功能将预测运行时间减少了两个数量级以上，实现了跨基因组比较，并使我们的新方法成为10-20%序列孤儿的最佳解决方案。SNAP 2可在以下网址获得：https://rostlab.org/services/snap2web Delta，计算天然氨基酸的差异特征评分和变体氨基酸的特征评分得到的输入特征; nsSNP，非同义SNP; PMD，蛋白质突变体数据库; SNAP，筛选不可接受的多态性; SNP，单核苷酸多态性;变体，任何氨基酸改变序列变体。
Elucidating the effects of naturally occurring genetic variation is one of the major challenges for personalized health and personalized medicine. Here, we introduce SNAP2, a novel neural network based classifier that improves over the state-of-the-art in distinguishing between effect and neutral variants. Our method's improved performance results from screening many potentially relevant protein features and from refining our development data sets. Cross-validated on >100k experimentally annotated variants, SNAP2 significantly outperformed other methods, attaining a two-state accuracy (effect/neutral) of 83%. SNAP2 also outperformed combinations of other methods. Performance increased for human variants but much more so for other organisms. Our method's carefully calibrated reliability index informs selection of variants for experimental follow up, with the most strongly predicted half of all effect variants predicted at over 96% accuracy. As expected, the evolutionary information from automatically generated multiple sequence alignments gave the strongest signal for the prediction. However, we also optimized our new method to perform surprisingly well even without alignments. This feature reduces prediction runtime by over two orders of magnitude, enables cross-genome comparisons, and renders our new method as the best solution for the 10-20% of sequence orphans. SNAP2 is available at: https://rostlab.org/services/snap2web Delta, input feature that results from computing the difference feature scores for native amino acid and feature scores for variant amino acid; nsSNP, non-synoymous SNP; PMD, Protein Mutant Database; SNAP, Screening for non-acceptable polymorphisms; SNP, single nucleotide polymorphism; variant, any amino acid changing sequence variant.