Electrical Engineering and Computer Science Department, University of California, Berkeley, California 94720, USA.
Genome Res. 2011 Nov;21(11):1969-80. doi: 10.1101/gr.104687.109. Epub 2011 Jul 22.
The Statistical Inference of Function Through Evolutionary Relationships (SIFTER) framework uses a statistical graphical model that applies phylogenetic principles to automate precise protein function prediction. Here we present a revised approach (SIFTER version 2.0) that enables annotations on a genomic scale. SIFTER 2.0 produces equivalently precise predictions compared to the earlier version on a carefully studied family and on a collection of 100 protein families. We have added an approximation method to SIFTER 2.0 and show a 500-fold improvement in speed with minimal impact on prediction results in the functionally diverse sulfotransferase protein family. On the Nudix protein family, previously inaccessible to the SIFTER framework because of the 66 possible molecular functions, SIFTER achieved 47.4% accuracy on experimental data (where BLAST achieved 34.0%). Finally, we used SIFTER to annotate all of the Schizosaccharomyces pombe proteins with experimental functional characterizations, based on annotations from proteins in 46 fungal genomes. SIFTER precisely predicted molecular function for 45.5% of the characterized proteins in this genome, as compared with four current function prediction methods that precisely predicted function for 62.6%, 30.6%, 6.0%, and 5.7% of these proteins. We use both precision-recall curves and ROC analyses to compare these genome-scale predictions across the different methods and to assess performance on different types of applications. SIFTER 2.0 is capable of predicting protein molecular function for large and functionally diverse protein families using an approximate statistical model, enabling phylogenetics-based protein function prediction for genome-wide analyses. The code for SIFTER and protein family data are available at http://sifter.berkeley.edu.
通过进化关系进行功能统计推断（SIFTER）框架使用统计图形模型，该模型应用系统发生原理实现精确的蛋白质功能预测。在这里，我们提出了一种经过修订的方法（SIFTER 版本 2.0），该方法可以实现基因组规模的注释。与早期版本相比，SIFTER 2.0 在经过精心研究的家族和 100 个蛋白质家族的集合中产生了同样精确的预测。我们在 SIFTER 2.0 中添加了一种近似方法，并在功能多样化的磺基转移酶蛋白质家族中展示了速度提高了 500 倍，而对预测结果的影响最小。对于 Nudix 蛋白质家族，由于有 66 种可能的分子功能，以前无法使用 SIFTER 框架进行分析，SIFTER 在实验数据上的准确率达到了 47.4%（BLAST 的准确率为 34.0%）。最后，我们使用 SIFTER 根据 46 个真菌基因组中的蛋白质注释，对酿酒酵母的所有蛋白质进行了实验功能特征注释。在这个基因组中，SIFTER 精确预测了 45.5%的有特征蛋白质的分子功能，而目前的四种功能预测方法分别精确预测了 62.6%、30.6%、6.0%和 5.7%的蛋白质的功能。我们使用精度-召回曲线和 ROC 分析比较了不同方法的基因组规模预测，并评估了不同类型应用程序的性能。SIFTER 2.0 能够使用近似统计模型预测大型和功能多样化的蛋白质家族的蛋白质分子功能，从而为全基因组分析提供基于系统发生的蛋白质功能预测。SIFTER 和蛋白质家族数据的代码可在 http://sifter.berkeley.edu 获得。
