Percolator is an important tool for greatly improving the results of a database search and subsequent downstream analysis. Using support vector machines (SVMs), Percolator recalibrates peptide–spectrum matches based on the learned decision boundary between targets and decoys. To improve analysis time for large-scale data sets, we update Percolator’s SVM learning engine through software and algorithmic optimizations rather than heuristic approaches that necessitate the careful study of their impact on learned parameters across different search settings and data sets. We show that by optimizing Percolator’s original learning algorithm, l₂-SVM-MFN, large-scale SVM learning requires nearly only a third of the original runtime. Furthermore, we show that by employing the widely used Trust Region Newton (TRON) algorithm instead of l₂-SVM-MFN, large-scale Percolator SVM learning is reduced to nearly only a fifth of the original runtime. Importantly, these speedups only affect the speed at which Percolator converges to a global solution and do not alter recalibration performance. The upgraded versions of both l₂-SVM-MFN and TRON are optimized within the Percolator codebase for multithreaded and single-thread use and are available under Apache license at bitbucket.org/jthalloran/percolator_upgrade.

中文翻译：

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时间问题：通过高效的 SVM 学习进行大规模蛋白质组学更快的渗滤器分析

Percolator 是一个重要的工具，可以极大地改善数据库搜索和后续下游分析的结果。使用支持向量机 (SVM)，Percolator 根据学习到的目标和诱饵之间的决策边界重新校准肽谱匹配。为了缩短大规模数据集的分析时间，我们通过软件和算法优化而不是启发式方法来更新 Percolator 的 SVM 学习引擎，启发式方法需要仔细研究它们对不同搜索设置和数据集的学习参数的影响。我们表明，通过优化 Percolator 的原始学习算法l ₂ -SVM-MFN，大规模 SVM 学习只需要原始运行时间的近三分之一。此外，我们还表明，通过采用广泛使用的信赖域牛顿 (TRON) 算法代替l ₂ -SVM-MFN，大规模 Percolator SVM 学习的运行时间减少到了原始运行时间的近五分之一。重要的是，这些加速仅影响 Percolator 收敛到全局解决方案的速度，不会改变重新校准性能。l ₂ -SVM-MFN 和 TRON的升级版本在 Percolator 代码库中针对多线程和单线程使用进行了优化，并且可根据 Apache 许可证在 bitbucket.org/jthalloran/percolator_upgrade 上获取。