A Matter of Time: Faster Percolator Analysis via Efficient SVM Learning for Large-Scale Proteomics.

ABSTRACT: 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 T

SUBMITTER: Halloran JT 

PROVIDER: S-EPMC6420878 | biostudies-literature | 2018 May

REPOSITORIES: biostudies-literature