Liquid chromatography with tandem mass spectrometry (MS/MS) has been widely used in proteomics. Although a typical experiment includes both MS and MS/MS scans, existing bioinformatics research has focused far more on MS/MS data than on MS data. In MS data, each peptide produces a few trails of signal peaks, which are collectively called a peptide feature. Here, we introduce MSTracer, a new software tool for detecting peptide features from MS data. The software incorporates two scoring functions based on machine learning: one for detecting the peptide features and the other for assigning a quality score to each detected feature. The software was compared with several existing tools and demonstrated significantly better performance.
Keywords: LC−MS; machine learning; peptide feature detection.