Genetic Programming for Preprocessing Tandem Mass Spectra to Improve the Reliability of Peptide Identification

Abstract

Tandem mass spectrometry (MS/MS) is currently the most commonly used technology in proteomics for identifying proteins in complex biological samples. Mass spectrometers can produce a large number of MS/MS spectra each of which has hundreds of peaks. These peaks normally contain background noise, therefore a preprocessing step to filter the noise peaks can improve the accuracy and reliability of peptide identification. This paper proposes to preprocess the data by classifying peaks as noise peaks or signal peaks, i.e., a highly-imbalanced binary classification task, and uses genetic programming (GP) to address this task. The expectation is to increase the peptide identification reliability. Meanwhile, six different types of classification algorithms in addition to GP are used on various imbalance ratios and evaluated in terms of the average accuracy and recall. The GP method appears to be the best in the retention of more signal peaks as examined on a benchmark dataset containing 1, 674 MS/MS spectra. To further evaluate the effectiveness of the GP method, the preprocessed spectral data is submitted to a benchmark de novo sequencing software, PEAKS, to identify the peptides. The results show that the proposed method improves the reliability of peptide identification compared to the original un-preprocessed data and the intensity-based thresholding methods.