Quantitative Evaluation of Ion Chromatogram Extraction Algorithms

Abstract

Extracted ion chromatograms (XIC) are the fundamental signal unit in mass spectrometry. There are many algorithms for analyzing raw mass spectrometry data tasked with distinguishing real isotopic signals from noise. While one or more of the available algorithms are typically chained together for end-to-end mass spectrometry analysis, analysis of each algorithm in isolation provides a specific measurement of the strengths and weaknesses of each approach. Though qualitative opinions on extraction algorithm performance abound, quantitative performance has never been publicly ascertained. Quantitative evaluation has not occurred partly due to the lack of an available quantitative ground truth MS1 data set. Using a recently published, manually extracted XICs as ground truth data, we evaluate the quality of popular XIC algorithms, including MaxQuant, MZMine2, and several methods from XCMS. The manually curated data set comprises 48 human proteins stratified over 6 abundance orders of magnitude. Signals in the sample were manually curated into XIC using a commercial tool for visually identifying XIC and isotopic envelopes. XIC algorithms were applied to the manually extracted data using a grid search of possible parameters. Performance varied greatly between different parameter settings, though nearly all algorithms with parameter settings optimized with respect to the number of true positives recovered over 10 000 XICs.

Quantitative proteomics and applications in covalent ligand discovery

“…To establish the relative abundance of peptides with reasonable confidence, various statistical frameworks are available which account for spectral counts, protein length, fragment ion intensities, and the number of unique peptides for each protein. A second strategy for label-free quantification involves utilizing MS1-based quantification of precursor ion intensities (Megger et al, 2013;Smith and Tostengard, 2020). Integrating the intensity of the m/z peak associated with a specific peptide over its chromatographic retention time gives the extracted ion chromatogram (XIC), with the area under the curve representing the ion count for the peptide.…”

An XIC-Centric Strategy for Improved Identification and Quantification in Proteomic Data Analyses

“…A recent study evaluated the performance of several tools (including MaxQuant) in XIC extraction for a data set that was manually curated. 10 The median performer had a success rate of less than 50%, underscoring the challenges of "blind" (without employing identification information) feature detection with respect to both accuracy and coverage.…”

“…MaxQuant, a widely used proteomic data analysis tool, is another example that performs feature extraction and isotope clustering for precursor mass refinement before identification. A recent study evaluated the performance of several tools (including MaxQuant) in XIC extraction for a data set that was manually curated . The median performer had a success rate of less than 50%, underscoring the challenges of “blind” (without employing identification information) feature detection with respect to both accuracy and coverage.…”