IQMMA: Efficient MS1 Intensity Extraction Pipeline Using Multiple Feature Detection Algorithms for DDA Proteomics

• Published in: Journal of proteome research Vol. 22; no. 9; pp. 2827 - 2835
• Main Authors: Postoenko, Valeriy I., Garibova, Leyla A., Levitsky, Lev I., Bubis, Julia A., Gorshkov, Mikhail V., Ivanov, Mark V.
• Format: Journal Article
• Language: English
• Published: United States American Chemical Society 01.09.2023
• Subjects: algorithms; Escherichia coli; glioblastoma; humans; peptides; proteome; proteomics; yeasts; feature detection; mass spectrometry; protein quantitation; bioinformatics
• ISSN: 1535-3893; 1535-3907; 1535-3907
• DOI: 10.1021/acs.jproteome.3c00075

One of the key steps in data dependent acquisition (DDA) proteomics is detection of peptide isotopic clusters, also called “features”, in MS1 spectra and matching them to MS/MS-based peptide identifications. A number of peptide feature detection tools became available in recent years, each relying on its own matching algorithm. Here, we provide an integrated solution, the intensity-based Quantitative Mix and Match Approach (IQMMA), which integrates a number of untargeted peptide feature detection algorithms and returns the most probable intensity values for the MS/MS-based identifications. IQMMA was tested using available proteomic data acquired for both well-characterized (ground truth) and real-world biological samples, including a mix of Yeast and E. coli digests spiked at different concentrations into the Human K562 digest used as a background, and a set of glioblastoma cell lines. Three open-source feature detection algorithms were integrated: Dinosaur, biosaur2, and OpenMS FeatureFinder. None of them was found optimal when applied individually to all the data sets employed in this work; however, their combined use in IQMMA improved efficiency of subsequent protein quantitation. The software implementing IQMMA is freely available at https://github.com/PostoenkoVI/IQMMA under Apache 2.0 license.