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

One of the key steps in data dependent acquisition (DDA)proteomicsis 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 recentyears, each relying on its own matching algorithm. Here, we providean integrated solution, the intensity-based Quantitative Mix and MatchApproach (IQMMA), which integrates a number of untargeted peptidefeature detection algorithms and returns the most probable intensityvalues for the MS/MS-based identifications. IQMMA was tested usingavailable proteomic data acquired for both well-characterized (groundtruth) and real-world biological samples, including a mix of Yeastand E. coli digests spiked at different concentrationsinto the Human K562 digest used as a background, and a set of glioblastomacell lines. Three open-source feature detection algorithms were integrated:Dinosaur, biosaur2, and OpenMS FeatureFinder. None of them was foundoptimal when applied individually to all the data sets employed inthis work; however, their combined use in IQMMA improved efficiencyof subsequent protein quantitation. The software implementing IQMMAis freely available at https://github.com/PostoenkoVI/IQMMA under Apache 2.0 license.

Automated summary

One of the key steps in DDA proteomics is detecting peptide isotopic clusters and matching them to MS/MS-based peptide identifications. IQMMA is an integrated solution that combines multiple untargeted peptide feature detection algorithms to provide the most probable intensity values for the MS/MS-based identifications.