Evaluation of moFF and FlashLFQ for label free peptide quantification in proteomic workflows within Galaxy-P framework.
Creators: name: Mehta, Subina
name: Easterly, Caleb
name: Johnson, James E.
name: Bjoern Gruening
name: Argentini, Andrea
name: Millikin, Robert J.
name: Shortreed, Michael R.
name: McGowan, Thomas
name: Kumar, Praveen
name: Martens, Lennart
name: Smith, Lloyd M.
name: Griffin, Timothy J.
name: Pratik Jagtap
Publisher: name: F1000 Research Limited
Year: 2018

description: Protein/peptide-level quantification (either labeled or label-free) is routinely used in shotgun proteomics data analysis for determining the abundance of proteins in a given sample. However, accurate, rapid and robust label-free quantification is still a major challenge in the field of quantitative proteomics. Label-free quantification (LFQ) based on the precursor peak (MS1) intensities is considered reliable due to its dynamic range and accuracy. LFQ has taken on new importance for multi-omic applications; it allows quantification of unmodified or modified peptides (proteomics), variant peptides (proteogenomics) or metapeptides (metaproteomics). Quantification of peptides after identification helps researchers to discover proteins that are differentially expressed across conditions, identify potential biomarkers, explore protein modifications, or assess other biological processes of the system under study. In this study, we have evaluated and implemented the open-source LFQ tools i.e. moFF (DOI:10.1038/nmeth.4075) and FlashLFQ (DOI:10.1021/acs.jproteome.7b00608) in the Galaxy-P platform. For testing and evaluating moFF and FlashLFQ software, samples from the ABRF-PRG (Association of Biomolecular Research Facilities - Proteomics Research Group) study were used. The data was generated using an Orbitrap instrument from fractionated human cell lysate samples with 4 spiked-in non-human proteins at 20-500 fmol amounts and a negative control sample were analyzed. Outputs generated from these tools were compared with MaxQuant results (DOI:10.1038/nprot.2016.136). The tools were run both outside and within Galaxy platform to confirm their reproducibility. For testing moFF and FlashLFQ, a tabular peptide spectral match (PSM) report (output from SearchGUI and Peptide Shaker) and tabular output from MaxQuant (msms.txt) were used, along with the RAW files. We compared the results of extracting MS1 intensity with and without “match between runs” (MBR) (sharing peptide identifications across files), in terms of number of peptides quantified and the total peptide intensities obtained. The intensities obtained from the quantification tools were normalized and ratios estimated from these measurements were compared with the true amounts of spiked-in proteins and their fold change ratios. We evaluated the correlation between the peptide and protein intensities estimated by FlashLFQ, moFF, and MaxQuant, and compared the number of peptides quantified; these observations will be presented and discussed. Various modes of quantification for fractionated samples were tested and results will be presented. The developers of these software tools were provided with the feedback after active testing, so that parameters and features in the software tools could be optimized. Results from this evaluation study will be used as a benchmark for future proteomics and multi-omics quantification studies utilizing the Galaxy platform.