Introduction Label Free Quantitation in MaxQuant GlyGly Diagnostic Peaks New Annotation Visualization OptionsLabel Free Quantitation (LFQ) Compare quantitative measures of proteins/peptides BETWEEN mass spec runs to identify significant changesComparison is WITHIN the mass spec run for SILAC, iTRAQ, and IPTL No chemical or metabolic labeling required Compatible with virtually any sampleSpectral Counting # MS/MS identified for each protein Very crude Easily implemented Somewhat sample dependentSpectral counts are sometimes normalized across mass spec runsExtracted Ion Chromatogram (XIC) Sum of areas under the curves of MS1 chromatograms of each peptide identified Requires advanced processing software Somewhat independent of the sampleLabel Free Quantitation in MaxQuant SILAC ratio calculation requires the comparison of XICs between the label states in a mass spec run Can use same processing to calculate XICs of peptides in each run and then compare across mass spec runs Need to map from one mass spec run to others Easy if MS2 identified in both runs Harder if not identified in both runs MaxQuant has option to match identified peaks to unidentified peak using retention time and accurate massLFQ Options in MaxQuant Peptide Level Raw intensity only option Protein Level Raw intensity MS2 counts LFQ value No publication yet Calculate global raw file normalization factor iBAQ Normalize intensities by number of observable peptidesBenchmarking LFQ Options Triplicate yeast analyses Pearson Correlation Typical correlation measure Sensitive to outliers Spearman Rank Correlation Only looks at ranking Insensitive to outliers Ratio 95% Confidence Interval Tells us the typical range of ratios we would expect to see for values that are actually unchangedPeptide LFQ PerformanceProtein Raw Intensity LFQ PerformanceProtein Spectral Counts LFQ PerformanceProtein iBAQ LFQ PerformanceProtein LFQ PerformanceLFQ Summary Now possible to get reasonable estimates of relative abundances between samples at both the peptide and protein level Protein 95% CI at 1.5 fold change for 1:1 Peptide 95% CI at 3 fold change for 1:1 Might consider using these techniques to measure large changes in modification status For example, protein footprintingUbiquitin Modification Data Mining Ubiquitin modifications result in a diGlycine modification on Lysine Geoff had produced a large dataset potentially containing many ubiquitin modifications Unfortunately, the error rate for these modifications was quite high Wondered if there were characteristics of modified peptides that made them difficult to identify in a standard database search Analyzed peptides with modified Lysines at various positions Sonja identified peaks that would correspond to backbone fragmentation between K-G (and between G-G)Ubi Modifications The K-G and G-G bonds are both peptide bonds and are therefore susceptible to fragmentation We should see diagnostic fragment ions g1 and g2 that correspond to the peptide without one or two GlycinesG G A-A-K-A-T-Rg2g1Ubi Modified Spectrag2g1Ubi Modified Spectra Stats What are the typical charge states of the fragment ions missing Glycines? How do these charge states depend on the precursor charge state? How intense are the diagnostic fragment ions? Are there any explanation for modified peptides missing diagnostic peaks? Use Tsui-Fen and Gygi ubiquitin-enriched data sets to mine the data for answersUbi Modified Spectra Stats In general, modified spectra tend to have a higher charge state than unmodified spectraUbi Diagnostic Ion StatisticsTsui-Fen DatasetUbi Diagnostic Ion StatisticsTsui-Fen DatasetUbi Diagnostic Ion StatisticsTsui-Fen DatasetUbi Diagnostic Ion StatisticsTsui-Fen DatasetUbi Diagnostic Ion StatisticsTsui-Fen Dataset Gygi DatasetUbi Summary There does appear to be a diagnostic ion in many modified peptides How does the charge state of the diagnostic ion correlate with the precursor ion? Is there any correlation between the peptides that do not have an observable (or strong) diagnostic ion Unclear how specific mass spec settings may affect the formation of the diagnostic ionsVisualizing DAVID Analysis In a recent Mann paper, they used a non-parametric statistical test to determine if the rank of the protein ratios with a particular GO annotation were significantly higher or lower than the other proteins Mann-Whitney-Wilcoxon Test No relation to Matthias Mann Additionally they used a “violin plot” to display the distribution of the ratios Developed scripts to perform similar calculations on data for BobbyVolcano Plot of DAVID AnalysisViolin Plot of Significant TermsSILAC Analysis Summary New plots help to detect interesting terms where there are few individual proteins that are significantly different, but the majority of t