Services

Data Dependent Acquisition

Quantification by precursor intensity is performed using MaxQuant and determines peptide/protein abundances from raw data. This intensity-based quantification is calculated using the iBAQ algorithm, which sums the intensities of observed peptides and normalizes against the number of peptides in the protein that are predicted to be observed in a mass spectrometry experiment. The MS1 iBAQ normalized intensities are then analyzed using proteoDA.

TMT Quantification

Data are searched via MaxQuant to identify proteins and extract MS3 reporter ion intensities. The data are then analyzed using proteoDA. Users that request multiple TMT batches are encouraged to use the DIA approach to avoid TMT batch effects.

Phosphoproteomics

Total protein lysate and the phosphopeptide enriched lysates are analyzed separately by TMT. For proteins, the MS3 reporter ion intensities are analyzed using proteoDA for normalization and to perform statistical analysis using limma with empirical Bayes smoothing to the standard errors. For phosphopeptides, we filter to retain peptides with a localization probability >75% and to remove peptides with zero values, and then log2 transformed for differential analysis using limma. Proteins and phosphopeptides with an FDR-adjusted p-value <0.05 and an absolute fold change >2 are significant. The analysis then accounts for changes at the protein level versus PTM level by qualitatively evaluating differentially abundant proteins from both analyses. Proteins may not be differentially expressed between conditions instead; a difference may be found at the PTM level.

Data Independent Acquisition

Data are searched using Spectronaut against the UniProtKB organism specific database using the directDIA method with an identification precursor and protein q-value cutoff of 1%, generate decoys set to true, protein inference workflow set to maxLFQ, inference algorithm set to IDPicker, quantity level set to MS2, cross-run normalization set to false, and protein grouping quantification set to median peptide and precursor quantity. MS2 intensity values are assessed for quality using ProteiNorm. Data are normalized using the method that provides the least variability among sample group replicates and highest replicate correlation, and then analyzed using proteoDA to perform statistical analysis using limma with empirical Bayes smoothing to the standard errors. Proteins with an FDR adjusted p-value <0.05 and a fold change >2 is significant.

Targeted Analysis

The total response for each protein is calculated as the geometric mean of all peptides measured for that protein, typically 2 to 3. Amount of each protein in the sample, in pmol, is determined by dividing the total response of the protein by the total response of the bovine serum albumin and multiplying by the amount of albumin added. Finally, concentration as pmol/ug total protein is calculated by dividing by the amount of protein taken for analysis. This approach to data analysis incorporates the Best Flyers system described by Aebersold’s laboratory. Data are analyzed using Skyline and R scripts. 

Statistical Analysis

There are multiple statistical approaches for proteomics data that are dependent on the user’s research question and the data distribution (e.g., t-tests, ANOVA, linear mixed models such as Limma, ROTS, non-parametric alternatives, data mining algorithms). The experimental design, mass spectrometry data acquisition method, and the data distribution determines the appropriate statistical approach.

Data Deliverable and User Interface