TITLE: Quantitative Proteomic Data Analysis
AUTHORS: Shiyi Wang

[DESCRIPTION]
Quantitative Proteomic Data Analysis

[STEPS]
1. **Data Import and Alignment** - Import data from 15 UPLC-MS/MS analyses into Proteome Discoverer 3.0 (Thermo Scientific Inc.). - Exclude conditioning runs but include 3 replicate SPQC samples. - Align individual LCMS data files based on accurate mass and retention time of detected precursor ions using the Minora Feature Detector algorithm.
2. **Relative Peptide Abundance Measurement** - Measure relative peptide abundance based on peak intensities of selected ion chromatograms of aligned features across all runs.
3. **MS/MS Data Search** - Search MS/MS data against the SwissProt M. musculus database, common contaminant/spiked protein database, and reversed-sequence decoys for false discovery rate determination. - Use Sequest with INFERYS to produce fragment ion spectra and perform database searches. - Database search parameters: - Fixed modification: Cys (carbamidomethyl) - Variable modification: Met (oxidation) - Search tolerances: 2ppm precursor and 0.8Da product ion with full trypsin enzyme rules. - Annotate data at a maximum 1% protein false discovery rate using Peptide Validator and Protein FDR Validator nodes in Proteome Discoverer.
4. **Peptide and Protein Homology** - Address peptide homology using razor rules, exclusively assigning a peptide matched to multiple different proteins to the protein with more identified peptides. - Address protein homology by grouping proteins with the same set of peptides and assigning a master protein based on % coverage.
5. **Data Filtering and Normalization** - Apply a filter to remove peptides not measured at least twice across all samples and in at least 50% of the replicates in any single group. - Total intensity normalization: Sum total intensity of all peptides for a sample and normalize across all samples.
6. **Imputation Strategy for Missing Values** - If less than half of the values are missing in a biological group, impute values with an intensity derived from a normal distribution of all values within the same intensity range (20 bins). - If greater than half values are missing for a peptide in a group and peptide intensity is > 5e6, set measured intensity to 0. - Impute all remaining missing values with the lowest 2% of all detected values.
7. **Trimmed-Mean Normalization** - Exclude the top and bottom 10 percent of the signals. - Use the average of the remaining values to normalize across all samples. - Sum peptide intensities belonging to the same protein into a single intensity for analysis.
8. **Technical Reproducibility Assessment** - Calculate the % coefficient of variation (%CV) for each protein across 3 injections of an SPQC pool. - The mean %CV of the SPQC pools should be 11.7%.
9. **Biological + Technical Variability Assessment** - Measure %CVs for each protein across the individual groups, averaging 19.8%.
10. **Initial Statistical Analysis** - Calculate fold-changes between sample groups based on protein expression values. - Perform two-tailed heteroscedastic t-test on log2-transformed data. - Annotate proteins significantly more abundant (Up, fold change >1.5 and p-value <0.05) or less abundant (Down, fold change < -1.5 and p-value <0.05) in a particular genotype or BioID sample group.
11. **Downstream Analysis** - Use Cytoscape (v.3.9.1) for protein interaction networks. - Perform Gene Ontology (GO) enrichment analysis using the ClusterProfiler package for R, with all M. musculus genes as the reference background.