tooluniverse-proteomics-analysis

Load the proteinGroups.txt file, filter proteins with 2+ unique peptides detected in at least 3 samples, remove contaminants and reverse sequences, impute missing values, normalize intensities, then run t-tests with Benjamini-Hochberg correction. Significant proteins require adj. p < 0.05 and |log2FC| > 1.

For label-free quantification (LFQ), use median or quantile normalization. TMT/iTRAQ experiments typically use TMM or VSN normalization, while SILAC uses ratios computed within the same MS run. The choice depends on your quantification strategy and experimental design.

A minimum of 3 biological replicates per condition is required for reliable statistics, with 4 or more preferred for clinical samples. Fewer than 3 replicates cannot estimate variance reliably, and significance should never be reported from duplicates.

Yes, it analyzes modification-specific peptides such as MaxQuant Phospho (STY)Sites.txt, filtering by localization probability above 0.75. It constructs site identifiers, runs differential phosphorylation analysis, and predicts upstream kinases using kinase-substrate databases.

Protein-RNA correlation is typically moderate (r ~ 0.4-0.6) due to post-transcriptional regulation. Genes with r < 0.2 and high protein but low RNA indicate translational regulation, while low protein with high RNA suggests protein degradation.

MS proteomics provides relative rather than absolute quantification unless TMT or SILAC is used, has limited dynamic range compared to RNA sequencing, and typically collapses protein isoforms to gene level. High missing value rates above 50% per protein limit statistical power.