The choice of search engine for interpretation of immunopeptidomics datasets affects the sequencing depth and the extent of detected HLA allele-specific peptide repertoires

Standardisation of Immunopeptidomics experiments across laboratories is a pressing issue within the field, and currently a variety of different methods for sample preparation and data analysis tools are applied. Here, we compared different software packages commonly used to interrogate immunopeptidomics datasets, in order to understand to which extent differences in performance can be observed. We found that a de novo-assisted database search reports substantially more peptide sequences (~30-70%) compared to three database search engines at a global FDR of <1%. This effect was reproducible across four immunopeptidomic datasets. We validated the results using data generated with a synthetic library of 2000 HLA-associated peptides from four HLA alleles, half of which were previously observed by LC-MS, and half were predicted only. Our investigation reveals that search engines create a bias in peptide sequence length distribution and peptide amino acid composition. Therefore, the choice of peptide identification method highly influences the proportion of peptide sequences identified for each HLA allele, and resulting data should be interpreted with caution.