Personalized multi-peptide vaccines are currently discussed intensively for tumor immunotherapy. In order to identify epitopes - short, immunogenic peptides - suitable for eliciting a tumor-specific immune response, human leukocyte antigen (HLA)-presented peptides are isolated by immunoaffinity purification from cancer tissue samples and analyzed by liquid chromatography-coupled tandem mass spectrometry (LC-MS/MS). Here, we present MHCquant, a fully automated, portable computational pipeline able to process LC-MS/MS data automatically and generate annotated, FDR-controlled lists of (neo-)epitopes with associated relative quantification information. We could show that MHCquant achieves higher sensitivity than established methods. While obtaining the highest number of unique peptides, the rate of predicted MHC binders remains still comparable to other tools.