Mass spectrometry (MS) is used in peptidomics to find novel endogenous peptides that may lead to the discovery of new biomarkers. Identifying endogenous peptides from MS is a time-consuming and challenging task; storing identified peptides in a database and comparing them against unknown peptides from other MS runs avoids re-doing identification. MS produce large amounts of data, making interpretation difficult. A platform for helping the identification of endogenous peptides was developed in this project, including a library application for storing peptide data. Machine learning methods were also used to try to find patterns in peptide abundance that could be correlated to a specific sample or treatment type, which can help focus the identification work on peptides of high interest.