A dataset for training/testing machine learning models of peptide feature detection: Human HELA LC-MSMS

We introduce MSTracer, a tool for peptide feature detection from MS1, which incorporates a machine-learning-combined scoring function based on peptide isotopic distribution and peptide intensity shape on the LC-MS map. By using Support Vector Regression (SVR), the quality of detected peptide features is remarkably improved. By utilising Neural Networks (NN), scores that indicate the quality of features are assigned for detected features as well. We use the Human HELA LC-MSMS dataset to train and test the results and compare with MaxQuant, OpenMS, and Dinosaur.