GhostMS: An error-controlled machine learning approach to efficient alignment and quantification of multi-sample experiments in Mass Spectrometry-based Proteomics
In recent years, the field of mass spectrometry (MS) has grown significantly, allowing for shotgun proteomic experiments to be used increasingly in biomarker discovery experiments. However, using standard methods of MS not all peptides present in a sample may be identified. To combat this, a procedure termed alignment, or match-between-runs, is used to propagate identifications from one run to ano