UC San Diego

Tandem mass spectrometry has become a leading method of analyzing large-scale proteomics data, necessitating fast and accurate computational methods of interpreting mass spectrometer output to identify the contents of protein samples. A wide array of algorithms have been developed to this end – protein database searches are a common approach, but other types of searches, such as spectral library searches, and post-translational modification-based searches, may also be valuable tools. It is often advantageous to run a single dataset through multiple algorithms, given that one search algorithm may be able to identify spectra that other algorithms cannot identify, or identify certain types of spectra more quickly than other algorithms. However, combining the results from multiple searches manually is time-consuming and prone to error, and can make interpretation of the results difficult. The Maestro workflow is introduced here, which runs spectra automatically through multiple search algorithms, aggregates the results, and produces in-depth analyses and visualizations of the data. Maestro identifies a wide variety of peptides and modifications.