Reducing the cost of protein identifications from mass spectrometry databases

We present two techniques to improve the computational efficiency of protein discovery from mass spectrometry databases: noise filtering and hierarchical searching. Our approaches are orthogonal to existing algorithms and are based on the observation that typical mass spectrometry data contains a large amount of noise that can lead to wasteful computation. Our first improvement uses standard machine learning techniques with novel feature vectors derived from the mass spectra to identify and filter the noisy spectra. We demonstrate this approach results in computational gains of around 38% with less than 10% loss of peptides. Additionally we present a hierarchical searching scheme in which most samples are matched against a small database at low computational cost, leaving only a small number of samples to be searched against larger databases. Combining this scheme with the machine learning filters leads to a further performance improvement of 3%.