MS-Reduce is a linear-time tool that allows massive reduction in amount of mass spectrometry data without significantly reducing the quality of the peptide deduction. Our novel data-reductive strategy for analysis of Big MS data is called MS-REDUCE and is capable of eliminating noisy peaks as well as peaks that do not contribute to peptide deduction before any peptide deduction is attempted. Our experiments have shown up to 100x speed up over existing state of the art noise elimination algorithms while maintaining comparable high quality matches. Using our approach we were able to process a million spectra in just under an hour on a moderate server which will be especially useful for processing in high-throughput environments. The algorithms has been implemented in Java and code/associated data sets.
Status
The method development for this work is complete