PARPST: a PARallel algorithm to find peptide sequence tags

Background: Protein identification is one of the most challenging problems in proteomics. Tandem mass spectrometry provides an important tool to handle the protein identification problem. Results: We developed a work-efficient parallel algorithm for the peptide sequence tag problem. The algorithm runs on the concurrent-read, exclusive-write PRAM in O(n) time using log n processors, where n is the number of mass peaks in the spectrum. The algorithm is able to find all the sequence tags having score greater than a parameter or all the sequence tags of maximum length. Our tests on 1507 spectra in the Open Proteomics Database shown that our algorithm is efficient and effective since achieves comparable results to other methods. Conclusions: The proposed algorithm can be used to speed up the database searching or to identify post-translational modifications, comparing the homology of the sequence tags found with the sequences in the biological database.