Graph Tree Decomposition Based Fast Peptide Sequencing and Spectral Alignment

De novo sequencing and spectral alignment are computationally important for the prediction of new peptides via tandem mass spectrometry (MS/MS). Both approaches are established upon the technique of finding the longest antisymmetric path on formulated graphs. The task is often complicated and the prediction accuracy is compromised when given spectra involve noise data, missing mass peaks, or post translational modifications/mutations. This paper introduces a graphical mechanism to describe relationships among mass peaks that, through 1 The preliminary version of this paper appeared in the proceedings of the 11th Pacific Symposium on Biocomputing (PSB2006). * Corresponding Author. Email: [email protected]. International Journal of Computational Science 1992-6669 (Print) 1992-6677 (Online) www.gip.hk/ijcs © 2008 Global Information Publisher (H.K) Co., Ltd. All rights reserved. Graph Tree Decomposition Based Fast Peptide Sequencing and Spectral Alignment GLOBAL INFORMATION PUBLISHER 1 graph tree decomposition, yields linear time and quadratic algorithms for optimal de novo sequencing and spectral alignment respectively. Our test results show that, in addition to high efficiency, the new algorithms can achieve desired prediction accuracy on spectra containing noise peaks and post translational modifications (PTMs) while allowing the presence of both b-ions and y-ions.