Issue No. 03 - May-June (2015 vol. 12)
Liang Dong , Dept. of Comput. Sci. & Technol., Shandong Univ., Jinan, China
Bing Shi , Dept. of Comput. Sci. & Technol., Shandong Univ., Jinan, China
Guangdong Tian , Transp. Coll., Northeast Forestry Univ., Harbin, China
YanBo Li , Inst. of Comput. Technol., Beijing, China
Bing Wang , Inst. of Comput. Technol., Beijing, China
MengChu Zhou , Inst. of Syst. Eng., Macau Univ. of Sci. & Technol., Macau, China
Determining the glycan topology automatically from mass spectra represents a great challenge. Existing methods fall into approximate and exact ones. The former including greedy and heuristic ones can reduce the computational complexity, but suffer from information lost in the procedure of glycan interpretation. The latter including dynamic programming and exhaustive enumeration are much slower than the former. In the past years, nearly all emerging methods adopted a tree structure to represent a glycan. They share such problems as repetitive peak counting in reconstructing a candidate structure. Besides, tree-based glycan representation methods often have to give different computational formulas for binary and ternary glycans. We propose a new directed acyclic graph structure for glycan representation. Based on it, this work develops a de novo algorithm to accurately reconstruct the tree structure iteratively from mass spectra with logical constraints and some known biosynthesis rules, by a single computational formula. The experiments on multiple complex glycans extracted from human serum show that the proposed algorithm can achieve higher accuracy to determine a glycan topology than prior methods without increasing computational burden.
trees (mathematics), bioinformatics, computational complexity, graph theory, mass spectra, molecular biophysics, molecular configurations
Liang Dong, Bing Shi, Guangdong Tian, YanBo Li, Bing Wang and MengChu Zhou, "An Accurate de novo Algorithm for Glycan Topology Determination from Mass Spectra," in IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 12, no. 3, pp. 568-578, 2015.