By Mohamed Ibrahim Abouelhoda, Enno Ohlebusch (auth.), Gary Benson, Roderic D. M. Page (eds.)
This booklet constitutes the refereed lawsuits of the 3rd foreign Workshop on Algorithms in Bioinformatics, WABI 2003, held in Budapest, Hungary, in September 2003.
The 36 revised complete papers provided have been rigorously reviewed and chosen from seventy eight submissions. The papers are prepared in topical sections on comparative genomics, database looking out, gene discovering and expression, genome mapping, development and motif discovery, phylogenetic research, polymorphism, protein constitution, series alignment, and string algorithms.
Read or Download Algorithms in Bioinformatics: Third International Workshop, WABI 2003, Budapest, Hungary, September 15-20, 2003. Proceedings PDF
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Additional info for Algorithms in Bioinformatics: Third International Workshop, WABI 2003, Budapest, Hungary, September 15-20, 2003. Proceedings
I. A. Papayannopoulos. The interpretation of collision-induced dissociation mass spectra of peptides. Mass Spectrometry Review, 14:49–73, 1995. 19. D. J. Papin, P. Hojrup, and A. J. Bleasby. Rapid identiﬁcation of proteins by peptide-mass ﬁngerprinting. Curr. , 3:327–332, 1993. 20. D. N. Perkins, D. J. Pappin, D. M. Creasy, and J. S. Cottrell. Probability-based protein identiﬁcation by searching sequence databases using mass spectrometry data. Electrophoresis, 20:3551–3567, 1999. 21. K. Petritis, L.
Tabb, L. L. Smith, L. A. Breci, V. H. Wysocki, D. Lin, and J. Yates. Statistical characterization of ion trap tandem mass spectra from doubly charged tryptic peptides. Anal. , 75:1155–1163, 2003. 28. J. Yates, J. , and Eng. Identiﬁcation of nucleotides, amino acids, or carbohydrates by mass spectrometry. United States Patent 6,017,693, 1994. 29. N. Zhang, R. Aebersold, and B. Schwikowski. ProbId: A probabilistic algorithm to identify peptides through sequence database searching using tandem mass spectral data.
Following the lemma 3, we don’t need to consider each position more than once in an i-path (P ositionP arsed is a boolean table maintained to avoid such useless iterations). Complexity of this part becomes linear with the length of s in time and space. start end if push(k, S) end for Compute the table RightDistance for k = |s | + 1 to 0 do if ri (sk ) < iMax and RightN eighbour(k) is not empty then t ← the greatest position in the stack ≤ RightN eighbour(k) (computed by binary search) RightDistance[k] ← ri (st ) end if while ri (stop(S) ) < ri (sk ) do pop(S) end while push(k, S) end for Compute the table Successor for all position k of s do if ri (sk ) < iMax and min(Lef tDistance[k], RightDistance[k]) < +∞ then if Lef tDistance[k] ≤ RightDistance[k] then Successor(k) ← Lef tN eighbour(k) else Successor(k) ← RightN eighbour(k) end if else Successor(k) ← empty end if end for Parse the i-paths and output the common intervals Initialize the table P ositionP arsed to false for all position j of i-rank 1 of s do BoundL ← j ; BoundR ← j repeat BoundL ← min(j, BoundL) ; BoundR ← max(j, BoundR) if [BoundL, BoundR] ⊂ Ii (j) then output ([i, pi (ri (sk ))], Ii (j)) a common interval end if P ositionP arsed(j) ← true ; j ← Successor(j) until j is empty or P ositionP arsed(j) is true end for end for 24 G.