By Katia S. Guimarães, Anna Panchenko, Teresa M. Przytycka
This ebook constitutes the refereed lawsuits of the 4th Brazilian Symposium on Bioinformatics, BSB 2009, held in Porto Alegre, Brazil, in July 2009.
The 12 revised complete papers and six prolonged abstracts have been rigorously reviewed and chosen from fifty five submissions. The papers are prepared in topical sections on algorithmic methods for molecular biology difficulties; micro-array research; desktop studying tools for category; and in silico simulation.
Read Online or Download Advances in Bioinformatics and Computational Biology: 4th Brazilian Symposium on Bioinformatics, BSB 2009, Porto Alegre, Brazil, July 29-31, 2009, Proceedings PDF
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Additional resources for Advances in Bioinformatics and Computational Biology: 4th Brazilian Symposium on Bioinformatics, BSB 2009, Porto Alegre, Brazil, July 29-31, 2009, Proceedings
This process provides for each peptide an experimental spectrum in the form of a series of peaks, each peak corresponding to a mass that has been measured. From these experimental spectra, we aim at retrieving the corresponding peptide sequences. S. Guimar˜ aes, A. M. ): BSB 2009, LNBI 5676, pp. 24–35, 2009. c Springer-Verlag Berlin Heidelberg 2009 Comparison of Spectra in Unsequenced Species 25 database such as SwissProt. Given an experimental spectrum, there are two main possibilities to obtain the corresponding peptide sequence: de novo sequencing or spectra comparison.
In particular, we use the same score function, which is the number of common peaks in both spectra. The number of shifts used by these two methods is set dynamically, depending on the size of the two compared spectra. We could have ﬁxed this to a constant value, but allowing for instance N2 shifts in an N amino acids long peptide does not make any sense, so we chose M to allow k shifts for a peptide of mass M where k = 600 + 1. In the case of PSA, the threshold T used to determine the possible masses kept in symmetric experimental spectra is set to 2.
Sorting by Transpositions. SIAM Journal on Discrete Mathematics 11(2), 224–240 (1998) 4. : A New and Faster Method of Sorting by Transpositions. , Zhang, K. ) CPM 2007. LNCS, vol. 4580, pp. 131–141. Springer, Heidelberg (2007) 5. : A Column-Generation Based Branch-andBound Algorithm for Sorting by Reversals. DIMACS Series in Discrete Mathematics and Theoretical Computer Science, The American Mathematical Society 47, 213–226 (1999) 6. : Sorting Permutations by Reversals through Branch-and-Price.
Advances in Bioinformatics and Computational Biology: 4th Brazilian Symposium on Bioinformatics, BSB 2009, Porto Alegre, Brazil, July 29-31, 2009, Proceedings by Katia S. Guimarães, Anna Panchenko, Teresa M. Przytycka