By Katia S. Guimarães, Anna Panchenko, Teresa M. Przytycka
This ebook constitutes the refereed court cases 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 conscientiously reviewed and chosen from fifty five submissions. The papers are geared up in topical sections on algorithmic ways for molecular biology difficulties; micro-array research; computer studying equipment for type; 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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Extra info for Advances in Bioinformatics and Computational Biology: 4th Brazilian Symposium on Bioinformatics, BSB 2009, Porto Alegre, Brazil, July 29-31, 2009, Proceedings
Spectra Comparison: In spectra comparison, an experimental spectrum is compared with theoretically predicted spectra. The theoretical spectra are inferred from the diﬀerent peptides generated by an in-silico digestion of all the proteins contained in a database. A score function is used to evaluate each possible comparison, and the results are ordered according to this score. The theoretical spectrum with the best score is associated to the corresponding experimental spectrum. There are a number of existing softwares that match uninterpreted MS/MS experimental spectra to theoretical spectra, such as SEQUEST  or MASCOT .
The ﬁrst one is a set of 1000 simulated spectra of size [10, 25] with 50% of noise peaks, generated the same way as described at the beginning of Section 4. On this dataset, a spectrum contains on average 150 peaks. Then we use three sets of 140 experimental maize spectra on which we apply diﬀerent ﬁlters: (1) we keep all peaks (meaning an average of 275 peaks per spectrum), (2) we keep the 100 most intense peaks, and (3) we keep the 50 most intense peaks. Table 1 shows the evolution of the 34 F.
The distance between the first and the last peak of a packet)*/ 7: M [p][m][k] = max(D[p][m][k], M [p − 1][m][k], M [p][m − 1][k]) 8: end for 9: end for 10: end for 11: return M [N bP acket][M AX][K] /*N bP acket is the number of packets composing SSt and M AX is the highest mass represented by a peak inside SSe */ Fig. 7. This ﬁgure shows how to ﬁnd a value val = D[p ][m − (Rp − Rp )][k], with p
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