By Gowtham Atluri, Rohit Gupta, Gang Fang, Gaurav Pandey, Michael Steinbach, Vipin Kumar (auth.), Sanguthevar Rajasekaran (eds.)

This ebook constitutes the refereed complaints of the 1st overseas on Bioinformatics and Computational Biology, BICoB 2007, held in New Orleans, los angeles, united states, in April 2007.

The 30 revised complete papers awarded including 10 invited lectures have been rigorously reviewed and chosen from seventy two preliminary submissions. The papers tackle present study within the quarter of bioinformatics and computational biology fostering the development of computing suggestions and their software to existence sciences in subject matters equivalent to genome research series research, phylogenetics, structural bioinformatics, research of high-throughput organic info, genetics and inhabitants research, in addition to structures biology.

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Extra resources for Bioinformatics and Computational Biology: First International Conference, BICoB 2009, New Orleans, LA, USA, April 8-10, 2009. Proceedings

Example text

Large-scale phosphorylation mapping reveals the extent of tyrosine phosphorylation in Arabidopsis. Mol. Syst. Biol. 4, 193 (2008) 28 J. Gao et al. 7. ELM: a database of phosphorylation sites–update 2008. Nucleic Acids Res. 36(Database issue), D240–D244 (2008) 8. : PHOSIDA (phosphorylation site database): management, structural and evolutionary investigation, and prediction of phosphosites. Genome Biol. 8, R250 (2007) 9. : The PlantsP and PlantsT Functional Genomics Databases. Nucleic Acids Res.

A previous study demonstrated that disorder information significantly improved the discrimination between phosphorylation and non-phosphorylation sites [15]. With increasing volume of empirical phosphorylation sites, it is advantageous to use nearest neighbor information. Test results on the PhosPhAt [10] dataset of phosphoserines and the TAIR7 [23] non-redundant protein database indeed shows the remarkable performance of our proposed phosphorylation prediction method. 2 Materials and Methods Phosphorylation site prediction can be formulated as a binary classification problem, namely each serine/threonine/tyrosine can be classified as either phosphorylation site or non-phosphorylation site.

This may be because some phosphoserines were not discovered by the experiments in [10] and as a result were incorrectly classified as non-phosphoserines. Alternatively, this could also reflect the general preference of serine in disordered regions. In any case, it is clear that phosphoserines in this dataset are significantly overrepresented in disordered regions. 5), while this percentage is only ~57% for non-phosphoserines. 3 Amino Acid Frequency Features In this section, we will study the amino acid composition surrounding the phosphoserines.

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