Computational phosphorylation site prediction in plants using random forests and organism-specific instance weights

Bioinformatics

Volumn 29, Issue 6, 2013, Pages 686-694

  Trost, Brett ^a   Kusalik, Anthony ^a  

^a UNIVERSITY OF SASKATCHEWAN   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

VEGETABLE PROTEIN;

ANIMAL; ARTICLE; ARTIFICIAL INTELLIGENCE; BIOLOGY; CATTLE; CHEMISTRY; COMPUTER PROGRAM; HUMAN; METABOLISM; METHODOLOGY; MOUSE; PHOSPHORYLATION; PROTEIN PROCESSING; SEQUENCE ALIGNMENT; SEQUENCE ANALYSIS; SOYBEAN;

ANIMALS; ARTIFICIAL INTELLIGENCE; CATTLE; COMPUTATIONAL BIOLOGY; HUMANS; MICE; PHOSPHORYLATION; PLANT PROTEINS; PROTEIN PROCESSING, POST-TRANSLATIONAL; SEQUENCE ALIGNMENT; SEQUENCE ANALYSIS, PROTEIN; SOFTWARE; SOYBEANS;

EID: 84875173596     PISSN: 13674803     EISSN: 14602059     Source Type: Journal    
DOI: 10.1093/bioinformatics/btt031     Document Type: Article

References (49)

1. Apweiler,R. et al. (2004) UniProt: the Universal Protein knowledgebase. Nucleic Acids Res., 32, D115-D119.
2. Biswas,A.K. et al. (2010) Machine learning approach to predict protein phosphorylation sites by incorporating evolutionary information. BMC Bioinformatics, 11, 273.
3. Blom,N. et al. (1999) Sequence and structure-based prediction of eukaryotic protein phosphorylation sites. J. Mol. Biol., 294, 1351-1362.
4. Blom,N. et al. (2004) Prediction of post-translational glycosylation and phosphorylation of proteins from the amino acid sequence. Proteomics, 4, 1633-1649.
5. Breiman,L. (2001) Random Forests. Mach. Learn., 45, 5-32.
6. Bu,Y.H. et al. (2010) Insulin receptor substrate 1 regulates the cellular differentiation and the matrix metallopeptidase expression of preosteoblastic cells. J. Endocrinol., 206, 271-277.
7. Champion,A. et al. (2004) Arabidopsis kinome: after the casting. Funct. Integr. Genomics., 4, 163-187.
8. Cherry,J.M. et al. (1998) SGD: Saccharomyces genome database. Nucleic Acids Res., 26, 73-79.
9. Diella,F. et al. (2004) Phospho.ELM: a database of experimentally verified phosphorylation sites in eukaryotic proteins. BMC Bioinformatics, 5, 79.
10. Diella,F. et al. (2008) Phospho.ELM: a database of phosphorylation sites-update 2008.Nucleic Acids Res., 36, D240-D244.
11. Diks,S.H. et al. (2007) Evidence for a minimal eukaryotic phosphoproteome? PLoS One, 2, e777.
12. Dinkel,H. et al. (2011) Phospho.ELM: a database of phosphorylation sites-update 2011.Nucleic Acids Res., 39, D261-D267.
13. Durek,P. et al. (2010) PhosPhAt: the Arabidopsis thaliana phosphorylation site database. An update. Nucleic Acids Res., 38, D828-D834.
14. Engel,S.R. et al. (2010) Saccharomyces genome database provides mutant phenotype data. Nucleic Acids Res., 38, D433-D436.
15. Frank,E. et al. (2004) Data mining in bioinformatics using Weka. Bioinformatics, 20, 2479-2481.
16. Gao,J. et al. (2009a) A new machine learning approach for protein phosphorylation site prediction in plants. Lect. Notes Comput. Sci., 5462/2009, 18-29.
17. Gao,J. et al. (2009b) P3DB: a plant protein phosphorylation database. Nucleic Acids Res., 37, D960-D962.
18. Goecks,J. et al. (2010) Galaxy Team. (2010) Galaxy: a comprehensive approach for supporting accessible, reproducible, and transparent computational research in the life sciences. Genome Biol., 11, R86.
19. Goodstein,D.M. et al. (2012) Phytozome: a comparative platform for green plant genomics. Nucleic Acids Res., 40, D1178-D1186.
20. Heazlewood,J.L. et al. (2008) PhosPhAt: a database of phosphorylation sites in Arabidopsis thaliana and a plant-specific phosphorylation site predictor. Nucleic Acids Res., 36, D1015-D1021.
21. Hornbeck,P.V. et al. (2004) PhosphoSite: a bioinformatics resource dedicated to physiological protein phosphorylation. Proteomics, 4, 1551-1561.
22. Hornbeck,P.V. et al. (2012) PhosphoSitePlus: a comprehensive resource for investigating the structure and function of experimentally determined posttranslational modifications in man and mouse. Nucleic Acids Res., 40, D261-D270.
23. Japkowicz,N. and Stephen,S. (2002) The class imbalance problem: a systematic study. Intell. Data Anal., 6, 429-449.
24. Kawashima,S. et al. (2008) AAindex: amino acid index database, progress report 2008.Nucleic Acids Res., 36, D202-D205.
25. Kim,S.H. and Lee,C.E. (2011) Counter-regulation mechanism of IL-4 and IFNsignal transduction through cytosolic retention of the pY-STAT6:pYSTAT2: p48 complex. Eur. J. Immunol., 41, 461-472.
26. Lee,T.Y. et al. (2011) PlantPhos: using maximal dependence decomposition to identify plant phosphorylation sites with substrate site specificity. BMC Bioinformatics, 12, 261.
27. Li,W. and Godzik,A. (2006) Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences. Bioinformatics, 22, 1658-1659.
28. Lian,I. et al. (2010) The role of YAP transcription coactivator in regulating stem cell self-renewal and differentiation. Genes Dev., 24, 1106-1118.
29. Manning,G. et al. (2002) The protein kinase complement of the human genome. Science, 298, 1912-1934.
30. Miller,M.L. et al. (2008) Linear motif atlas for phosphorylation- dependent signaling. Sci. Signal., 1, ra2.
31. Nakai,K. et al. (1988) Cluster analysis of amino acid indices for prediction of protein structure and function. Protein Eng., 2, 93-100.
32. Neuberger,G. et al. (2007) pkaPS: prediction of protein kinase A phosphorylation sites with the simplified kinase-substrate binding model. Biol. Direct., 2, 1.
33. Petersen,B. et al. (2009) A generic method for assignment of reliability scores applied to solvent accessibility predictions. BMC Struct. Biol., 9, 51.
34. Ressurreica o,M. et al. (2011) A role for p38 MAPK in the regulation of ciliary motion in a eukaryote. BMC Cell Biol., 12, 6.
35. Saha,I. et al. (2011) Fuzzy clustering of physicochemical and biochemical properties of amino Acids. Amino Acids, 43, 583-594.
36. Sing,T. et al. (2005) ROCR: visualizing classifier performance in R. Bioinformatics, 21, 3940-3941.
37. Stark,C. et al. (2010) PhosphoGRID: a database of experimentally verified in vivo protein phosphorylation sites from the budding yeast Saccharomyces cerevisiae. Database, 2010, bap026.
38. Swarbreck,D. et al. (2008) The Arabidopsis information resource (TAIR): gene structure and function annotation. Nucleic Acids Res., 36, D1009-D1014.
39. Tang,Y.R. et al. (2007) GANNPhos: a new phosphorylation site predictor based on a genetic algorithm integrated neural network. Protein Eng. Des. Sel., 20, 405-412.
40. Tomii,K. and Kanehisa,M. (1996) Analysis of amino acid indices and mutation matrices for sequence comparison and structure prediction of proteins. Protein Eng., 9, 27-36.
41. Trost,B. and Kusalik,A. (2011) Computational prediction of eukaryotic phosphorylation sites. Bioinformatics, 27, 2927-2935.
42. Uddin,S. et al. (2003) Role of Stat5 in type I interferon-signaling and transcriptional regulation. Biochem. Biophys. Res. Commun., 308, 325-330.
43. UniProt Consortium. (2008) The universal protein resource (UniProt). Nucleic Acids Res., 36, D190-D195.
44. UniProt Consortium. (2012) Reorganizing the protein space at the universal protein resource (UniProt). Nucleic Acids Res., 40, D71-D75.
45. Wang,Y.Y. et al. (2010) Hydrogen peroxide stress stimulates phosphorylation of FoxO1 in rat aortic endothelial cells. Acta. Pharmacol. Sin., 31, 160-164.
46. Witten,I.H. et al. (2011) Data Mining: Practical Machine Learning Tools and Techniques, 3rd edn. Morgan Kaufmann, Burlington, MA
47. Wood,C.D. et al. (2009) Nuclear localization of p38 MAPK in response to DNA damage. Int. J. Biol. Sci., 5, 428-437.
48. Xue,Y. et al. (2010) A summary of computational resources for protein phosphorylation. Curr. Protein Pept. Sci., 11, 485-496.
49. Zhang,J. and Johnson,G.V. (2000) Tau protein is hyperphosphorylated in a site-specific manner in apoptotic neuronal PC12 cells. J. Neurochem., 75, 2346-2357.