Mining Cytochrome b561 proteins from plant genomes

International Journal of Bioinformatics Research and Applications

Volumn 6, Issue 2, 2010, Pages 209-221

  Opiyo, Stephen O ^a   Moriyama, Etsuko N ^b  

^a UNIVERSITY OF NEBRASKA   (United States)

^b UNIVERSITY OF NEBRASKA LINCOLN   (United States)

Author keywords

Cytochrome b561; partial least squares; PLS; profile hidden Markov model; PSI BLAST; support vector machines; SVMs

Indexed keywords

CYTOCHROME B; CYTOCHROME B561; VEGETABLE PROTEIN;

ARTICLE; CHEMISTRY; DATA MINING; GENETICS; METHODOLOGY; PLANT GENOME; PROTEIN DATABASE;

CYTOCHROME B GROUP; DATA MINING; DATABASES, PROTEIN; GENOME, PLANT; PLANT PROTEINS;

EID: 77953630985     PISSN: 17445485     EISSN: 17445493     Source Type: Journal    
DOI: 10.1504/IJBRA.2010.032122     Document Type: Article

References (29)

1. Altschul, S.F., Madden, T.L., Schaffer, A.A., Zhang, J., Zhang, Z., Miller, W. and Lipman, D.J. (1997) ‘Gapped BLAST and PSI-BLAST: a new generation of protein database search programs’, Nucleic Acids Res., Vol. 25, pp.3389–3402.
2. Andreeva, A., Howorth, D., Brenner, S.E., Hubbard, T.J.P., Chothia, C. and Murzin, A.G. (2004) ‘SCOP database in 2004: refinements integrate structure and sequence family data’, Nucleic Acids Res., Vol. 32, pp.D226–D229.
3. Asard, H., Kappila, J. and Brczi, W. (2001) ‘Higher-plant plasma membrane Cytochrome b561: a protein in search of a function’, Protoplasma, Vol. 217, pp.77–93.
4. Bateman, A., Coin, L., Durbin, R., Finn, R.D., Hollich, V., Griffiths-Jones, S.N., Khanna, A., Marshall, M., Moxon, S., Sonnhammer, E.L.L., Studholme, D.J., Yeats, C. and Eddy, S.R. (2004) ‘The Pfam protein families database’, Nucleic Acids Res., Vol. 32, pp.D138–D141.
5. Bhasin, M. and Raghava, G.P.S. (2005) ‘GPCRsclass: A web tool for the classification of amine type of G-protein-coupled receptors’, Nucleic Acids Res., Vol. 33, pp.W143–W147.
6. Boeckmann, B., Bairoch, A., Apweiler, R., Blatter, M.C., Estreicher, A., Gasteiger, E., Martin, M.J., Michoud, K., O’Donovan, C., Phan, I., Pilbout, S.N. and Schneider, M. (2003) ‘The SWISS-PROT protein knowledgebase and its supplement TrEMBL in 2003’, Nucleic Acids Res., Vol. 31, pp.365–370.
7. Cheng, B.Y., Carbonell, J.G. and Klein-Seetharaman, J. (2005) ‘Protein classification based on text document classification techniques’, Proteins, Vol. 58, pp.955–970.
8. Cristianini, N. and Shawe-Taylor, J. (2000) An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods, Cambridge University Press, Cambridge.
9. Durbin, R., Eddy, S., Krogh, A. and Mitchison, G. (1998) Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids, Cambridge University Press, Cambridge.
10. Geladi, P. and Kowalski, B.R. (1986) ‘Partial least squares regression: a tutorial’, Anal. Chim. Acta., Vol. 185, pp.1–17.
11. Hughey, R. and Krogh, A. (1996) ‘Hidden Markov models for sequence analysis: extension and analysis of the basic method’, Comp. Appl. Biosci., Vol. 12, pp.95–107.
12. Karchin, R., Karplus, K. and Haussler, D. (2002) ‘Classifying G-protein coupled receptors with support vector machines’, Bioinformatics, Vol. 18, pp.147–159.
13. Kazutaka, K. and Hiroyuki, T. (2008) ‘Recent development in the MAFFT multiple sequence alignment program’, Briefings in Bioinformatics, Vol. 9, pp.286–298.
14. Kim, J., Moriyama, E.N., Warr, C.G., Clyne, P.J. and Carlson, J.R. (2000) ‘Identification of novel multi-transmembrane proteins from genomic databases using quasi-periodic structural properties’, Bioinformatics, Vol. 16, pp.767–775.
15. Leslie, C.S., Eskin, E., Cohen, A., Weston, J. and Noble, W.S. (2004) ‘Mismatch string kernels for discriminative protein classification’, Bioinformatics, Vol. 20, pp.467–476.
16. Liao, L. and Noble, W.S. (2003) ‘Combining pairwise sequence similarity and support vector machines for detecting remote protein evolutionary and structural relationships’, J. Comput. Biol., Vol. 10, pp.857–868.
17. Moriyama, E.N. and Kim, J. (2005) ‘Protein family classification with discriminant function analysis’, in Gustafson, J.P., Shoemaker, R. and Snape, J.W. (Eds.): Genome Exploitation: Data Mining the Genome, Springer, New York, pp.121–132.
18. Mulder, N.J., Apweiler, R., Attwood, T.K., Bairoch, A., Bateman, A., Binns, D., Bradley, P., Bork, P., Bucher, P., Cerutti, L., Copley, R., Courcelle, E., Das, U., Durbin, R., Fleischmann, W., Gough, J., Haft, D., Harte, N., Hulo, N., Kahn, D., Kanapin, A., Krestyaninova, M., Lonsdale, D., Lopez, R., Letunic, I., Madera, M., Maslen, J., McDowall, J., Mitchell, A., Nikolskaya, N.A., Orchard, S., Pagni, M., Ponting, C.P., Quevillon, E., Selengut, J., Sigrist, C.J.A., Silventoinen, V., Studholme, D.J., Vaughan, R. and Wu, C.H. (2005) ‘InterPro, progress and status in 2005’, Nucleic Acids Res., Vol. 33, pp.D201–D205.
19. Nanasato, Y., Akasi, K. and Yokata, A. (2005) ‘Co-expression of Cytochrome b561 and ascorbate oxidase in leaves of wild Watermelon under drought and high light conditions’, Plant Cell Phisiol., Vol. 46, pp.1515–1524.
20. Opiyo, S.O. and Moriyama, E.N. (2007) ‘Protein family classification by partial least squares’, J. Proteome Res., Vol. 6, pp.846–853.
21. Opiyo, S.O. and Moriyama, E.N. (2009) ‘Mining the Arabidopsis and rice genomes for cyclophlin protein families’, Int. J. Bioinformatics Research and Applications, Vol. 5, pp.295–309.
22. Otaki, J.M., Mori, A., Itoh, Y., Nakayama, T. and Yamamoto, H. (2006) ‘Alignment free classification of G-protein-coupled receptors using self-organizing maps’, J. Chem. Inf. Model., Vol. 46, pp.1479–1490.
23. Ponting, C.P. (2001) ‘Domain homologues of dopamine β-hydroxylase and ferric reductase: roles for iron metabolism in neurodegenerative disorders’, Human Mol Gen., Vol. 10, pp.1853–1858.
24. R Development Core Team (2007) R: A Language and Environment for Statistical Computing; R Foundation for Statistical Computing, Vienna, Austria, http://www.R-project.org
25. Strope, P.K. and Moriyama, E.N. (2007) ‘Simple alignment-free methods for protein classification: A case study from G-protein coupled receptors’, Genomics, Vol. 89, pp.602–612.
26. Vapnik, V.N. (1999) The Nature of Statistical Learning Theory, Springer-Verlag, New York.
27. Verelst, W. and Asard, H. (2001) ‘Analysis of an Arabidopsis thaliana protein family, structurally related to Cytochrome b561 and potentially involved in catecholamine biochemistry’, Journal of Plant Physiology, Vol. 161, pp.175–181.
28. Verelst, W. and Asard, H. (2003) ‘A phylogenetic study of Cytochrome b561 proteins’, Genome Biology, Vol. 4, p.R38.
29. Wehrens, R. and Mevik, B. (2006) pls: Partial Least Squares Regression (PLSR) and Principal Component Regression (PCR), R package version 1.2–1, http://mevik.net/work/software/pls.html