RF-DYMHC: Detecting the yeast meiotic recombination hotspots and coldspots by random forest model using gapped dinucleotide composition features

Nucleic Acids Research

Volumn 35, Issue SUPPL.2, 2007, Pages

  Jiang, Peng ^a   Wu, Haonan ^a   Wei, Jiawei ^a   Sang, Fei ^a   Sun, Xiao ^a   Lu, Zuhong ^a  

^a SOUTHEAST UNIVERSITY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

NUCLEOTIDE;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; BIOLOGY; CHROMOSOME MAP; CODON; COMPUTER PROGRAM; CYTOLOGY; DNA SEQUENCE; GENETIC RECOMBINATION; GENETICS; GENOME; MEIOSIS; METHODOLOGY; SACCHAROMYCES CEREVISIAE;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; CHROMOSOME MAPPING; CODON; COMPUTATIONAL BIOLOGY; GENOME, FUNGAL; MEIOSIS; NUCLEOTIDES; PATTERN RECOGNITION, AUTOMATED; RECOMBINATION, GENETIC; SACCHAROMYCES CEREVISIAE; SEQUENCE ANALYSIS, DNA; SOFTWARE;

EID: 34547567090     PISSN: 03051048     EISSN: 13624962     Source Type: Journal    
DOI: 10.1093/nar/gkm217     Document Type: Article

References (62)

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2. Zenvirth, D., Arbel, T., Sherman, A., Goldway, M., Klein, S. and Simchen, G. (1992) Multiple sites for double-strand breaks in whole meiotic chromosomes of Saccharomyces cerevisiae. EMBO J., 11, 3441-3447.
3. Klein, S., Zenvirth, D., Dror, V., Barton, A.B., Kaback, D.B. and Simchen, G. (1996) Patterns of meiotic double-strand breakage on native and artificial yeast chromosomes. Chromosoma, 105, 276-284.
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7. Fullerton, S.M., Bernardo Carvalho, A. and Clark, A.G. (2001) Local rates of recombination are positively correlated with GC content in the human genome. Mol. Biol. Evol., 18, 1139-1142.
8. Kliman, R.M. and Hey, J. (1993) Reduced natural selection associated with low recombination in Drosophila melanogaster. Mol. Biol. Evol., 10, 1239-1258.
9. Kliman, R.M., Irving, N. and Santiago, M. (2003) Selection conflicts, gene expression, and codon usage trends in yeast. J. Mol. Evol., 57, 98-109.
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12. Perry, J. and Ashworth, A. (1999) Evolutionary rate of a gene affected by chromosomal position. Curr. Biol., 9, 987-989.
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25. Matthews, B.W. (1975) Comparison of the predicted and observed secondary structure of T4 phage lysozyme. Biochim. Biophys. Acta., 405, 442-451.
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27. Bhasin, M., Reinherz, E.L. and Reche, P.A. (2006) Recognition and classification of histones using support vector machine. J. Comput. Biol., 13, 102-112.
28. Lin, H.H., Han, L.Y., Cai, C.Z., Ji, Z.L. and Chen, Y.Z. (2006) Prediction of transporter family from protein sequence by support vector machine approach. Proteins, 62, 218-231.
29. Yu, X., Cao, J., Cai, Y., Shi, T. and Li, Y. (2006) Predicting rRNA-, RNA-, and DNA-binding proteins from primary structure with support vector machines. J. Theor. Biol., 240, 175-184.
30. Cai, C.Z., Wang, W.L., Sun, L.Z. and Chen, Y.Z. (2003) Protein function classification via support vector machine approach. Math. Biosci., 185, 111-122.
31. Cai, Y.D., Liu, X.J., Li, Y.X., Xu, X.B. and Chou, K.C. (2003) Prediction of beta-turns with learning machines. Peptides, 24, 665-669.
32. Lichten, M. and Goldman, A.S. (1995) Meiotic recombination hotspots. Annu. Rev. Genet., 29, 423-444.
33. Zenvirth, D., Arbel, T., Sherman, A., Goldway, M., Klein, S. and Simchen, G. (1992) Multiple sites for double-strand breaks in whole meiotic chromosomes of Saccharomyces cerevisiae. EMBO J., 11, 3441-3447.
34. Klein, S., Zenvirth, D., Dror, V., Barton, A.B., Kaback, D.B. and Simchen, G. (1996) Patterns of meiotic double-strand breakage on native and artificial yeast chromosomes. Chromosoma, 105, 276-284.
35. Fearnhead, P. and Smith, N.G. (2005) A novel method with improved power to detect recombination hotspots from polymorphism data reveals multiple hotspots in human genes. Am. J. Hum. Genet., 77, 781-794.
36. Fearnhead, P. and Donnelly, P. (2001) Estimating recombination rates from population genetic data. Genetics, 159, 1299-1318.
37. Stumpf, M.P. and McVean, G.A. (2003) Estimating recombination rates from population-genetic data. Nat. Rev. Genet., 4, 959-968.
38. Fullerton, S.M., Bernardo Carvalho, A. and Clark, A.G. (2001) Local rates of recombination are positively correlated with GC content in the human genome. Mol. Biol. Evol., 18, 1139-1142.
39. Kliman, R.M. and Hey, J. (1993) Reduced natural selection associated with low recombination in Drosophila melanogaster. Mol. Biol. Evol., 10, 1239-1258.
40. Kliman, R.M., Irving, N. and Santiago, M. (2003) Selection conflicts, gene expression, and codon usage trends in yeast. J. Mol. Evol., 57, 98-109.
41. Marais, G., Mouchiroud, D. and Duret, L. (2001) Does recombination improve selection on codon usage? Lessons from nematode and fly complete genomes. Proc. Natl Acad. Sci. USA, 98, 5688-5692.
42. Marais, G. and Piganeau, G. (2002) Hill-Robertson interference is a minor determinant of variations in codon bias across Drosophila melanogaster and Caenorhabditis elegans genomes. Mol. Biol. Evol., 19, 1399-1406.
43. Perry, J. and Ashworth, A. (1999) Evolutionary rate of a gene affected by chromosomal position. Curr. Biol., 9, 987-989.
44. Zhou, T., Weng, J., Sun, X. and Lu, Z. (2006) Support vector machine for classification of meiotic recombination hotspots and coldspots in Saccharomyces cerevisiae based on codon composition. BMC Bioinformatics, 7, 223.
45. Gerton, J.L., DeRisi, J., Shroff, R., Lichten, M., Brown, P.O. and Petes, T.D. (2000) Inaugural article: global mapping of meiotic recombination hotspots and coldspots in the yeast Saccharomyces cerevisiae. Proc. Natl Acad. Sci. USA, 97, 11383-11390.
46. Cherry, J.M., Ball, C., Weng, S., Juvik, G., Schmidt, R., Adler, C., Dunn, B., Dwight, S., Riles, L. et al. (1997) Genetic and physical maps of Saccharomyces cerevisiae. Nature, 387, 67-73.
47. Park, K.J. and Kanehisa, M. (2003) Prediction of protein subcellular locations by support vector machines using compositions of amino acids and amino acid pairs. Bioinformatics, 19, 1656-1663.
48. Breiman, L. (2001) Random forest. Mach. Learning, 45, 5-32.
49. Breiman, L. (1996) Bagging predictors. Mach. Learning, 24, 123-14.
50. Svetnik, V., Liaw, A., Tong, C., Culberson, J.C., Sheridan, R.P. and Feuston, B.P. (2003) Random forest: a classification and regression tool for compound classification and QSAR modeling. J. Chem. Inf. Comput. Sci., 43, 1947-1958.
51. Diaz-Uriarte, R. and Alvarez de Andres, S. (2006) Gene selection and classification of microarray data using random forest. BMC Bioinformatics, 7, 3.
52. Liaw, A. and Wiener, M. (2002) Classification and regression by randomForest. R News, 2, 18-22.
53. Vapnik, V. (1998) Statistical Learning Theory Wiley, NY, USA.
54. Dimitriadou, E., Hornik, K., Leisch, F., Meyer, D. and Weingessel, A. (2006) e1071: Misc Functions of the Department of Statistics (e1071), TU Wien.
55. Hua, S. and Sun, Z. (2001) Support vector machine approach for protein subcellular localization prediction. Bioinformatics, 17, 721-728.
56. Matthews, B.W. (1975) Comparison of the predicted and observed secondary structure of T4 phage lysozyme. Biochim. Biophys. Acta., 405, 442-451.
57. Bhasin, M. and Raghava, G.P. (2004) ESLpred: SVM-based method for subcellular localization of eukaryotic proteins using dipeptide composition and PSI-BLAST. Nucleic Acids Res., 32, W414-W419.
58. Bhasin, M., Reinherz, E.L. and Reche, P.A. (2006) Recognition and classification of histones using support vector machine. J. Comput. Biol., 13, 102-112.
59. Lin, H.H., Han, L.Y., Cai, C.Z., Ji, Z.L. and Chen, Y.Z. (2006) Prediction of transporter family from protein sequence by support vector machine approach. Proteins, 62, 218-231.
60. Yu, X., Cao, J., Cai, Y., Shi, T. and Li, Y. (2006) Predicting rRNA-, RNA-, and DNA-binding proteins from primary structure with support vector machines. J. Theor. Biol., 240, 175-184.
61. Cai, C.Z., Wang, W.L., Sun, L.Z. and Chen, Y.Z. (2003) Protein function classification via support vector machine approach. Math. Biosci., 185, 111-122.
62. Cai, Y.D., Liu, X.J., Li, Y.X., Xu, X.B. and Chou, K.C. (2003) Prediction of beta-turns with learning machines. Peptides, 24, 665-669.