Prediction of motifs based on a repeated-measures model for integrating cross-species sequence and expression data

Statistical Applications in Genetics and Molecular Biology

Volumn 8, Issue 1, 2009, Pages

  Siewert, Elizabeth A ^a   Kechris, Katerina J ^a  

^a UNIVERSITY OF COLORADO   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

TRANSCRIPTION FACTOR; FUNGAL PROTEIN;

ALGORITHM; ARTICLE; BINDING SITE; COVARIANCE; GENE EXPRESSION; HEAT SHOCK; MEASUREMENT; NONHUMAN; PHYLOGENY; PREDICTION; PROTEIN BINDING; SPECIES; STATISTICAL MODEL; YEAST; CHEMISTRY; DNA SEQUENCE; GENETICS; OXIDATIVE STRESS; SACCHAROMYCES;

ALGORITHMS; BINDING SITES; FUNGAL PROTEINS; GENE EXPRESSION; LINEAR MODELS; OXIDATIVE STRESS; PHYLOGENY; SACCHAROMYCES; SEQUENCE ANALYSIS, DNA; TRANSCRIPTION FACTORS;

EID: 70350449676     PISSN: None     EISSN: 15446115     Source Type: Journal    
DOI: 10.2202/1544-6115.1464     Document Type: Article

References (65)

1. Bailey, T. L. and Elkan, C. Unsupervised learning of multiple motifs in biopolymers using expectation maximization. Machine Learning Journal, 21:51-83, 1995.
2. Barash, Y. and Friedman, N. Context-specific Bayesian clustering for gene expression data. J Comput Biol, 9(2):169-191, 2002.
3. Bergmann, S., Ihmels, J., and Barkai, N. Similarities and differences in genomewide expression data of six organisms. PLoS Biol, 2(1):E9, 2004.
4. Berman, B., Pfeiffer, B., Laverty, T., Salzberg, S., Rubin, G., Eisen, M., and Celniker, S. Computational identification of developmental enhancers: conservation and function of transcription factor binding-site clusters in Drosophila melanogaster and Drosophila pseudoobscura. Genome Biology, 5(9), 2004. ISSN 1465-6914.
5. Blanchette, M. and Tompa, M. Discovery of regulatory elements by a computational method for phylogenetic footprinting. Genome Res, 12(5):739-748, 2002.
6. Blanchette, M., Schwikowski, B., and Tompa, M. Algorithms for phylogenetic footprinting. J Comput Biol, 9(2):211-223, 2002.
7. Brazma, A., Jonassen, I., Vilo, J., and Ukkonen, E. Predicting gene regulatory elements in silico on a genomic scale. Genome Res, 8(11):1202-1215, 1998.
8. Bussemaker, H. J., Li, H., and Siggia, E. D. Regulatory element detection using correlation with expression. Nat Genet, 27(2):167-171, 2001.
9. Caselle, M., Di Cunto, F., and Provero, P. Correlating overrepresented upstream motifs to gene expression: a computational approach to regulatory element discovery in eukaryotes. BMC Bioinformatics, 3:7, 2002.
10. Cliften, P., Sudarsanam, P., Desikan, A., Fulton, L., Fulton, B., Majors, J., Waterston, R., Cohen, B. A., and Johnston, M. Finding functional features in Saccharomyces genomes by phylogenetic footprinting. Science, 301(5629):71-76, 2003.
11. Conlon, E. M., Liu, X. S., Lieb, J. D., and Liu, J. S. Integrating regulatory motif discovery and genome-wide expression analysis. Proc Natl Acad Sci U S A, 100 (6):3339-3344, 2003.
12. Das, D., Pellegrini, M., and Gray, J. W. A primer on regression methods for decoding cis-regulatory logic. PLoS Comput Biol, 5(1):e1000269, 2009.
13. Das, D., Banerjee, N., and Zhang, M. Q. Interacting models of cooperative gene regulation. Proceedings of the National Academy of Sciences, 101(46):16234-16239, 2004.
14. Das, M. K. and Dai, H. K. A survey of DNA motif finding algorithms. BMC Bioinformatics, 8 Suppl 7:S21, 2007.
15. Feng, L. and Niu, D. K. Relationship between mRNA stability and length: an old question with a new twist. Biochem Genet, 45(1-2):131-137, 2007.
16. Gasch, A. P., editor. The Environmental Stress Response: a common yeast response to environmental stresses, volume 1, Topics in Current Genetics of Yeast Stress Responses. Springer-Verlag, Heidelberg, 2002.
17. Gasch, A. P., Spellman, P. T., Kao, C. M., Carmel-Harel, O., Eisen, M. B., Storz, G., Botstein, D., and Brown, P. O. Genomic expression programs in the response of yeast cells to environmental changes. Mol. Biol. Cell, 11(12):4241-4257, 2000.
18. Hampson, S., Baldi, P., Kibler, D., and Sandmeyer, S. B. Analysis of yeast's orf upstream regions by parallel processing, microarrays, and computational methods. Proc Int Conf Intell Syst Mol Biol, 8:190-201, 2000.
19. Hampson, S., Kibler, D., and Baldi, P. Distribution patterns of over-represented k-mers in non-coding yeast DNA. Bioinformatics, 18(4):513-528, 2002.
20. Harbison, C. T., Gordon, D. B., Lee, T. I., Rinaldi, N. J., Macisaac, K. D., Danford, T. W., Hannett, N. M., Tagne, J.-B., Reynolds, D. B., Yoo, J., Jennings, E. G., Zeitlinger, J., Pokholok, D. K., Kellis, M., Rolfe, P. A., Takusagawa, K. T., Lander, E. S., Gifford, D. K., Fraenkel, E., and Young, R. A. Transcriptional regulatory code of a eukaryotic genome. Nature, 431(7004):99-104, 2004.
21. Holland, B. R., Huber, K. T., Moulton, V., and Lockhart, P. J. Using consensus networks to visualize contradictory evidence for species phylogeny. Mol Biol Evol, 21(7):1459-1461, 2004.
22. Holmes, I. and Bruno, W. J. Finding regulatory elements using joint likelihoods for sequence and expression profile data. Proc Int Conf Intell Syst Mol Biol, 8: 202-210, 2000.
23. Ihmels, J., Bergmann, S., Berman, J., and Barkai, N. Comparative gene expression analysis by differential clustering approach: application to the Candida albicans transcription program. PLoS Genet, 1(3):e39, 2005.
24. Kechris, K. and Li, H. c-REDUCE: Incorporating sequence conservation to detect motifs that correlate with expression. BMC Bioinformatics, 9 (1):506, 2008. ISSN 1471-2105. doi: 10.1186/1471-2105-9-506. URL http://www. biomedcentral.com/1471-2105/9/506.
25. Keleş, S., Van Der Laan, M., and Eisen, M. B. Identification of regulatory elements using a feature selection method. Bioinformatics, 18(9):1167-1175, 2002.
26. Keleş, S., Van Der Laan, M. J., and Vulpe, C. Regulatory motif finding by logic regression. Bioinformatics, 20(16):2799-2811, 2004.
27. Kellis, M., Patterson, N., Endrizzi, M., Birren, B., and Lander, E. S. Sequencing and comparison of yeast species to identify genes and regulatory elements. Nature, 423(6937):241-254, 2003.
28. Khaitovich, P., Weiss, G., Lachmann, M., Hellmann, I., Enard, W., Muetzel, B., Wirkner, U., Ansorge, W., and Paabo, S. A neutral model of transcriptome evolution. PLoS Biol, 2(5):E132, 2004.
29. Kim, R., Ji, H., and Wong, W. An improved distance measure between the expression profiles linking co-expression and co-regulation in mouse. BMC Bioinformatics, 7:44, 2006. ISSN 1471-2105. doi: 10.1186/1471-2105-7-44.
30. Kuramae, E. E., Robert, V., Snel, B., Weiss, M., and Boekhout, T. Phylogenomics reveal a robust fungal tree of life. FEMS Yeast Research, 6(8):1213-1220, 2006.
31. Lawrence, C. E., Altschul, S. F., Boguski, M. S., Liu, J. S., Neuwald, A. F., and Wootton, J. C. Detecting subtle sequence signals: a Gibbs sampling strategy for multiple alignment. Science, 262(5131):208-214, 1993.
32. Lefebvre, C., Aude, J. C., Glemet, E., and Neri, C. Balancing protein similarity and gene co-expression reveals new links between genetic conservation and developmental diversity in invertebrates. Bioinformatics, 21(8):1550-1558, 2005.
33. Lelandais, G., Vincens, P., Badel-Chagnon, A., Vialette, S., Jacq, C., and Hazout, S. Comparing gene expression networks in a multi-dimensional space to extract similarities and differences between organisms. Bioinformatics, 22(11):1359-1366, 2006.
34. Lemos, B., Meiklejohn, C. D., Caceres, M., and Hartl, D. L. Rates of divergence in gene expression profiles of primates, mice, and flies: stabilizing selection and variability among functional categories. Evolution, 59(1):126-137, 2005.
35. Lewin, B. Genes V. Oxford University Press, Oxford, 1994. Lu, Y., Huggins, P., and Bar-Joseph, Z. Cross species analysis of microarray expression data. Bioinformatics, 25(12):1476-1483, 2009.
36. Lu, Y., He, X., and Zhong, S. Cross-species microarray analysis with the OSCAR system suggests an INSR - Pax6 - NQO1 neuro-protective pathway in aging and Alzheimer's disease. Nucleic Acids Research, 35(Suppl. S):W105-W114, JUL 2007. ISSN 0305-1048. doi: 10.1093/nar/gkm408.
37. Martinez-Pastor, M. T., Marchler, G., Schuller, C., Marchler-Bauer, A., Ruis, H., and Estruch, F. The Saccharomyces cerevisiae zinc finger proteins Msn2p and Msn4p are required for transcriptional induction through the stress response element (stre). Embo J, 15(9):2227-2235, 1996.
38. McCarroll, S. A., Murphy, C. T., Zou, S., Pletcher, S. D., Chin, C. S., Jan, Y. N., Kenyon, C., Bargmann, C. I., and Li, H. Comparing genomic expression patterns across species identifies shared transcriptional profile in aging. Nat Genet, 36(2): 197-204, 2004.
39. Neter, J., Kutner, M. H., Nachtsheim, C. J., and Wasserman, W. Applied Linear Statistical Models, 4th Edition. McGraw-Hill, Columbus, Ohio, 1996.
40. Oleksiak, M. F., Churchill, G. A., and Crawford, D. L. Variation in gene expression within and among natural populations. Nat Genet, 32(2):261-266, 2002.
41. Rieger, T. R., Morimoto, R. I., and Hatzimanikatis, V. Mathematical modeling of the eukaryotic heat-shock response: dynamics of the hsp70 promoter. Biophys J, 88(3):1646-1658, 2005.
42. Rifkin, S. A., Houle, D., Kim, J., and White, K. P. A mutation accumulation assay reveals a broad capacity for rapid evolution of gene expression. Nature, 438 (7065):220-223, 2005.
43. Rokas, A., Williams, B. L., King, N., and Carroll, S. B. Genome-scale approaches to resolving incongruence in molecular phylogenies. Nature, 425(6960):798-804, 2003.
44. Sachs, A. B. Messenger RNA degradation in eukaryotes. Cell, 74(3):413-421, 1993.
45. Segal, E., Barash, Y., Simon, I., Friedman, N., and Koller, D. From promoter sequence to expression: A probabilistic framework. In Proc. Sixth Annual Inter. Conf. on Computational Molecular Biology (RECOMB), 2002.
46. Segal, E., Yelensky, R., and Koller, D. Genome-wide discovery of transcriptional modules from DNA sequence and gene expression. Bioinformatics, 19 Suppl 1: i273-82, 2003.
47. Shalem, O., Dahan, O., Levo, M., Martinez, M. R., Furman, I., Segal, E., and Pilpel, Y. Transient transcriptional responses to stress are generated by opposing effects of mRNA production and degradation. Mol Syst Biol, 4:223, 2008.
48. Siddharthan, R., Siggia, E. D., and van Nimwegen, E. Phylogibbs: a Gibbs sampling motif finder that incorporates phylogeny. PLoS Comput Biol, 1(7):e67, 2005.
49. Sinha, S., Blanchette, M., and Tompa, M. Phyme: a probabilistic algorithm for finding motifs in sets of orthologous sequences. BMC Bioinformatics, 5:170, 2004.
50. Speed, T., editor. Statistical Analysis of Gene Expression Microarray Data. Interdisciplinary Statistics. Chapman and Hall/CRC, Danvers, MA, 2003.
51. Stuart, J. M., Segal, E., Koller, D., and Kim, S. K. A gene-coexpression network for global discovery of conserved genetic modules. Science, 302:249-255, 2003.
52. Tagle, D. A., Koop, B. F., Goodman, M., Slightom, J. L., Hess, D. L., and Jones, R. T. Embryonic epsilon and gamma globin genes of a prosimian primate (Galago crassicaudatus). Nucleotide and amino acid sequences, developmental regulation and phylogenetic footprints. J Mol Biol, 203(2):439-455, 1988.
53. Tanay, A. Extensive low-affinity transcriptional interactions in the yeast genome. Genome Res, 16(8):962-972, 2006.
54. Tanay, A., Regev, A., and Shamir, R. Conservation and evolvability in regulatory networks: the evolution of ribosomal regulation in yeast. Proc Natl Acad Sci U S A, 102(20):7203-7208, 2005.
55. Thompson, J., Higgins, D., and Gibson, T., Clustal W: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice. Nucleic Acids Res, 22 (22):4673-4680, 1994.
56. Tirosh, I., Weinberger, A., Carmi, M., and Barkai, N. A genetic signature of interspecies variations in gene expression. Nat Genet, 38(7):830-834, 2006.
57. Tirosh, I., Bilu, Y., and Barkai, N. Comparative biology: beyond sequence analysis. Current Opinion in Biotechnology, 18(4):371-377, 2007.
58. Wang, T. and Stormo, G. D. Combining phylogenetic data with co-regulated genes to identify regulatory motifs. Bioinformatics, 19(18):2369-2380, 2003.
59. Wang, W., Cherry, J. M., Botstein, D., and Li, H. A systematic approach to reconstructing transcription networks in Saccharomyces cerevisiae. Proc Natl Acad Sci U S A, 99(26):16893-16898, 2002.
60. Wang, W., Cherry, J. M., Nochomovitz, Y., Jolly, E., Botstein, D., and Li, H. Inference of combinatorial regulation in yeast transcriptional networks: a case study of sporulation. Proc Natl Acad Sci U S A, 102(6):1998-2003, 2005.
61. Ward, L. D. and Bussemaker, H. J. Predicting functional transcription factor binding through alignment-free and affinity-based analysis of orthologous promoter sequences. Bioinformatics, 24(13):i165-171, 2008.
62. Wasserman, W. W., Palumbo, M., Thompson, W., Fickett, J. W., and Lawrence, C. E. Human-mouse genome comparisons to locate regulatory sites. Nature Genetics, 26(2):225-228, 2000.
63. Wu, R. Z., Chaivorapol, C., Zheng, J., Li, H., and Liang, S. fREDUCE: detection of degenerate regulatory elements using correlation with expression. BMC Bioinformatics, 8:399, 2007.
64. Zhang, N. R., Wildermuth, M. C., and Speed, T. P. Transcription factor binding site prediction with multivariate gene expression data. The Annals of Applied Statisitcs, 2(1):332-365, 2008.
65. Zhong,W., Zeng, P., Ma, P., Liu, J. S., and Zhu, Y. RSIR: regularized sliced inverse regression for motif discovery. Bioinformatics, 21(22):4169-4175, 2005.