A stationary wavelet entropy-based clustering approach accurately predicts gene expression

Journal of Computational Biology

Volumn 22, Issue 3, 2015, Pages 236-249

  Nguyen, Nha ^a   Vo, An ^b   Choi, Inchan ^a,c   Won, Kyoung Jae ^a  

^a UNIVERSITY OF PENNSYLVANIA   (United States)

^b FEINSTEIN INSTITUTE FOR MEDICAL RESEARCH   (United States)

^c Metabolic Engineering Division Department of Agricultural Biotechnology National Academy of Agricutural Science   (South Korea)

Author keywords

algorithms; gene expression; genetic analysis; genome analysis; next generation sequencing

Indexed keywords

3T3 CELL LINE; ALGORITHM; ANIMAL; BIOLOGICAL MODEL; CLUSTER ANALYSIS; ENTROPY; GENE EXPRESSION; GENETIC EPIGENESIS; MOUSE; MULTIGENE FAMILY; PROMOTER REGION; WAVELET ANALYSIS;

3T3-L1 CELLS; ALGORITHMS; ANIMALS; CLUSTER ANALYSIS; ENTROPY; EPIGENESIS, GENETIC; GENE EXPRESSION; MICE; MODELS, GENETIC; MULTIGENE FAMILY; PROMOTER REGIONS, GENETIC; WAVELET ANALYSIS;

EID: 84924763000     PISSN: 10665277     EISSN: None     Source Type: Journal    
DOI: 10.1089/cmb.2014.0221     Document Type: Article

References (58)

1. Agarwal, S., Verma, A.K., and Singh, P. 2013. Content based image retrieval using discrete wavelet transform and edge histogram descriptor. 2013 International Conference on Information Systems and Computer Networks (ISCON), pp. 19-23.
2. Barski, A., Cuddapah, S., Cui, K., et al. 2007. High-resolution profiling of histone methylations in the human genome. Cell 129, 823-837.
3. Baylin, S.B., and Jones, P.A. 2011. A decade of exploring the cancer epigenome-biological and translational implications. Nat. Rev. Cancer 11, 726-734.
4. Bernstein, B.E., Meissner, A., and Lander, E.S. 2007. The mammalian epigenome. Cell 128, 669-681.
5. Bernstein, B.E., Mikkelsen, T.S., Xie, X., et al. 2006. A bivalent chromatin structure marks key developmental genes in embryonic stem cells. Cell 125, 315-326.
6. Bernstein, B.E., Stamatoyannopoulos, J.A., Costello, J.F., et al. 2010. The NIH Roadmap Epigenomics Mapping Consortium. Nat. Biotechnol. 28, 1045-1048.
7. Creyghton, M.P., Cheng, A.W., Welstead, G.G., et al. 2010. Histone H3K27ac separates active from poised enhancers and predicts developmental state. Proc. Natl. Acad. Sci. USA 107, 21931-21936.
8. Daily, K., Rigor, P., Christley, S., et al. 2010. Data structures and compression algorithms for high-throughput sequencing technologies. BMC Bioinform. 11, 514.
9. Demirel, H., and Anbarjafari, G. 2011. IMAGE resolution enhancement by using discrete and stationary wavelet decomposition. IEEE Trans. Image Process. 20, 1458-1460.
10. Deng, J., Ban, Y.F., Liu, J.S., et al. 2014. Hierarchical segmentation of multitemporal RADARSAT-2 SAR data using stationary wavelet transform and algebraic multigrid method. IEEE Trans. Geosci. Remote Sens. 52, 4353-4363.
11. Dong, X., Greven, M.C., Kundaje, A., et al. 2012. Modeling gene expression using chromatin features in various cellular contexts. Genome Biol. 13, R53.
12. Ernst, J., Kheradpour, P., Mikkelsen, T.S., et al. 2011. Mapping and analysis of chromatin state dynamics in nine human cell types. Nature 473, 43-49.
13. Ernst, J., Plasterer, H.L., Simon, I., et al. 2010. Integrating multiple evidence sources to predict transcription factor binding in the human genome. Genome Res. 20, 526-536.
14. Guenther, M.G., Levine, S.S., Boyer, L.A., et al. 2007. A chromatin landmark and transcription initiation at most promoters in human cells. Cell 130, 77-88.
15. Harrow, J., Frankish, A., Gonzalez, J.M., et al. 2012. GENCODE: the reference human genome annotation for The ENCODE Project. Genome Res. 22, 1760-1774.
16. Heintzman, N.D., Hon, G.C., Hawkins, R.D., et al. 2009. Histone modifications at human enhancers reflect global celltype-specific gene expression. Nature. 459, 108-112.
17. Heintzman, N.D., Stuart, R.K., Hon, G., et al. 2007. Distinct and predictive chromatin signatures of transcriptional promoters and enhancers in the human genome. Nat. Genet. 39, 311-318.
18. Huber, W., von Heydebreck, A., Sultmann, H., et al. 2002. Variance stabilization applied to microarray data calibration and to the quantification of differential expression. Bioinformatics 18 Suppl 1, S96-S104.
19. Jones, P.A., and Martienssen, R. 2005. A blueprint for a Human Epigenome Project: the AACR Human Epigenome Workshop. Cancer Res. 65, 11241-11246.
20. Karlic, R., Chung, H.R., Lasserre, J., et al. 2010. Histone modification levels are predictive for gene expression. Proc. Natl. Acad. Sci. USA 107, 2926-2931.
21. Kouzarides, T. 2007. Chromatin modifications and their function. Cell 128, 693-705.
22. Kumar, V., Muratani, M., Rayan, N.A., et al. 2013. Uniform, optimal signal processing of mapped deep-sequencing data. Nat. Biotechnol. 31, 615-622.
23. Laird, P.W. 2003. The power and the promise of DNA methylation markers. Nat. Rev. Cancer 3, 253-266.
24. Li, H., Zhang, K., and Jiang, T. 2004. Minimum entropy clustering and applications to gene expression analysis. Proceedings of the IEEE Computational Systems Bioinformatics Conference, pp. 142-151.
25. Li, Y., Daniel, M., and Tollefsbol, T.O. 2011. Epigenetic regulation of caloric restriction in aging. BMC Med. 9, 98.
26. Lister, R., Pelizzola, M., Dowen, R.H., et al. 2009. Human DNA methylomes at base resolution show widespread epigenomic differences. Nature 462, 315-322.
27. Liu, L., van Groen, T., Kadish, I., et al. 2011. Insufficient DNA methylation affects healthy aging and promotes agerelated health problems. Clin. Epigenet. 2, 349-360.
28. Luco, R.F., and Misteli, T. 2011. More than a splicing code: integrating the role of RNA, chromatin and non-coding RNA in alternative splicing regulation. Curr. Opin. Genet. Dev. 21, 366-372.
29. Luco, R.F., Pan, Q., Tominaga, K., et al. 2010. Regulation of alternative splicing by histone modifications. Science 327, 996-1000.
30. Mallat, S.G. 2009. A Wavelet Tour of Signal Processing: The Sparse Way. Elsevier/Academic Press, Amsterdam.
31. Meissner, A. 2010. Epigenetic modifications in pluripotent and differentiated cells. Nat. Biotechnol. 28, 1079-1088.
32. Menayo, R., Encarnacion, A., Gea, G.M., et al. 2014. Sample entropy-based analysis of differential and traditional training effects on dynamic balance in healthy people. J. Motor Behav. 46, 73-82.
33. Micsinai, M., Parisi, F., Strino, F., et al. 2012. Picking ChIP-seq peak detectors for analyzing chromatin modification experiments. Nucleic Acids Res. 40, e70.
34. Mikkelsen, T.S., Ku, M., Jaffe, D.B., et al. 2007. Genome-wide maps of chromatin state in pluripotent and lineagecommitted cells. Nature 448, 553-560.
35. Mikkelsen, T.S., Xu, Z., Zhang, X., et al. 2010. Comparative epigenomic analysis of murine and human adipogenesis. Cell 143, 156-169.
36. Nguyen, N., Huang, H., Oraintara, S., et al. 2010. Mass spectrometry data processing using zero-crossing lines in multiscale of Gaussian derivative wavelet. Bioinformatics 26, i659-i665.
37. Nguyen, N., Vo, A., and Won, K.J. 2014a. A wavelet-based method to exploit epigenomic language in the regulatory region. Bioinformatics. 30, 908-914.
38. Nguyen, N., Vo, A., and Won, K.J. 2014b. A wavelet approach to detect enriched regions and explore epigenomic landscapes. J. Comput. Biol. Accepted.
39. Rada-Iglesias, A., and Wysocka, J. 2011. Epigenomics of human embryonic stem cells and induced pluripotent stem cells: insights into pluripotency and implications for disease. Genome Med. 3, 36.
40. Rajagopal, N., Xie, W., Li, Y., et al. 2013. RFECS: a random-forest based algorithm for enhancer identification from chromatin state. PLoS Comput. Biol. 9, e1002968.
41. Rashid, N.U., Giresi, P.G., Ibrahim, J.G., et al. 2011. ZINBA integrates local covariates with DNA-seq data to identify broad and narrow regions of enrichment, even within amplified genomic regions. Genome Biol. 12, R67.
42. Santoni, F.A. 2013. EMdeCODE: a novel algorithm capable of reading words of epigenetic code to predict enhancers and retroviral integration sites and to identify H3R2me1 as a distinctive mark of coding versus non-coding genes. Nucleic Acids Res. 41, e48
43. Shannon, C.E. 1948. A mathematical theory of communication. Bell Syst. Tech. J. 27, 379-423.
44. Shin, J.H., Park, C.H., Yang, Y.J., et al. 2007. Entropy-based analysis of the non-linear relationship between gene expression profiles of amplified and non-amplified RNA. Int. J. Mol. Med. 20, 905-912.
45. Song, Q., and Smith, A.D. 2011. Identifying dispersed epigenomic domains from ChIP-Seq data. Bioinformatics 27, 870-871.
46. Storey, J.D. 2002. A direct approach to false discovery rates. J. R. Stat. Soc. Ser. B Stat. Methodol. 64, 479-498.
47. Sun, H., Wu, J., Wickramasinghe, P., et al. 2011. Genome-wide mapping of RNA Pol-II promoter usage in mouse tissues by ChIP-seq. Nucleic acids research 39, 190-201.
48. Swartz, J.B., Rothenberg, S.J., Teklehaimanot, S., et al. 1999. Comparison of the entropy technique with two other techniques for detecting disease clustering using data from children with high blood lead levels. Am. J. Epidemiol. 149, 750-760.
49. Szekely, G.J., and Rizzo, M.L. 2005. Hierarchical clustering via joint between-within distances: Extending Ward's minimum variance method. J. Classification 22, 151-183.
50. Ucar, D., Hu, Q., and Tan, K. 2011. Combinatorial chromatin modification patterns in the human genome revealed by subspace clustering. Nucleic Acids Res. 39, 4063-4075.
51. Wang, X.H., Istepanian, R.S.H., and Song, Y.H. 2003. Microarray image enhancement by denoising using stationary wavelet transform. IEEE Trans. Nanobiosci. 2, 184-189.
52. Won, K.J., Chepelev, I., Ren, B., et al. 2008. Prediction of regulatory elements in mammalian genomes using chromatin signatures. BMC Bioinform. 9, 547.
53. Won, K.J., Zhang, X., Wang, T., et al. 2013. Comparative annotation of functional regions in the human genome using epigenomic data. Nucleic Acids Res. 41, 4423-4432.
54. Xie, R., Everett, L.J., Lim, H.W., et al. 2013. Dynamic chromatin remodeling mediated by polycomb proteins orchestrates pancreatic differentiation of human embryonic stem cells. Cell Stem Cell 12, 224-237.
55. Yogesan, K., Jorgensen, T., Albregtsen, F., et al. 1996. Entropy-based texture analysis of chromatin structure in advanced prostate cancer. Cytometry 24, 268-276.
56. Yu, P., Xiao, S., Xin, X., et al. 2013. Spatiotemporal clustering of the epigenome reveals rules of dynamic gene regulation. Genome Res. 23, 352-364.
57. Zang, C., Schones, D.E., Zeng, C., et al. 2009. A clustering approach for identification of enriched domains from histone modification ChIP-Seq data. Bioinformatics 25, 1952-1958.
58. Zhang, Y., Liu, H., Lv, J., et al. 2011. QDMR: a quantitative method for identification of differentially methylated regions by entropy. Nucleic Acids Res. 39, e58.