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Computational Approaches in Epigenomics

Computational Approaches in Epigenomics. Guo-Cheng Yuan Department of Biostatistics and Computational Biology Dana-Farber Cancer Institute Harvard School of Public Health. BIO506, Jan 11 th , 2010. Definition.

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Computational Approaches in Epigenomics

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  1. Computational Approaches in Epigenomics Guo-Cheng Yuan Department of Biostatistics and Computational Biology Dana-Farber Cancer Institute Harvard School of Public Health BIO506, Jan 11th, 2010

  2. Definition • Epigenetics refers to changes in phenotype (appearance) or gene expression caused by mechanisms other than changes in the underlying DNA sequence. wikipedia

  3. Epigenetic mechanisms • Nucleosome positions • Histone modification • DNA methylation

  4. Chromatin • DNA is packaged into chromatin. • Nucleosome is the fundamental unit of chromatin. It wraps 146 bp DNA. • The chromatin structure is hierarchical. Felsenfeld and Groudine 2003

  5. Nucleosome and histone modification First layer chromatin structure looks like “beads-on-a-string”. A nucleosome is made of core histone proteins. The amino acids on the N-terminus of histones can be covalently modified. Felsenfeld and Groudine 2003

  6. DNA methylation DNA methylation normally occurs at CpG dinucleotide only and can be inherited during cell-division. Alberts et al. Molecular Biology of the Cell

  7. Why do we care? • Epigenetics is an extra layer of transcriptional control. • Epigenetics plays an important role in development. • Epigenetic mechanisms can cause cancer and other diseases. • Epigenetic patterns are reversible and can be influenced by environments.

  8. Our goals Characterize cell-type specific epigenetic states epigenonic data TF binding Elucidate epigenetic targeting mechanism Computational model microarray DNA sequence Understand epigenetic regulation in cell differentiation … Epigenetic signature of diseases

  9. Chromatin domains chromatin loops Intrachromosomal interactions large-scale histone modification patterns

  10. A hidden Markov model for prediction of multi-gene chromatin domains Jessica Larson

  11. Prediction results

  12. Targeting mechanism for epigenetic factors Nucleosome positions Histone modification pattern

  13. Dinucleotide Frequency Signal Wavelet Basis Signal Decomposition E1 E2 E3 An N-score model to prediction nucleosome positions Wavelet Energy Yuan and Liu

  14. N-score prediction in two yeast species Lanterman et al.

  15. Polycomb targets developmental genes in ES expressed Oct4 Nanog Sox2 Polycomb repressed Kim et al. 2008 Boyer et al. 2006

  16. A computational model: BART BART is a Bayesian average of regression trees Motif A Motif B Motif C NO YES NO YES NO YES Chipman et al. 2007

  17. Propensity score Number of cell-types in which the gene is targeted Overall prediction accuracy testing data ROC AUC = 0.82 all factors 5 factors CpG random Spring Liu; Zhen Shao

  18. + Hox Polycomb + Hox Dnmt1 cell-type B cell-type A An integrated network TF network Jess Mar

  19. Future directions • How do genetic and epigenetic factors work together to regulate cell-type specific gene expression? • How does the integrated regulatory network change across cell-types? • Are there epigenetic signatures associated with common diseases and if so what role do they have?

  20. Acknowledgment • Jessica Larson • Yingchun (Spring) Liu • Zhen Shao • John Quackenbush Lab • Jess Mar • Stuart Orkin Lab • Xiaohua Shen • Jongwan Kim • Steve Altschuler • Ollie Rando • Jun Liu • Claudia Adams Barr Program

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