BMICS 776 wwwbiostatwiscedubmi776 Spring 2019 Colin Dewey colindeweywiscedu These slides excluding thirdparty material are licensed under CC BYNC 40 by Anthony Gitter Mark Craven and Colin Dewey ID: 1026228
Download Presentation The PPT/PDF document "Introduction to Epigenetics" is the property of its rightful owner. Permission is granted to download and print the materials on this web site for personal, non-commercial use only, and to display it on your personal computer provided you do not modify the materials and that you retain all copyright notices contained in the materials. By downloading content from our website, you accept the terms of this agreement.
1. Introduction to EpigeneticsBMI/CS 776 www.biostat.wisc.edu/bmi776/Spring 2019Colin Deweycolin.dewey@wisc.eduThese slides, excluding third-party material, are licensed under CC BY-NC 4.0 by Anthony Gitter, Mark Craven, and Colin Dewey
2. Goals for lecture2Key conceptsImportance of epigenetic data for understanding transcriptional regulationUse of epigenetic data for predicting transcription factor binding sites
3. Defining epigenetics3Formally: attributes that are “in addition to” genetic sequence or sequence modificationsInformally: experiments that reveal the context of DNA sequenceDNA has multiple states and modificationsG T G C G T T A C TATACGHistonesG A C T A G T G C G T T A C Tvs.modificationinaccessible
4. Importance of epigenetics4Better understandDNA binding and transcriptional regulationDifferences between cell and tissue typesDevelopment and other important processesNon-coding genetic variants (next lecture)
5. PWMs are not enough5Genome-wide motif scanning is impreciseTranscription factors (TFs) bind < 5% of their motif matchesSame motif matches in all cells and conditions
6. PWMs are not enough6DNA looping can bring distant binding sites close to transcription start sitesWhich genes does an enhancer regulate?Nature Education 2010Enhancer: DNA binding site for TFs, can be far from affected genePromoter: DNA binding site for TFs, close to genetranscription start site
7. Mapping regulatory elements genome-wide7Can do much better than motif scanning with additional dataChIP-seq measures binding sites for one TF at a timeEpigenetic data suggests where some TF bindsShlyueva Nature Reviews Genetics 2014
8. DNase I hypersensitivity8Regulatory proteins bind accessible DNADNase I enzyme cuts open chromatin regions that are not protected by nucleosomesWang PLoS ONE 2012Nucleosome: DNA wrapped around histone proteins
9. Mark particular regulatory configurationsH3 (protein) K27 (amino acid) ac (modification)Histone modifications9Latham Nature Structural & Molecular Biology 2007; Katie Ris-VicariShlyueva Nature Reviews Genetics 2014Two copies of histone proteins H2A, H2B, H3, H4
10. Reversible DNA modificationRepresses gene expressionDNA methylation10OpenStax CNX
11. Algorithms to predict long range enhancer-promoter interactionsOr measure with chromosome conformation capture (3C, Hi-C, etc.)3d organization of chromatin11Rao Cell 2014
12. Hi-C produces 2d chromatin contact mapsLearn domains, enhancer-promoter interactions3d organization of chromatin12Rao Cell 2014500 kb50 kb5 kb
13. Large-scale epigenetic maps13Epigenomes are condition-specificRoadmap Epigenomics Consortium and ENCODE surveyed over 100 types of cells and tissuesRoadmap Epigenomics Consortium Nature 2015
14. Genome annotation14Combinations of epigenetic signals can predict functional stateChromHMM: Hidden Markov modelSegway: Dynamic Bayesian networkRoadmap Epigenomics Consortium Nature 2015
15. Genome annotation15States are more interpretable than raw dataErnst and Kellis Nature Methods 2012
16. Predicting TF binding with DNase-Seq16
17. DNase I hypersensitive sites17Arrows indicate DNase I cleavage sitesObtain short reads that we map to the genomeWang PLoS ONE 2012
18. DNase I footprints18Distribution of mapped reads is informative of open chromatin and specific TF binding sitesRead depth at each positionIChIP-Seq peakNucleosome free “open” chromatinNeph Nature 2012Zoom inTF binding prevents DNase cleavage leaving Dnase I “footprint”, only consider 5′ end
19. DNase I footprints to TF binding predictions19DNase footprints suggest that some TF binds that locationWe want to know which TF binds that locationTwo ideas:Search for DNase footprint patterns, then match TF motifsSearch for motif matches in genome, then model proximal DNase-Seq readsWe’ll consider this approach