“An integrated encyclopedia of DNA elements in the human genome”

1. “An integrated encyclopedia of DNA elements in the human genome” ENCODE Project Consortium. Nature 2012 Sep 6; 489:57-74. Michael M. Hoffman University of Washington 12 September 2012

2. Major results • 80% of genome shows biochemical activity • ENCODE elements under purifying selection • We can predict RNA using chromatin data • Segmentation of genome into labels • Noncoding variants often in ENCODE elements • Disease phenotypes from GWAS can be associated with a cell type or transcription factor

3. Biochemical activity • The vast majority (80.4%) of the human genome participates in at least one biochemical RNA- and/or chromatin-associated event in at least one cell type. Much of the genome lies close to a regulatory event: 95% of the genome lies within 8 kilobases (kb) of a DNA–protein interaction (as assayed by bound ChIP-seq motifs or DNase I footprints), and 99% is within 1.7 kb of at least one of the biochemical events measured by ENCODE.

4. Elements show negative selection • Primate-specific elements as well as elements without detectable mammalian constraint show, in aggregate, evidence of negative selection; thus, some of them are expected to be functional.

5. Impact of selection on ENCODE functional elements in mammals and human populations

6. Impact of selection on ENCODE functional elements in mammals and human populations

7. Impact of selection on ENCODE functional elements in mammals and human populations

8. Impact of selection on ENCODE functional elements in mammals and human populations

9. Impact of selection on ENCODE functional elements in mammals and human populations

10. Impact of selection on ENCODE functional elements in mammals and human populations

11. We can predict RNA expression • It is possible to correlate quantitatively RNA sequence production and processing with both chromatin marks and transcription factor binding at promoters, indicating that promoter functionality can explain most of the variation in RNA expression.

12. Modelling transcription levels from histone modification and transcription-factor-binding patterns

13. Modelling transcription levels from histone modification and transcription-factor-binding patterns

14. Patterns and asymmetry of chromatin modification attranscription-factor-binding sites

15. Patterns and asymmetry of chromatin modification attranscription-factor-binding sites

16. Co-association between transcription factors

17. Co-association between transcription factors

18. Genomic segmentation • Classifying the genome into seven chromatin states indicates an initial set of 399,124 regions with enhancer-like features and 70,292 regions with promoter-like features, as well as hundreds of thousands of quiescent regions. High-resolution analyses further subdivide the genome into thousands of narrow states with distinct functional properties.

19. Integration of ENCODE databy genome-wide segmentation

20. Integration of ENCODE databy genome-wide segmentation

21. Integration of ENCODE databy genome-wide segmentation

22. Experimental characterization of segmentations

23. Experimental characterization of segmentations

24. Experimental characterization of segmentations

25. Experimental characterization of segmentations

26. High-resolution segmentation of ENCODE databy self-organizing maps (SOM)

27. High-resolution segmentation of ENCODE databy self-organizing maps (SOM)

28. High-resolution segmentation of ENCODE databy self-organizing maps (SOM)

29. Non-coding variant annotation • Many non-coding variants in individual genome sequences lie in ENCODE-annotated functional regions; this number is at least as large as those that lie in protein-coding genes.

30. Allele-specific ENCODE elements

31. Allele-specific ENCODE elements

32. Examining ENCODE elements on a per individual basis in the normal and cancer genome.

33. Examining ENCODE elements on a per individual basis in the normal and cancer genome.

34. Examining ENCODE elements on a per individual basis in the normal and cancer genome.

35. GWAS disease SNPsand ENCODE elements • Single nucleotide polymorphisms (SNPs) associated with disease by GWAS are enriched within non-coding functional elements, with a majority residing in or near ENCODE-defined regions that are outside of protein-coding genes. In many cases, the disease phenotypes can be associated with a specific cell type or transcription factor.

36. Examining ENCODE elements on a per individual basis in the normal and cancer genome.

37. Examining ENCODE elements on a per individual basis in the normal and cancer genome.

38. Examining ENCODE elements on a per individual basis in the normal and cancer genome.

39. Major results • 80% of genome shows biochemical activity • ENCODE elements under purifying selection • We can predict RNA using chromatin data • Segmentation of genome into labels • Noncoding variants often in ENCODE elements • Disease phenotypes from GWAS can be associated with a cell type or transcription factor