Basset: Learning the regulatory code of the accessible genome with deep convolutional neural networks. David R. Kelley

Transkriptio

1 Basset: Learning the regulatory code of the accessible genome with deep convolutional neural networks David R. Kelley

2 DNA codes for complex life. How? Kundaje et al. Integrative analysis of 111 reference human epigenomes. Nature, 2015.

3 Noncoding DNA determines gene expression Transcription Factors. Kelvin Song. CC BY 3.0

4 We can t read noncoding DNA TAGTAAAAAAACAACAAAAGACTTTTTCTGACAGTATGATTTACATAACTA CATTTTCATTACTTTTATTTTTCTACATAACTAGTGTCTTTTCAGTTGCAA ATGTTTGACATTCTGACAAGTAGTGTGGCAGAGTCTTAGATTATAGGTTGC ATTTAGCCAAAAGAAAGACTTCGAATGGAATTTTTTTCTATTGACACACTT TCTAACAACATACTTATTTTCTAAAAAGGTTTTTATAACTTAGTGTTGATA ATATCAAAATGCTAAGCAATTTTGCTTAAAAAGCGTAGAACACCAATATTT AATGAAGATTAATTAAATAGCACACATTGATTACTTGTTTAAAAATATTCG GAAAAGTTTTGACACATGCTAAAGTGCTGAAGTAGGATTTTGGCCTTCCAT AAAAATAATATATTGTGCATAAATGGATGCAGAATGAAGAAAGCAATGGGG

5 Reading noncoding DNA would transform variant interpretation

6 DNaseI hypersensitivity KLF4 20 kb Boyle et al. High-resolution mapping and characterization of open chromatin across the genome. Cell, 2008.

7 Is this sequence accessible? TAGTAAAAAAACAACAAAAGACTTTTTCTGACAGTATGATTTACATAACTA CATTTTCATTACTTTTATTTTTCTACATAACTAGTGTCTTTTCAGTTGCAA ATGTTTGACATTCTGACAAGTAGTGTGGCAGAGTCTTAGATTATAGGTTGC ATTTAGCCAAAAGAAAGACTTCGAATGGAATTTTTTTCTATTGACACACTT TCTAACAACATACTTATTTTCTAAAAAGGTTTTTATAACTTAGTGTTGATA ATATCAAAATGCTAAGCAATTTTGCTTAAAAAGCGTAGAACACCAATATTT AATGAAGATTAATTAAATAGCACACATTGATTACTTGTTTAAAAATATTCG GAAAAGTTTTGACACATGCTAAAGTGCTGAAGTAGGATTTTGGCCTTCCAT AAAAATAATATATTGTGCATAAATGGATGCAGAATGAAGAAAGCAATGGGG

8 Learning DNA sequence activity ACGTGATTACAACGT 1 ATTTAGCCAAAAGAA 1 AGACTTCGAATGGAA 0 TTTTTTTCTATTGAC 0 ACACTTTCTAACAAC 0 ATACTTATTTTCTAA 1 AAAGGTTTTTATAAC 0 TTAGTGTTGATAATA 0

9 Sequence representation ACGTGATTACAACGT

10 Sequence representation ACGTGATTACAACGT ACGT 1

11 Sequence representation ACGTGATTACAACGT ACGT 1 CGTG 1

12 Sequence representation With big data & big computers, Can we learn better representations? ACGTGATTACAACGT AACG 1 ACAA 1 ACGT 2 ATTA 1 CAAC 1 CGTG 1 GATT 1 GTGA 1 TACA 1 TGAT 1 TTAC 1 0 Learning Algorithm

13 Artificial neural networks

14 Convolutional neural network

15 Convolutional neural network Zeiler, Fergus. Visualizing and understanding convolutional networks. ECCV 2014.

16 Convolutional neural network Zeiler, Fergus. Visualizing and understanding convolutional networks. ECCV 2014.

17 Convolutional neural network Zeiler, Fergus. Visualizing and understanding convolutional networks. ECCV 2014.

18 Convolutional neural network Zeiler, Fergus. Visualizing and understanding convolutional networks. ECCV 2014.

19 DNA convolutional neural network A C G T

20 DNA convolutional neural network A C G T

21 DNA convolutional neural network A C G T

22 DNA convolutional neural network A C G T

23 DNA convolutional neural network A C G T

24 DNA convolutional neural network A C G T

25 DNA convolutional neural network

26 Basset

27 Learning the accessibility code DNaseI hypersensitivity sites DNaseI-seq from 164 cell types from ENCODE and Epigenomics Roadmap. Cells 2 million sites broken into training, validation, and test sets.

28 Convolutional nets accurately predict accessibility 1.0 True positive rate PanIslets AUC: HUVEC AUC: CLL AUC: HRE AUC: HPF AUC: False positive rate Basset AUC mean AUC mean AUC gkm-svm AUC Ghandi, Lee et al. Enhanced Regulatory Sequence Prediction Using Gapped k-mer Features. PLoS Comp Bio, 2014.

29 Filters recapitulate known protein binding motifs

30 Filters recapitulate known protein binding motifs

31 Multiple filters capture motif variants

32 Filter influence reflects cell specificity

33 Annotate nucleotide influence mb mb mb mb JUN ChIP-seq DNaseI-seq JUND ChIP-seq CAGCCTTTGTTAATGGGGACACAATCCTGGAAATTTTGCCTGTGTGTAAACCTCTAGGGGCTTTTTCTTTCATCGTTTTACATCAGCCAGACTCTGACTCACAGCTGGAGAATCAGCTTCCTTATTATGTAGCGAATTCCATGAACACAC PhyloP

34 Case study: vitiligo - rs

35 Case study: vitiligo - rs Jin et al. Genome-wide associate analyses identify 13 new susceptibility loci for generalized vitiligo. Nat Genetics, 2012.

36 Predictive of causal disease SNPs

37 CTCF ChIP-seq shows allele-specific rs binding

38 Can we easily add new datasets? SNP interpretation requires relevant cell types. 1. Seed a new model with pretrained parameters. 2. Chop off the final model layer. 3. Train one pass (to avoid overfitting).

39 Large-scale public data informs new dataset learning

40 Basset Published in Advance May 3, 2016, doi: /gr

41 Summary Deep convolutional neural networks predict DNaseI hypersensitivity far beyond previous algorithms. ChIP-seq peaks work great, too. Accurate models enable nucleotide-resolution genome annotation. Such models show great promise for interpreting noncoding variants.

42 Acknowledgments Jasper Snoek John Rinn Research was supported by NIEHS of the National Institutes of Health under K25 award ES