Finding regulatory modules from local alignment

1. Finding regulatory modules from local alignment - Department of Computer Science & Helsinki Institute of Information Technology HIIT University of Helsinki Erice 30 Nov 2005

2. Pairwise alignment of strings • A: S T O C K H O L M B: T U K H O L M A • minimum number of ’mutation’ steps: a -> b a -> є є -> b …

3. Dynamic programming di,j = min(if ai=bj then di-1,j-1 else , di-1,j + 1, di,j-1 + 1) = distance between i-prefix of A and j-prefix of B (without substitutions) B mxn table d bj di-1,j-1 di-1,j A +1 ai di,j-1 di,j +1 dm,n

4. di,j = min(if ai=bj then di-1,j-1 else , di-1,j + 1, di,j-1 + 1) optimal alignment by trace-back dID(A,B)

5. Homology searches • find homologous sequences: new sequence versus all old ones in database – the most popular computational task in present-day molecular biology = approximate string matching • BLAST - big success • good homology => same biological function D A T A B A S E NEW SEQUENCE ?

6. Multiple alignment • multiple alignment of sequence families to find interesting conserved motifs: NP-hard => heuristics, Hidden Markov models, MCMC • comparison of entire genomes

7. Gene enhancer module prediction

8. Problem • Gene expression regulation in multicellular organisms is controlled in combinatorial fashion by so called transcription factors (TFs). • Transcription factors bind to DNA cis-elements (TF binding sites) on enhancer modules (promoters), and multiple factors need to bind to activate the module. • In mammals, the modules are few and far • The problem: Locate functional regulatory modules, that is, find interesting patterns.

9. Gene enhancer modules enhancer module gene1 gene2 gene3 gene4 DNA transcription transcription factors RNA translation Proteins

10. Model of cell type specific regulation of target gene expression Common targets (e.g. Patched): GLI GLI Ubiquitously expressed TF transcription Cell type specific targets (e.g. N-myc): GLI X Y (tissue specific TFs) transcription

11. Binding affinity matrices • The TF binding sites are represented by affinity matrices. • A column per position • A row per nucleotide • Discovered: • Computationally • Traditional wet lab • Microarrays 9 11 49 51 0 1 1 4 19 3 0 0 0 45 25 16 5 1 2 0 17 0 4 21 18 36 0 0 34 5 21 10

12. Binding affinity matrices 9 11 49 51 0 1 1 4 19 3 0 0 0 45 25 16 5 1 2 0 17 0 4 21 18 36 0 0 34 5 21 10

13. Determined TF binding profiles (+ JASPAR)

14. Finding conserved motifs of binding sites • looking at one (human) genome gives too many positives • comparative genomics approach: • take the 200 kB regions surrounding the same genes (paralogs and orthologs) of different mammals: human, mouse, chicken, … • find conserved clusters (= motifs) of binding sites • cluster = group of binding sites with good local alignment = > Smith-Waterman type algorithm with a novel scoring function

15. Smith-Waterman • find the best local alignment of strings A and B: substring X of A and substring Y of B such that X and Y have the best scoring pairwise alignment Y X

16. Computational identification of enhancer elements • Preserved in evolution: • Affinities of functional cis-elements. • Spatial arrangement of elements within a module. Human Mouse

17. Parameter optimization • scoring function has 3 free parameters. • Find good parameters by greedy hill climbing using a training data

18. Whole genome comparisons • Whole genomes can be analyzed with our implementation EEL (Enhancer Element Locator) • We compared human genes to orthologs in mouse, rat, chicken, fugu, tetraodon and zebrafish • 100 kbp flanking regions on both sides of the gene. • Coding regions masked out. • About 20 000 comparisons for each pair of species.

19. Annotating the Human genome with mammalian enhancer-elements

20. EEL output • Output from EEL program. • Previously known functional sites are highlighted • DNA between the sites is aligned just for the output

21. coding region of N-Myc Enhancer prediction for N-myc 200 kb Mouse N-Myc genomic region 200 kb Human N-Myc genomic region Conserved GLI binding sites in two predicted enhancer elements, CM5 and CM7

22. Wet-lab verification • Selected some predicted enhancer modules for wet-lab verification • Fused 1kb DNA segment containing the predicted enhancer to a marker gene (LacZ) with a minimal promoter, and generated transgenic embryos.

23. coding region of N-Myc Enhancer prediction for N-myc 200 kb Mouse N-Myc genomic region 200 kb Human N-Myc genomic region Conserved GLI binding sites in two predicted enhancer elements, CM5 and CM7

24. Summary • input: +- 100 kb flanking sequences of DNA of orthologous pairs of genes from human and mouse • find all good enough TF binding sites from the sequences • find the best local alignments of the binding sites using the EEL scoring function • output: the sequences in good local alignments; these are the putative enhancers • postprocessing: an expert biologist selects the most promising predictions for wet lab verification; hopefully he/she has good luck!

25. Kimmo Palin Outi Hallikas (Biom) Jussi Taipale (Biom) The BioSapiens project is funded by the European Commission within its FP6 Programme, under the thematic area "Life sciences, genomics and biotechnology for health,"contract number LHSG-CT-2003-503265. Acknowledgements