1. Discovery of Regulatory Elements by a Phylogenetic Footprinting Algorithm Mathieu Blanchette Martin Tompa Computer Science & Engineering University of Washington

2. 2 Outline How are genes regulated? What is phylogenetic footprinting? First solution Improvements and extensions Application to regulation of several important genes

3. 3 Regulation of Genes What turns genes on and off? When is a gene turned on or off? Where (in which cells) is a gene turned on? How many copies of the gene product are produced?

4. 4 Regulation of Genes Coding region Regulatory Element RNA polymerase Transcription Factor DNA

5. 5 RNA polymerase Transcription Factor DNA Coding region Regulation of Genes Regulatory Element

6. 6 Goal Identify regulatory elements in DNA sequences. These are: Binding sites for proteins Short substrings (5-25 nucleotides) Up to 1000 nucleotides (or farther) from gene Inexactly repeating patterns (“motifs”)

7. 7 Phylogenetic Footprinting (Tagle et al. 1988) Functional sequences evolve slower than nonfunctional ones. Consider a set of orthologous sequences from different species Identify unusually well conserved regions

8. 8 Substring Parsimony Problem Given: phylogenetic tree T, set of orthologous sequences at leaves of T, length k of motif threshold d Problem: Find each set S of k-mers, one k-mer from each leaf, such that the “parsimony” score of S in T is at most d. This problem is NP-hard.

9. 9 Small Example AGTCGTACGTGAC... (Human) AGTAGACGTGCCG... (Chimp) ACGTGAGATACGT... (Rabbit) GAACGGAGTACGT... (Mouse) TCGTGACGGTGAT... (Rat) Size of motif sought: k = 4

10. 10 Solution Parsimony score: 1 mutation AGTCGTACGTGAC... AGTAGACGTGCCG... ACGTGAGATACGT... GAACGGAGTACGT... TCGTGACGGTGAT... ACGG ACGT

11. 11 CLUSTALW multiple sequence alignment (rbcS gene) CottonACGGTT-TCCATTGGATGA---AATGAGATAAGAT---CACTGTGC---TTCTTCCACGTG--GCAGGTTGCCAAAGATA-------AGGCTTTACCATT PeaGTTTTT-TCAGTTAGCTTA---GTGGGCATCTTA----CACGTGGC---ATTATTATCCTA--TT-GGTGGCTAATGATA-------AGG--TTAGCACA TobaccoTAGGAT-GAGATAAGATTA---CTGAGGTGCTTTA---CACGTGGC---ACCTCCATTGTG--GT-GACTTAAATGAAGA-------ATGGCTTAGCACC Ice-plantTCCCAT-ACATTGACATAT---ATGGCCCGCCTGCGGCAACAAAAA---AACTAAAGGATA--GCTAGTTGCTACTACAATTC--CCATAACTCACCACC TurnipATTCAT-ATAAATAGAAGG---TCCGCGAACATTG--AAATGTAGATCATGCGTCAGAATT--GTCCTCTCTTAATAGGA-------A-------GGAGC WheatTATGAT-AAAATGAAATAT---TTTGCCCAGCCA-----ACTCAGTCGCATCCTCGGACAA--TTTGTTATCAAGGAACTCAC--CCAAAAACAAGCAAA DuckweedTCGGAT-GGGGGGGCATGAACACTTGCAATCATT-----TCATGACTCATTTCTGAACATGT-GCCCTTGGCAACGTGTAGACTGCCAACATTAATTAAA LarchTAACAT-ATGATATAACAC---CGGGCACACATTCCTAAACAAAGAGTGATTTCAAATATATCGTTAATTACGACTAACAAAA--TGAAAGTACAAGACC CottonCAAGAAAAGTTTCCACCCTC------TTTGTGGTCATAATG-GTT-GTAATGTC-ATCTGATTT----AGGATCCAACGTCACCCTTTCTCCCA-----A PeaC---AAAACTTTTCAATCT-------TGTGTGGTTAATATG-ACT-GCAAAGTTTATCATTTTC----ACAATCCAACAA-ACTGGTTCT---------A TobaccoAAAAATAATTTTCCAACCTTT---CATGTGTGGATATTAAG-ATTTGTATAATGTATCAAGAACC-ACATAATCCAATGGTTAGCTTTATTCCAAGATGA Ice-plantATCACACATTCTTCCATTTCATCCCCTTTTTCTTGGATGAG-ATAAGATATGGGTTCCTGCCAC----GTGGCACCATACCATGGTTTGTTA-ACGATAA TurnipCAAAAGCATTGGCTCAAGTTG-----AGACGAGTAACCATACACATTCATACGTTTTCTTACAAG-ATAAGATAAGATAATGTTATTTCT---------A WheatGCTAGAAAAAGGTTGTGTGGCAGCCACCTAATGACATGAAGGACT-GAAATTTCCAGCACACACA-A-TGTATCCGACGGCAATGCTTCTTC-------- DuckweedATATAATATTAGAAAAAAATC-----TCCCATAGTATTTAGTATTTACCAAAAGTCACACGACCA-CTAGACTCCAATTTACCCAAATCACTAACCAATT LarchTTCTCGTATAAGGCCACCA-------TTGGTAGACACGTAGTATGCTAAATATGCACCACACACA-CTATCAGATATGGTAGTGGGATCTG--ACGGTCA CottonACCAATCTCT---AAATGTT----GTGAGCT---TAG-GCCAAATTT-TATGACTATA--TAT----AGGGGATTGCACC----AAGGCAGTG-ACACTA PeaGGCAGTGGCC---AACTAC--------------------CACAATTT-TAAGACCATAA-TAT----TGGAAATAGAA------AAATCAAT--ACATTA TobaccoGGGGGTTGTT---GATTTTT----GTCCGTTAGATAT-GCGAAATATGTAAAACCTTAT-CAT----TATATATAGAG------TGGTGGGCA-ACGATG Ice-plantGGCTCTTAATCAAAAGTTTTAGGTGTGAATTTAGTTT-GATGAGTTTTAAGGTCCTTAT-TATA---TATAGGAAGGGGG----TGCTATGGA-GCAAGG TurnipCACCTTTCTTTAATCCTGTGGCAGTTAACGACGATATCATGAAATCTTGATCCTTCGAT-CATTAGGGCTTCATACCTCT----TGCGCTTCTCACTATA WheatCACTGATCCGGAGAAGATAAGGAAACGAGGCAACCAGCGAACGTGAGCCATCCCAACCA-CATCTGTACCAAAGAAACGG----GGCTATATATACCGTG DuckweedTTAGGTTGAATGGAAAATAG---AACGCAATAATGTCCGACATATTTCCTATATTTCCG-TTTTTCGAGAGAAGGCCTGTGTACCGATAAGGATGTAATC LarchCGCTTCTCCTCTGGAGTTATCCGATTGTAATCCTTGCAGTCCAATTTCTCTGGTCTGGC-CCA----ACCTTAGAGATTG----GGGCTTATA-TCTATA CottonT-TAAGGGATCAGTGAGAC-TCTTTTGTATAACTGTAGCAT--ATAGTAC PeaTATAAAGCAAGTTTTAGTA-CAAGCTTTGCAATTCAACCAC--A-AGAAC TobaccoCATAGACCATCTTGGAAGT-TTAAAGGGAAAAAAGGAAAAG--GGAGAAA Ice-plantTCCTCATCAAAAGGGAAGTGTTTTTTCTCTAACTATATTACTAAGAGTAC LarchTCTTCTTCACAC---AATCCATTTGTGTAGAGCCGCTGGAAGGTAAATCA TurnipTATAGATAACCA---AAGCAATAGACAGACAAGTAAGTTAAG-AGAAAAG WheatGTGACCCGGCAATGGGGTCCTCAACTGTAGCCGGCATCCTCCTCTCCTCC DuckweedCATGGGGCGACG---CAGTGTGTGGAGGAGCAGGCTCAGTCTCCTTCTCG

12. 12 An Exact Algorithm (generalizing Sankoff and Rousseau 1975) W u [s] =best parsimony score for subtree rooted at node u, if u is labeled with string s. AGTCGTACGTG ACGGGACGTGC ACGTGAGATAC GAACGGAGTAC TCGTGACGGTG … ACGG: 2 ACGT: 1... … ACGG : 0 ACGT : 2... … ACGG : 1 ACGT : 1... … ACGG: +  ACGT: 0... … ACGG: 1 ACGT: 0... 4 k entries … ACGG: 0 ACGT: + ... … ACGG:  ACGT :0...

13. 13 W u [s] =  min ( W v [t] + d(s, t) ) v : child t of u Recurrence

14. 14 O(k  4 2k ) time per node W u [s] =  min ( W v [t] + d(s, t) ) v : child t of u Running Time

15. 15 O(k  4 2k ) time per node Number of species Average sequence length Motif length Total time O(n k (4 2k + l )) W u [s] =  min ( W v [t] + d(s, t) ) v : child t of u Running Time

16. 16 Improvements Better algorithm reduces time from O(n k (4 2k + l )) to O(n k (4 k + l )) By restricting to motifs with parsimony score at most d, greatly reduce the number of table entries computed (exponential in d, polynomial in k) Amenable to many useful extensions (e.g., allow insertions and deletions)

17. 17 Application to  -actin Gene Gilthead sea bream (678 bp) Medaka fish (1016 bp) Common carp (696 bp) Grass carp (917 bp) Chicken (871 bp) Human (646 bp) Rabbit (636 bp) Rat (966 bp) Mouse (684 bp) Hamster (1107 bp)

18. 18 Common carp ACGGACTGTTACCACTTCACGCCGACTCAACTGCGCAGAGAAAAACTTCAAACGACAAC A TTGGCATGGCTT TTGTTATTTTTGGCGC TTGACTCAGG AT C T AAAAACTGGAAC G GCGAAGGTGACGGCAATGTTTTGGCAAATAAGCATCCCCGAAGTTCTACAATGCATCTGAGGACTCAATGTTTTTTTTTTTTTTT TTTCTTT AGTCATTCCAAAT GTTTGTTAAATGCATTGTTCCGAAACTTATTTGCCTCTATGAAGGCTGCCCAGTAATTGGGAGCATACTTAACATTGTAGTATTGTA T GTAAATTATGT AACAAAACAATGACTGGGTTTTTGTACTTTCAGCCTTAATCTTGGGTTTTTTTTTTTTTTTGGTTCCAAAAAACTAAGCTTTACCATTCAAGATGTAAA GGTTTCATTCCCCCTGGCATATTGAAAAAGCTGTGTGGAACGTGGCGGTGCAGACATTTGGTGGGGCCA A CCTGTACACTGAC T AATTCAAATAAAAGT GCACATGTAAGACATCCTACTCTGTGTGATTTTTCTGTTTGTGCTGAGTGAACTTGCTATGAAGTCTTTTAGTGCACTCTTTAATAAAAGTAGTCTTCCCTTAAAGTGTCC CTTCCCTTATGGCCTTCACATTTCTCAACTAGCGCTTCAACTAGAAAGCACTTTAGGGACTGGGATGC Chicken ACCGGACTGTTACCAACACCCACACCCCTGTGATGAAACAAAACCCATAAATGCGCATAAAACAAGACGAG A TTGGCATGGCTT TATTTGTTTTTTCTTTTGGC GC TTGACTCAGGAT T A AAAAACTGGAAT G GTGAAGGTGTCAGCAGCAGTCTTAAAATGAAACATGTTGGAGCGAACGCCCCCAAAGTTCTACAATG CATCTGAGGACTTTGATTGTACATTTGTTTCTTTTTTAAT AGTCATTCCAAAT ATTGTTATAATGCATTGTTACAGGAAGTTACTCGCCTCTGTGAAGGCAACAGCCCA GCTGGGAGGAGCCGGTACCAATTACTGGTGTTAGATGATAATTGCTTGTC TGTAAATTATGT AACCCAACAAGTGTCTTTTTGTATCTTCCGCCTTAAAAACAAAACAC ACTTGATCCTTTTTGGTTTGTCAAGCAAGCGGGCTGTGTTCCCCAGTGATAGATGTGAATGAAGGCTTTACAGTCCCCCACAGTCTAGGAGTAAAGTGCCAGTATGTGGG GGAGGGAGGGGCT A CCTGTACACTGAC T TAAGACCAGTTCAAATAAAAGTGCACACAATAGAGGCTTGACTGGTGTTGGTTTTTATTTCTGTGCTGCGC TGCTTGGCCGTTGGTAGCTGTTCTCATCTAGCCTTGCCAGCCTGTGTGGGTCAGCTATCTGCATGGGCTGCGTGCTGGTGCTGTCTGGTGCAGAGGTTGGATAAACCGT GATGATATTTCAGCAAGTGGGAGTTGGCTCTGATTCCATCCTGAGCTGCCATCAGTGTGTTCTGAAGGAAGCTGTTGGATGAGGGTGGGCTGAGTGCTGGGGGACAGCT GGGCTCAGTGGGACTGCAGCTGTGCT Human GCGGACTATGACTTAGTTGCGTTACACCCTTTCTTGACAAAACCTAACTTGCGCAGAAAACAAGATGAG A TTGGCATGGCTT TATTTGTTTTTTTTGTTTTGTT TTGGTTTTTTTTTTTTTTTTGGC TTGACTCAGGAT T T AAAAACTGGAAC G GTGAAGGTGACAGCAGTCGGTTGGAGCGAGCATCCCCCAAAGTTCA CAATGTGGCCGAGGACTTTGATTGCATTGTTGTTTTTTTAAT AGTCATTCCAAAT ATGAGATGCATTGTTACAGGAAGTCCCTTGCCATCCTAAAAGCCACCCCACTTC TCTCTAAGGAGAATGGCCCAGTCCTCTCCCAAGTCCACACAGGGGAGGTGATAGCATTGCTTTCG TGTAAATTATGT AATGCAAAATTTTTTTAATCTTCGCCTTAATA CTTTTTTATTTTGTTTTATTTTGAATGATGAGCCTTCGTGCCCCCCCTTCCCCCTTTTTGTCCCCCAACTTGAGATGTATGAAGGCTTTTGGTCTCCCTGGGAGTGGGTGG AGGCAGCCAGGGCTT A CCTGTACACTGAC T TGAGACCAGTTGAATAAAAGTGCACACCTTAAAAATGAGGCCAAGTGTGACTTTGTGGTGTGGCTGGGT TGGGGGCAGCAGAGGGTG Parsimony score over 10 vertebrates: 0 1 2

19. 19 Motifs Absent from Some Species Find motifs –with small parsimony score –that span a large part of the tree Example: in tree of 10 species spanning 760 Myrs, find all motifs with –score 0 spanning at least 250 Myrs –score 1 spanning at least 350 Myrs –score 2 spanning at least 450 Myrs –score 3 spanning at least 550 Myrs

20. 20 Application to c-fos Gene Asked for motifs of length 10, with 0 mutations over tree of size 6 1 mutation over tree of size 11 2 mutations over tree of size 16 3 mutations over tree of size 21 4 mutations over tree of size 26 Puffer fish Chicken Pig Mouse Hamster Human 102 7 2 2 2 1 0 1 1 Found: 0 mutations over tree of size 8 1 mutation over tree of size 16 3 mutations over tree of size 21 4 mutations over tree of size 28

21. 21 Application to c-fos Gene MotifScoreConserved inKnown? CAGGTGCGAATGTTC04 mammals TTCCCGCCTCCCCTCCCC04 mammalsyes GAGTTGGCTGcagcc3puffer + 4 mammals GTTCCCGTCAATCcct1chicken + 4 mammals yes CACAGGATGTcc4all 6 yes AGGACATCTG1chicken + 4 mammals yes GTCAGCAGGTTTCCACG04 mammals yes TACTCCAACCGC04 mammals

22. 22 Other Genes Similar results for the following genes: insulin c-myc promoter and intron growth hormone interleukin-3 histone H1  -globin dihydrofolate reductase fibroin myogenin prolactin thyroglobulin γ-actin 3´ UTR rbcS rbcL

23. 23 Conclusions Guaranteed optimality for question posed Time linear in the number of species and the total sequence lengths, exponential in the parsimony score Practical on real biological data sets Discovered highly conserved regions, both known and not (yet) known Available at http://bio.cs.washington.edu/software.html