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Discovery of Regulatory Elements by a Phylogenetic
Footprinting AlgorithmMathieu Blanchette
Martin Tompa
Computer Science & EngineeringUniversity of Washington
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Outline•How are genes regulated?•What is phylogenetic footprinting?•First solution•Improvements and extensions•Application to regulation of several important genes
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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?
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Regulation of Genes
Coding regionRegulatory Element
RNA polymerase
Transcription Factor
DNA
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RNA polymerase
Transcription Factor
DNA
Coding region
Regulation of Genes
Regulatory Element
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GoalIdentify 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”)
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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
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Substring Parsimony ProblemGiven:
• 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.
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Small Example
AGTCGTACGTGAC... (Human)
AGTAGACGTGCCG... (Chimp)
ACGTGAGATACGT... (Rabbit)
GAACGGAGTACGT... (Mouse)
TCGTGACGGTGAT... (Rat)
Size of motif sought: k = 4
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Solution
Parsimony score: 1 mutation
AGTCGTACGTGAC...AGTAGACGTGCCG...ACGTGAGATACGT...GAACGGAGTACGT...TCGTGACGGTGAT...ACGG
ACGT
ACGT
ACGT
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CLUSTALW multiple sequence alignment (rbcS gene)Cotton ACGGTT-TCCATTGGATGA---AATGAGATAAGAT---CACTGTGC---TTCTTCCACGTG--GCAGGTTGCCAAAGATA-------AGGCTTTACCATTPea GTTTTT-TCAGTTAGCTTA---GTGGGCATCTTA----CACGTGGC---ATTATTATCCTA--TT-GGTGGCTAATGATA-------AGG--TTAGCACATobacco TAGGAT-GAGATAAGATTA---CTGAGGTGCTTTA---CACGTGGC---ACCTCCATTGTG--GT-GACTTAAATGAAGA-------ATGGCTTAGCACCIce-plant TCCCAT-ACATTGACATAT---ATGGCCCGCCTGCGGCAACAAAAA---AACTAAAGGATA--GCTAGTTGCTACTACAATTC--CCATAACTCACCACCTurnip ATTCAT-ATAAATAGAAGG---TCCGCGAACATTG--AAATGTAGATCATGCGTCAGAATT--GTCCTCTCTTAATAGGA-------A-------GGAGCWheat TATGAT-AAAATGAAATAT---TTTGCCCAGCCA-----ACTCAGTCGCATCCTCGGACAA--TTTGTTATCAAGGAACTCAC--CCAAAAACAAGCAAADuckweed TCGGAT-GGGGGGGCATGAACACTTGCAATCATT-----TCATGACTCATTTCTGAACATGT-GCCCTTGGCAACGTGTAGACTGCCAACATTAATTAAALarch TAACAT-ATGATATAACAC---CGGGCACACATTCCTAAACAAAGAGTGATTTCAAATATATCGTTAATTACGACTAACAAAA--TGAAAGTACAAGACC
Cotton CAAGAAAAGTTTCCACCCTC------TTTGTGGTCATAATG-GTT-GTAATGTC-ATCTGATTT----AGGATCCAACGTCACCCTTTCTCCCA-----APea C---AAAACTTTTCAATCT-------TGTGTGGTTAATATG-ACT-GCAAAGTTTATCATTTTC----ACAATCCAACAA-ACTGGTTCT---------ATobacco AAAAATAATTTTCCAACCTTT---CATGTGTGGATATTAAG-ATTTGTATAATGTATCAAGAACC-ACATAATCCAATGGTTAGCTTTATTCCAAGATGAIce-plant ATCACACATTCTTCCATTTCATCCCCTTTTTCTTGGATGAG-ATAAGATATGGGTTCCTGCCAC----GTGGCACCATACCATGGTTTGTTA-ACGATAATurnip CAAAAGCATTGGCTCAAGTTG-----AGACGAGTAACCATACACATTCATACGTTTTCTTACAAG-ATAAGATAAGATAATGTTATTTCT---------AWheat GCTAGAAAAAGGTTGTGTGGCAGCCACCTAATGACATGAAGGACT-GAAATTTCCAGCACACACA-A-TGTATCCGACGGCAATGCTTCTTC--------Duckweed ATATAATATTAGAAAAAAATC-----TCCCATAGTATTTAGTATTTACCAAAAGTCACACGACCA-CTAGACTCCAATTTACCCAAATCACTAACCAATTLarch TTCTCGTATAAGGCCACCA-------TTGGTAGACACGTAGTATGCTAAATATGCACCACACACA-CTATCAGATATGGTAGTGGGATCTG--ACGGTCA
Cotton ACCAATCTCT---AAATGTT----GTGAGCT---TAG-GCCAAATTT-TATGACTATA--TAT----AGGGGATTGCACC----AAGGCAGTG-ACACTAPea GGCAGTGGCC---AACTAC--------------------CACAATTT-TAAGACCATAA-TAT----TGGAAATAGAA------AAATCAAT--ACATTATobacco GGGGGTTGTT---GATTTTT----GTCCGTTAGATAT-GCGAAATATGTAAAACCTTAT-CAT----TATATATAGAG------TGGTGGGCA-ACGATGIce-plant GGCTCTTAATCAAAAGTTTTAGGTGTGAATTTAGTTT-GATGAGTTTTAAGGTCCTTAT-TATA---TATAGGAAGGGGG----TGCTATGGA-GCAAGGTurnip CACCTTTCTTTAATCCTGTGGCAGTTAACGACGATATCATGAAATCTTGATCCTTCGAT-CATTAGGGCTTCATACCTCT----TGCGCTTCTCACTATAWheat CACTGATCCGGAGAAGATAAGGAAACGAGGCAACCAGCGAACGTGAGCCATCCCAACCA-CATCTGTACCAAAGAAACGG----GGCTATATATACCGTGDuckweed TTAGGTTGAATGGAAAATAG---AACGCAATAATGTCCGACATATTTCCTATATTTCCG-TTTTTCGAGAGAAGGCCTGTGTACCGATAAGGATGTAATCLarch CGCTTCTCCTCTGGAGTTATCCGATTGTAATCCTTGCAGTCCAATTTCTCTGGTCTGGC-CCA----ACCTTAGAGATTG----GGGCTTATA-TCTATA
Cotton T-TAAGGGATCAGTGAGAC-TCTTTTGTATAACTGTAGCAT--ATAGTACPea TATAAAGCAAGTTTTAGTA-CAAGCTTTGCAATTCAACCAC--A-AGAACTobacco CATAGACCATCTTGGAAGT-TTAAAGGGAAAAAAGGAAAAG--GGAGAAAIce-plant TCCTCATCAAAAGGGAAGTGTTTTTTCTCTAACTATATTACTAAGAGTACLarch TCTTCTTCACAC---AATCCATTTGTGTAGAGCCGCTGGAAGGTAAATCATurnip TATAGATAACCA---AAGCAATAGACAGACAAGTAAGTTAAG-AGAAAAGWheat GTGACCCGGCAATGGGGTCCTCAACTGTAGCCGGCATCCTCCTCTCCTCCDuckweed CATGGGGCGACG---CAGTGTGTGGAGGAGCAGGCTCAGTCTCCTTCTCG
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An Exact Algorithm(generalizing Sankoff and Rousseau 1975)
Wu [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 ...
4k entries
… ACGG: 0 ACGT: + ...
… ACGG: ACGT :0 ...
… ACGG: ACGT :0 ...
… ACGG: ACGT :0 ...
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Wu [s] = min ( Wv [t] + d(s, t) ) v: child t of u
Recurrence
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O(k 42k ) time per node
Wu [s] = min ( Wv [t] + d(s, t) ) v: child t of u
Running Time
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O(k 42k ) time per node
Number of species
Average sequence
length
Motif length
Total time O(n k (42k + l ))
Wu [s] = min ( Wv [t] + d(s, t) ) v: child t of u
Running Time
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Improvements• Better algorithm reduces time from
O(n k (42k + l )) to O(n k (4k + 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)
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Application to -actin GeneGilthead 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)
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Common carpACGGACTGTTACCACTTCACGCCGACTCAACTGCGCAGAGAAAAACTTCAAACGACAACATTGGCATGGCTTTTGTTATTTTTGGCGCTTGACTCAGGATCTAAAAACTGGAACGGCGAAGGTGACGGCAATGTTTTGGCAAATAAGCATCCCCGAAGTTCTACAATGCATCTGAGGACTCAATGTTTTTTTTTTTTTTTTTTCTTTAGTCATTCCAAATGTTTGTTAAATGCATTGTTCCGAAACTTATTTGCCTCTATGAAGGCTGCCCAGTAATTGGGAGCATACTTAACATTGTAGTATTGTATGTAAATTATGTAACAAAACAATGACTGGGTTTTTGTACTTTCAGCCTTAATCTTGGGTTTTTTTTTTTTTTTGGTTCCAAAAAACTAAGCTTTACCATTCAAGATGTAAAGGTTTCATTCCCCCTGGCATATTGAAAAAGCTGTGTGGAACGTGGCGGTGCAGACATTTGGTGGGGCCAACCTGTACACTGACTAATTCAAATAAAAGTGCACATGTAAGACATCCTACTCTGTGTGATTTTTCTGTTTGTGCTGAGTGAACTTGCTATGAAGTCTTTTAGTGCACTCTTTAATAAAAGTAGTCTTCCCTTAAAGTGTCCCTTCCCTTATGGCCTTCACATTTCTCAACTAGCGCTTCAACTAGAAAGCACTTTAGGGACTGGGATGC
ChickenACCGGACTGTTACCAACACCCACACCCCTGTGATGAAACAAAACCCATAAATGCGCATAAAACAAGACGAGATTGGCATGGCTTTATTTGTTTTTTCTTTTGGCGCTTGACTCAGGATTAAAAAACTGGAATGGTGAAGGTGTCAGCAGCAGTCTTAAAATGAAACATGTTGGAGCGAACGCCCCCAAAGTTCTACAATGCATCTGAGGACTTTGATTGTACATTTGTTTCTTTTTTAATAGTCATTCCAAATATTGTTATAATGCATTGTTACAGGAAGTTACTCGCCTCTGTGAAGGCAACAGCCCAGCTGGGAGGAGCCGGTACCAATTACTGGTGTTAGATGATAATTGCTTGTCTGTAAATTATGTAACCCAACAAGTGTCTTTTTGTATCTTCCGCCTTAAAAACAAAACACACTTGATCCTTTTTGGTTTGTCAAGCAAGCGGGCTGTGTTCCCCAGTGATAGATGTGAATGAAGGCTTTACAGTCCCCCACAGTCTAGGAGTAAAGTGCCAGTATGTGGGGGAGGGAGGGGCTACCTGTACACTGACTTAAGACCAGTTCAAATAAAAGTGCACACAATAGAGGCTTGACTGGTGTTGGTTTTTATTTCTGTGCTGCGCTGCTTGGCCGTTGGTAGCTGTTCTCATCTAGCCTTGCCAGCCTGTGTGGGTCAGCTATCTGCATGGGCTGCGTGCTGGTGCTGTCTGGTGCAGAGGTTGGATAAACCGTGATGATATTTCAGCAAGTGGGAGTTGGCTCTGATTCCATCCTGAGCTGCCATCAGTGTGTTCTGAAGGAAGCTGTTGGATGAGGGTGGGCTGAGTGCTGGGGGACAGCTGGGCTCAGTGGGACTGCAGCTGTGCT
HumanGCGGACTATGACTTAGTTGCGTTACACCCTTTCTTGACAAAACCTAACTTGCGCAGAAAACAAGATGAGATTGGCATGGCTTTATTTGTTTTTTTTGTTTTGTTTTGGTTTTTTTTTTTTTTTTGGCTTGACTCAGGATTTAAAAACTGGAACGGTGAAGGTGACAGCAGTCGGTTGGAGCGAGCATCCCCCAAAGTTCACAATGTGGCCGAGGACTTTGATTGCATTGTTGTTTTTTTAATAGTCATTCCAAATATGAGATGCATTGTTACAGGAAGTCCCTTGCCATCCTAAAAGCCACCCCACTTCTCTCTAAGGAGAATGGCCCAGTCCTCTCCCAAGTCCACACAGGGGAGGTGATAGCATTGCTTTCGTGTAAATTATGTAATGCAAAATTTTTTTAATCTTCGCCTTAATACTTTTTTATTTTGTTTTATTTTGAATGATGAGCCTTCGTGCCCCCCCTTCCCCCTTTTTGTCCCCCAACTTGAGATGTATGAAGGCTTTTGGTCTCCCTGGGAGTGGGTGGAGGCAGCCAGGGCTTACCTGTACACTGACTTGAGACCAGTTGAATAAAAGTGCACACCTTAAAAATGAGGCCAAGTGTGACTTTGTGGTGTGGCTGGGTTGGGGGCAGCAGAGGGTG
Parsimony score over 10 vertebrates: 0 1 2
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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
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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
10
2
7
2
2
21
01
1
Found: 0 mutations over tree of size 81 mutation over tree of size 163 mutations over tree of size 214 mutations over tree of size 28
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Application to c-fos GeneMotif Score Conserved in Known?
CAGGTGCGAATGTTC 0 4 mammals
TTCCCGCCTCCCCTCCCC 0 4 mammals yes
GAGTTGGCTGcagcc 3 puffer + 4 mammals
GTTCCCGTCAATCcct 1 chicken + 4 mammals yes
CACAGGATGTcc 4 all 6 yes
AGGACATCTG 1 chicken + 4 mammals yes
GTCAGCAGGTTTCCACG 0 4 mammals yes
TACTCCAACCGC 0 4 mammals
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Other GenesSimilar 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
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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