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Comparative Motif Finding

Comparative Motif Finding. CS 374 – Lecture 23 Mayukh Bhaowal. Reference Papers.

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Comparative Motif Finding

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  1. Comparative Motif Finding CS 374 – Lecture 23 Mayukh Bhaowal

  2. Reference Papers • Xiaohui Xie, Jun Lu, E. J. Kulbokas, Todd R. Golub, Vamsi Mootha, Kerstin Lindblad-Toh, Eric S. Lander, Manolis Kellis, “Systematic discovery of regulatory motifs in human promoters and 3’UTRs by comparison of several mammals”, Nature, 2005 • Mathieu Blanchette and Martin Tompa, “Discovery of Regulatory Elements by a Computational Method for Phylogenetic Footprinting”, Genome Res. 2002 12: 739-748

  3. What is a Motif ? • A motif is a nucleotide sequence pattern and has biological significance. • Regulatory motifs are DNA fragments

  4. Motif Logos • Height of letters represents probability of being found in that location in the motif

  5. Why is it difficult to find them? 1. Short fragments 2. Degenerate 3. Unpredictable Motifs can occur in either strands.

  6. Promoter • In genetics, a promoter is a DNA sequence that enables a gene to be transcribed. The promoter is recognized by RNA polymerase, which then initiates transcription.

  7. 3’ UTR • The three prime untranslated region (3' UTR) is a particular section of messenger RNA (mRNA). • An mRNA codes for a protein through translation. The mRNA also contains regions that are not translated. In eukaryotes the 5' untranslated region, 3' untranslated region, cap and polyA tail. Image source : http://en.wikipedia.org/wiki/Image:MRNA_structure.png

  8. What the paper proposes • What? Discovering the regulatory motifs in human promoters and 3’ UTRs. • How? By comparing sequence motifs of several mammals. That’s why it is called comparative motif finding. • Which mammals? Human, mouse, rat, dog.

  9. Conservation properties

  10. Methods • Chose 17,700 well annotated genes from RefSeq database. • Promoters = 4kb centered at transcriptional start site (only noncoding) • 3-UTRs = based on annotation of reference mRNA • Intronic sequences as a control (last two introns from each gene)

  11. Motif Conservation Score • A motif is said to be conserved when an exact match is found in all 4 species. • Conservation =conserved occurrences/all occurrences • MCS = Observed conservation – random conservation Standard deviation

  12. Known highly conserved motif • Err α [TGACCTTG] • Of the 434 times err α occurs in human promoter regions, 162 of them are conserved across all the 4 species. • Conservation rate = 37% • Random 8-mer motif shows only 6.8% conservation rate

  13. Results: Promoter Region • 174 highly conserved motifs (MCS > 6) • 59 strong match to known motifs, 10 weaker match. • 105 potential new regulatory motifs

  14. Approaches to explore biological significance • So why is the motif biologically significant? 1. tissue specificity 2. positional bias

  15. Tissue Specificity • Tissue specificity of expression for genes containing discovered motifs • Expression data for 75 tissues • 59 of 69 known, and 53 of 105 unknown show tissue specificity

  16. Position Bias • Motifs show position bias • Conserved motifs show strong position bias • Preferential occurrence within 100bases of TSS

  17. Results: motifs in 3’ UTRs • In UTR 106 conserved motifs found (MCS>6) • 3’-UTR motifs have not studied before • Comparison of discovered motifs to a large collection of previously known motifs not possible • Two unique properties • Strand specificity • Bias towards 8-mers

  18. Property1: strand specificity Xie, X. et al., Nature, 2005

  19. Property2 : bias towards 8-mers Xie, X. et al., Nature, 2005

  20. Digression: miRNA • Single stranded RNA • transcribed from DNA but not translated into protein • Many mature miRNA start with U followed by a 7-base “seed” complementary to a site in the 3’ UTR of target mRNAs. • Thus many are 8 mers microRNA that regulates insulin secretion by an NYU study published in Nature.

  21. Inference • Thus we can infer many of the conserved 8-mer motifs act as binding sites for miRNA • Leads to discovery of 52% existing miRNA genes • Leads to discovery of 129 new miRNA genes

  22. Phylogenetic Footprinting

  23. Problem Definition (why?) • Major challenge of current genomics is to understand how gene expression is regulated. • An important step towards this understanding is the capability to identify regulatory elements.

  24. What? • Phylogenetic footprinting is 1. method for the discovery of regulatory elements 2. in orthologous regulatory regions 3. from multiple species.

  25. Image source: http://www.biorecipes.com/Orthologues/code.html

  26. Main idea • Coding sequences evolving at a slower rate than non-coding sequences cause selective pressure • Transition in a coding sequence can possibly alter the whole function of coded protein • Transition in a non-coding sequence (RE) may only change expression frequency of a gene

  27. Phylogenetic Footprinting • Study orthologous non-coding DNA from species that are related (phylogenetic tree) Differentiation: • Tree • Find one motif in many species Well conserved = possible Regulatory Element

  28. Formalization Given: • phylogenetic tree T, • set of orthologous sequences at leaves of T, • length k of motif • threshold d Problem: • Find each set Sof k-mers, one k-mer from each leaf, such that the “parsimony” score of S in T is at most d.

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

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

  31. … ACGG: +ACGT: 0 ... … ACGG:ACGT :0 ... … ACGG:ACGT :0 ... … ACGG:ACGT :0 ... … ACGG: 1 ACGT: 0 ... … ACGG: 2ACGT: 1 ... … ACGG: 1ACGT: 1 \... … ACGG: 0ACGT: 2 ... … ACGG: 0 ACGT: + ... An Exhaustive Algorithm Wu[s] = best parsimony score for subtree rooted at node u, if u is labeled with string s. AGTCGTACGTG ACGGGACGTGC ACGTGAGATAC GAACGGAGTAC TCGTGACGGTG

  32. Wu[s] =  min ( Wv[t] + h(s, t) ) v:children t ofu Simple Recurrence Words Good: K-mer score at a node is the sum of its children’s best parsimony scores for that k-mer

  33. Wu[s] =  min ( Wv[t] + h(s, t) ) v:children t ofu Average sequence length Number of species Total time O(n k(42k + l)) Motif length Running Time O(k 42k )timeper node

  34. Results • Metallothionein Gene Family • Insulin Gene Family • C-myc promoter

  35. Metallothionein Gene Family • Large number of promoter sequences • Large number of RE • Binding sites occurs within 300 bp of start codon • 590 bp of sequence located upstream of start codon • Conserved elements of lengths 7,8,9,10 (K values) • Identified 12 motifs of which 4 have been confirmed Analysis

  36. Insulin Gene Family • two rodents and a pig (two gene copies each) • motifs with 0 mutations, K=8 • motifs with 1 mutation, K=9,10 • 4 conserved motifs identified • Several binding sites missed as they contain very few mutations Analysis

  37. C-myc Promoter • 7 species analyzed • Contains members from diverse animal phyla (fishes, birds, mammals, batrachians) • 4 of 9 predictions known are binding sites • Most located in 120 bp promoter region Analysis

  38. Drawbacks • Some binding sites does not have significant matches to most other species • Some binding sites show good conservation rate in sequences shorter than footprinter looked at T3R

  39. Drawbacks cont’d • Deletions/Insertions • Failure to meet statistical significance • Some TFs bind as dimers where the binding site may consist of 2 conserved regions, separated by a few variable nucleotides

  40. Thank You

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