Sequence Comparison

1. Sequence Comparison Intragenic - self to self. -find internal repeating units. Intergenic -compare two different sequences. Dotplot - visual alignment of two sequences Multiple Sequence Alignment -Two or more sequences

2. Overview • Why compare sequences • Homology vs. identity/similarity • DotPlots • Scoring • Match • Mismatch • Gap penality • Global vs. local alignment • Do the results make biological sense?

3. Why Align Sequences • Identify conserved sequences

4. Why Align Sequences • Identify conserved sequences • Identify elements that repeat in a single sequence.

5. Why Align Sequences • Identify conserved sequences • Identify elements that repeat in a single sequence. • Identify elements conserved between genes.

6. Why Align Sequences • Identify conserved sequences • Identify elements that repeat in a single sequence. • Identify elements conserved between genes. • Identify elements conserved between species.

7. Why Align Sequences • Identify conserved sequences • Identify elements that repeat in a single sequence. • Identify elements conserved between genes. • Identify elements conserved between species. • Regulatory elements

8. Why Align Sequences • Identify conserved sequences • Identify elements that repeat in a single sequence. • Identify elements conserved between genes. • Identify elements conserved between species. • Regulatory elements • Functional elements

9. Underlying Hypothesis?

10. Underlying Hypothesis? EVOLUTION

11. Underlying Hypothesis? EVOLUTION Based upon conservation of sequence during evolution we can infer function.

12. Basic terms: • Similarity - measurable quantity. • Similarity- applied to proteins using concept of conservative substitutions • Identity • percentage • Homology-specific term indicating relationship by evolution

13. Basic terms: • Orthologs: homologous sequences found in two or more species, that have the same function (i.e. alpha- hemoglobin).

14. Basic terms: • Orthologs: homologous sequences found it two or more species, that have the same function (i.e. alpha- hemoglobin). • Paralogs: homologous sequences found in the same species that arose by gene duplication. ( alpha and beta hemoglobin).

15. Pairwise comparison • Dotplot • All against all comparison. • Every position is compared with every other position.

16. Pairwise comparison • Dotplot • All against all comparison. • Every position is compared with every other position. • Nucleic acids and proteins have polarity.

17. Pairwise comparison • Dotplot • All against all comparison. • Every position is compared with every other position. • Nucleic acids and proteins have polarity. • Typically only one direction makes biological sense.

18. Pairwise comparison • Dotplot • All against all comparison. • Every position is compared with every other position. • Nucleic acids and proteins have polarity. • Typically only one direction makes biological sense. • 5’ to 3’ or amino terminus to carboxyl terminus.

19. DotPlot • Dotplot- matrix, with one sequence across top, other down side. Put a dot, or 1, where ever there is identity.

20. DotPlot • Dotplot- matrix, with one sequence across top, other down side. Put a dot, or 1, where ever there is identity. G A T C T G A T C T

21. DotPlot • Dotplot- matrix, with one sequence across top, other down side. Put a dot, or 1, where ever there is identity. . G A T C T G A T C T

22. DotPlot • Dotplot- matrix, with one sequence across top, other down side. Put a dot, or 1, where ever there is identity. . . G A T C T G A T C T

23. DotPlot • Dotplot- matrix, with one sequence across top, other down side. Put a dot, or 1, where ever there is identity. . . G A T C T . . G A T C T

24. DotPlot • Dotplot- matrix, with one sequence across top, other down side. Put a dot, or 1, where ever there is identity. . . G A T C T . . G A T C T .

25. DotPlot • Dotplot- matrix, with one sequence across top, other down side. Put a dot, or 1, where ever there is identity. . . G A T C T . . G A T C T . . .

27. Simple plot • Window: size of sequence block used for comparison. In previous example: • window = 1 • Stringency = Number of matches required to score positive. In previous example: • stringency = 1 (required exact match)

31. Dot Plot • Compare two sequences in every register. • Vary size of window and stringency depending upon sequences being compared. • For nucleotide sequences typically start with window = 21; stringency = 14

32. DotPlot WINDOW = 4; STRINGENCY = 2 GATCGTACCATGGAATCGTCCAGATCA GATC + (4/4) GATC - (0/4) GATC - (0/4) GATC + (2/4)

33. This “match” from G and C out of the four

34. Top 3 Rows

35. Intragenic Comparison • Rat Groucho Gene

39. Intergenic Comparison • Rat and Drosophila Groucho Gene

41. Intergenic comparison • Nucleotide sequence contains three domains.

42. Intergenic comparison • Nucleotide sequence contains three domains. • 50 - 350 - Strong conservation • Indel places comparison out of register

43. Intergenic comparison • Nucleotide sequence contains three domains. • 50 - 350 - Strong conservation • Indel places comparison out of register • 450 - 1300 - Slightly weaker conservation

44. Intergenic comparison • Nucleotide sequence contains three domains. • 50 - 350 - Strong conservation • Indel places comparison out of register • 450 - 1300 - Slightly weaker conservation • 1300 - 2400 - Strong conservation

45. Groucho • These three coding regions correspond to apparent functional domains of the encoded protein

46. Scoring Alignments • Quality Score: • Score x for match, -y for mismatch;

47. Scoring Alignments • Quality Score: • Score x for match, -y for mismatch; • Penalty for: • Creating Gap • Extending a gap

48. Scoring Alignments • Quality Score: • Quality = [10(match)]

49. Scoring Alignments • Quality Score: • Quality = [10(match)] + [-1(mismatch)]

50. Scoring Alignments • Quality Score: • Quality = [10(match)] + [-1(mismatch)] - [(Gap Creation Penalty)(#of Gaps)