Distances

1. Distances

2. A natural or ideal measure of distance between two sequences should have an evolutionary meaning. One such measure may be the number of nucleotide substitutions that have accumulated in the two sequences since they have diverged from each other.

5. To derive a measure of distance, we need to make several simplifying assumptions regarding the probability of substitution of a nucleotide by another.

7. Jukes & Cantor one-parameter model

8. Assumption: • Substitutions occur with equal probabilities among the four nucleotide types.

9. Kimura’s two-parameter model

10. Assumptions: • The rate of transitional substitution at each nucleotide site is  per unit time. • The rate of each type of transversional substitution is  per unit time.

12. NUMBER OF NUCLEOTIDE SUBSTITUTIONS BETWEEN TWO DNA SEQUENCES

13. After two nucleotide sequences diverge from each other, each of them will start accumulating nucleotide substitutions. If two sequences of length N differ from each other at n sites, then the proportion of differences, n/N, is referred to as the degreeof divergence or Hamming distance. Degrees of divergence are usually expressed as percentages (n/N 100%).

15. The observed number of differences is likely to be smaller than the actual number of substitutions due to multiple hits at the same site.

16. 13 mutations=3 differences

18. Number of substitutions between two noncoding sequences

19. The one-parameter model In this model, it is sufficient to consider only I(t), which is the probability that the nucleotide at a given site at time t is the same in both sequences.

20. where p is the observed proportion of different nucleotides between the two sequences.

21. L = number of sites compared in the ungapped alignment between the two sequences.

22. The two-parameter model

23. The differences between two sequences are classified into transitions and transversions. P = proportion of transitional differencesQ = proportion of transversional differences ATCGG ACCCG Q = 0.2 P = 0.2

27. Numerical example (2P-model)

28. Substitution schemes with more than two parameters.- Parameter-free substitution schemes.

29. Number of substitutions between two protein-coding genes

32. Difficulties with denominator: 1. The classification of a site changes with time: For example, the third position of CGG (Arg) is synonymous. However, if the first position changes to T, then the third position of the resulting codon, TGG (Trp), becomes nonsynonymous.

33. T Trp Nonsynonymous

34. Difficulties with denominator: 2. Many sites are neither completely synonymous nor completely nonsynonymous. For example, a transition in the third position of GAT (Asp) will be synonymous, while a transversion to either GAG or GAA will alter the amino acid.

36. Difficulties with nominator:1. The classification of the change depends on the order in which the substitutions had occurred.

38. Difficulties with nominator:2. Transitions occur with different frequencies than transversions. 3. The type of substitution depends on the mutation. Transitions result more frequently in synonymous substitutions than transversions.

40. Miyata & Yasunaga (1980)andNei & Gojobori (1986)method

41. Step 1: Classify Nucleotides into non-degenerate, twofold and fourfold degenerate sites L0 L2 L4

45. Number of Amino-Acid Replacements between Two Proteins • The observed proportion of different amino acids between the two sequences (p) is p = n /L • n = number of amino acid differences between the two sequences • L = length of the aligned sequences.

46. Number of Amino-Acid Replacements between Two Proteins The Poisson model is used to convert p into the number of amino replacements between two sequences (d ): d = - ln(1 – p) The variance of d is estimated as V(d) = p/L (1 – p)

47. How do you detect adaptive evolution at the genetic level?

48. Theoretical Expectations Neutral mutations Deleterious mutations Overdominant mutations Advantageous mutations