Tutorial #2

1. Tutorial #2

2. Quiz next week • Cover everything you’ve seen in the course so far • Combination of True/False, definition, short answer, or some similar question from the problem set

3. How to design a PCR primer? • Primer length and sequence are of critical importance in designing the parameters of a successful amplification • A simple formula for calculating the Tm Tm = 4(G + C) + 2(A + T) • When designing a PCR primer, Tm is not the only thing, should also consider; the GC content, any secondary structure or hairpin loop

4. Example Design PCR primer to amplify IFI16 (interferon, gamma-inducible protein 16)

15. NCBI

21. Synonymous Vs Nonsynonymous • When studying the evolutionary divergences of DNA sequence • Synonymous = silent • Nonsynonymous = amino acid altering • The rates of these nucleotide substitution maybe used as a molecular clock for dating the evolutionary time of closely related species

22. Calculating Synonymous sites (s) and nonsynonymous sites (n) • Each codon has 3 nucleotides, denote by fi (I = 1,2,3) • Where s and n for a codon are given by s = ∑3i=1fiand n = (3-s) Ex. TTA (Leu) f1=1/3 (T→C) f2=0 f3=1/3 (A→G) Thus, s = 2/3 and n = 7/3 • For DNA sequence of r codons, it will be s = ∑ri=1siand n = (3r-s), where siis the value of s for the ith codon

23. Calculation of s and n for 2 nucleotide differences between 2 codons Ex. GTT (Val) and GTA (Val) 1 synonymous difference Denote sdand nd the number of synonymous and nonsynonymous differences per codon, respectively sd = 1 nd = 0

24. Con’t Ex. TTT and GTA, 2 pathways to get there Pathway #1: TTT(Phe)↔GTT(Val)↔GTA(Val) Pathway #2: TTT(Phe)↔TTA(Leu)↔GTA(Val) Pathway 1 involve 1 synonymous and 1 nonsynonymous substitution Pathway 2 involve 2 nonsynonymous substitution sd = 1 synonymous substitution / 2 change state = 0.5 nd = 3 nonsysnonymous substitution / 2 change state =1.5 D in the problem set = proportion of synonymous or nonsynonymous differences, therefore, for this nonsynonymous site, the Dn would be 1 / 1.5 = 0.667 Note thatsd + ndis equal to the total number of nucleotide differences between the two DNA sequences compared

25. Sequence Alignment • Every alignment will have a scoring system • Base change cost = 1 • Gap cost = 2 • Gap extension cost = 1 • Ex. ACT GTT GCC AG - C - - GCT Score of this alignment would be 3 + 2x2 + 1 = 8 In this case, a higher score means a worst alignment

26. MLST - Methods • Isolate multiple strains of species of interest • PCR ~500bp regions of 4-20 housekeeping genes (“loci”) • Sequence PCR products • Assign “allele numbers” to each locus • Arbitrary, each # represents a different sequence 2 3 1 2 1 1 1 2 1

27. 2 3 1 2 1 1 1 2 1 MLST - Methods • Collate the information into a table • Row = isolate • Column = loci • Fill in allele numbers

28. MLST of a Halorubrum Population • 36 isolates • 4 housekeeping genes • atpB • ef-2 • radA • secY • 500bp PCR product • Allelic profiles vary • Few identical pairs • All loci polymorphic • 8-15 alleles

29. Insights from the MLST Data - 1 How genetically diverse is the saltern Archaeal population? Genetic diversity H = 1-Σxi2 • Overall genetic diversity = 0.69 • Varied between ponds of different salinity • 0.57 in 23% saline pond • 0.83 in 36% saline pond • Higher than E. coli diversity of 0.47 • >5x higher than eukaryotic diversity

30. Insights from the MLST Data - 2 Is recombination occurring in the Archaea? Linkage disequilibrium calculator – mlst.net • LD = Alleles are linked and are transferred together during recombination • LE = Alleles are not linked and recombination scatters them randomly • Halorubrum population is near linkage equilibrium • Suggests recombination is occurring

31. Tetraodon Nigroviridis 2X? Nature Reviews Genetics3; 838-849 (2002);

32. Phylogenetic tree • Phylogenetics is the field of systematics that focuses on evolutionary relationship between organisms or genes/proteins (phylogeny) A node Human Mouse Fly A clade clade -- A monophyletic taxon taxon -- Any named group of organisms, not necessarily a clade.

33. A phylogenetic tree A node A+B+C is less than D+B+C So the mouse Sequence is more related to fly than the human sequence is to fly in this example Human Mouse Fly D A C A clade B

34. Tetraodon gene evolution • Fourfold degenerate (4D) site substitution - a mesure of neutral nucleotide mutations • 4D site = 3rd base of codon free to change with no FX on AA • # of AA changes at these sites = neutral mutations • Fish proteins have diverged faster vs. mammalian homologues Figure 3

35. Brief generalization of the papers • Comparative genomics help identifying region of DNA that are shared between two different species and allows the transfer of information between both species in the common region. • It can also detect regions that have gone through chromosomes rearrangement occurring in many different diseases. This information can be of different type. • 1) Using one of the species it is possible to transfer annotation information that were not known in the other species, • 2) identify region that are under selective pressure, • 3) It is also possible to compare for examples regions that have gone through chromosomes rearrangement with annotation genes map to identify genes responsible for a particular disease

36. Homologs Have common origins but may or may not have common activity • Orthologs – Homologs produced by speciation. They tend to have similar function • Paralogs – Homologs produced by gene duplication. They tend to have differing function • Xenologs – Homologs resulting from horizontal gene transfer between two organism

38. BLAST • Basic Local Alignment Search Tool • Developed in 1990 and 1997 (S. Altschul) • A heuristic method (Fast alignment method) for performing local alignments through searches of high scoring segment pairs (HSP’s) • 1st to use statistics to predict significance of initial matches - saves on false leads • Offers both sensitivity and speed

39. BLAST • Looks for clusters of nearby or locally dense “similar or homologous” k-tuples • Uses “look-up” tables to shorten search time • Uses larger “word size” than FASTA to accelerate the search process • Can generate “domain friendly” local alignments • Fastest and most frequently used sequence alignment tool –BECAME THE STANDARD

41. Connecting HSP’s

42. -x -e P(x) = 1 - e Extreme Value Distribution = • Kmne-lS is called Expect or E-value • In BLAST, default E cutoff = 10 so P = 0.99995 • If E is small then P is small • Why does BLAST report an E-value instead of a p value? • E-values of 5 and 10 are easier to understand than P-values of 0.993 and 0.99995. • However, note that when E < 0.01, P-values and E-value are nearly identical.

43. Expect value • Kmne-lS = Expect or E-value What parameters does it depend on? - l and K are two parameters – natural scales for search space size and scoring system, respectively • l = lnq/p and K = (q-p)2/q • p = probability of match (i.e. 0.05) • q = probability of not match (i.e. 0.95) • Then l = 2.94 and K =0.85 • p and q calculated from a “random sequence model” (Altschul, S.F. & Gish, W. (1996) "Local alignment statistics." Meth. Enzymol. 266:460-480.) based on given subst. matrix and gap costs - m = length of sequence - n = length of database - S = score for given HSP

44. Expect value • Expect value an intuitive value but… • Expect value changes as database changes • Expect value becomes zero quickly • Alternative: bit score S' (bits) = [lambda * S (raw) - ln K] / ln 2 • Independent of scoring system used - normalized • Larger value for more similar sequences, therefore useful in analyses of very similar sequences

46. Similarity by chance – the impact of sequence complexity MCDEFGHIKLAN…. High Complexity ACTGTCACTGAT…. Mid Complexity NNNNTTTTTNNN…. Low Complexity Low complexity sequences are more likely to appear similar by chance