Tutorial #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

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

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

NCBI

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

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

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

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 s d = 1 synonymous substitution / 2 change state = 0.5 n d = 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 = Note that s d + n d is equal to the total number of nucleotide differences between the two DNA sequences compared

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

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

MLST - Methods Collate the information into a table  Row = isolate  Column = loci  Fill in allele numbers Locus A Locus B Locus C Isolate Isolate Isolate 3 312

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

Insights from the MLST Data - 1 Genetic diversity H = 1-Σx i 2 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 Higher than E. coli diversity of 0.47 >5x higher than eukaryotic diversity >5x higher than eukaryotic diversity How genetically diverse is the saltern Archaeal population?

Insights from the MLST Data - 2 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 Halorubrum population is near linkage equilibrium Suggests recombination is occurring Suggests recombination is occurring Is recombination occurring in the Archaea?

Nature Reviews Genetics 3; (2002); 2X? Tetraodon Nigroviridis

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

A phylogenetic tree Human Mouse Fly A clade A node DACDAC 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 B

Tetraodon gene evolution Fourfold degenerate (4D) site substitution - a mesure of neutral nucleotide mutations  4D site = 3 rd 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

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

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

BLAST B asic L ocal A lignment S earch T ool 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

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 BLAST

Connecting HSP ’ s

Extreme Value Distribution Kmne - S is called Expect or E-value In BLAST, default E cutoff = 10 so P = 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 and  However, note that when E < 0.01, P-values and E- value are nearly identical. P(x) = 1 - e -e -x =

Expect value Kmne - S = Expect or E-value What parameters does it depend on? - and K are two parameters – natural scales for search space size and scoring system, respectively   = 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 = 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: ) based on given subst. matrix and gap costs - m = length of sequence - n = length of database - S = score for given HSP

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

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