Transcript tutorialdm

Tutorial #2
Quiz next week
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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?
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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)
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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
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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)
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Each codon has 3 nucleotides, denote by fi (I = 1,2,3)
Where s and n for a codon are given by
s = ∑3i=1fi and n = (3-s)
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=1si and n = (3r-s),
where si is the value of s for the ith codon
Ex. TTA (Leu)
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Calculation of s and n for 2 nucleotide
differences between 2 codons
Ex. GTT (Val) and GTA (Val)
1 synonymous difference
Denote sd and nd the number of synonymous and
nonsynonymous differences per codon, respectively
sd = 1
nd = 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
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 that sd + nd is equal to the total number of nucleotide differences
between the two DNA sequences compared
Sequence Alignment
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Every alignment will have a scoring system
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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
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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
1
2
3
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2
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MLST - Methods
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Collate the information into a table
 Row = isolate
 Column = loci
 Fill in allele numbers
1
2
3
1
2
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1
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Isolate 1
Isolate 2
Locus A
1
2
Locus B
1
2
Locus C
1
1
Isolate 3
3
1
2
MLST of a Halorubrum Population
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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
How genetically diverse is the saltern
Archaeal population?
Genetic diversity H = 1-Σxi2
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Overall genetic diversity = 0.69
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Varied between ponds of different salinity
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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
Insights from the MLST Data - 2
Is recombination occurring in the Archaea?
Linkage disequilibrium calculator – mlst.net
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LD = Alleles are linked and are transferred together
during recombination
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LE = Alleles are not linked and recombination scatters
them randomly
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Halorubrum population is near linkage equilibrium
Suggests recombination is occurring
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Tetraodon
Nigroviridis
2X?
Nature Reviews
Genetics 3; 838849 (2002);
Phylogenetic tree
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Phylogenetics is the field of systematics that focuses on
evolutionary relationship between organisms or
genes/proteins (phylogeny)
A node
Human
A clade
Mouse
Fly
clade -- A monophyletic taxon
taxon -- Any named group of organisms, not necessarily a clade.
A phylogenetic tree
A node
D
B
A
C
A+B+C is less than
D+B+C
Human
A clade
Mouse
Fly
So the mouse
Sequence is more
related to fly than
the human sequence
is to fly in this
example
Tetraodon gene evolution
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Fourfold degenerate (4D) site
substitution - a mesure of
neutral nucleotide mutations
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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
Brief generalization of the papers
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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
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Basic Local Alignment Search Tool
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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
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BLAST
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Looks for clusters of nearby or locally dense
“similar or homologous” k-tuples
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Uses “look-up” tables to shorten search time
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Uses larger “word size” than FASTA to
accelerate the search process
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Can generate “domain friendly” local
alignments
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Fastest and most frequently used sequence
alignment tool – BECAME THE STANDARD
Connecting HSP’s
Extreme Value Distribution
-x
P(x) = 1 - e -e
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=
P(x) = 1 – e
-(Kmne-lS)
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 Pvalues of 0.993 and 0.99995.
 However, note that when E < 0.01, P-values and Evalue are nearly identical.
Expect value
Kmne-lS = Expect or E-value
What parameters does it depend on?
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- 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
Expect value
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Expect value an intuitive value but…
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Expect value changes as database changes
Expect value becomes zero quickly
Alternative: bit score
S' (bits) = [lambda * S (raw) - ln K] / ln 2
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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