In this presentation we will see what is CpG Island, why is it important to us and why do we use Hidden Markov Model for its identification.

1. CpG Island identification
with Hidden Markov
Models
!
- Kshitij Tayal
1

2. CpG Island
• Region of the genome with high frequency of CpG
sites than the rest of the genome.
• Formal Definition - CpG island is a region with at
least 200 bp, and a GC percentage that is greater
than 50 % .
• CpG is shorthand for “—C—phosphate—G—that
is, cytosine and guanine separated by only one
phosphate.
2

3. Genome ~ 3 billion
characters. Find gene ?
3

4. Importance of CpG Islands
• CpG island acts as a proxy to
identify a gene.
• They often occur at the start of
the gene.
• Cytosines in CpG
dinucleotides can be
methylated(have methyl group
attache) to form 5-
methylcytosine.
4

5. 5

6. Importance of Methylation
• Our body consist thousand of cell . Every cell of our body
contain same copy of DNA with same blueprint of genetic code,
then how do they decide among themselves which function has
to performed ?
• How Does heart cell know it’s a heart cell
• How Does skin cell know it’s skin cell.
• They need outside instructions from these little carbon hydrogen
compounds called methyl group.
• How characteristics change across generations without changes
to the DNA sequence itself.
6

7. Epigenetics & CpG Islands
• Literal meaning of epigenetic is ‘above genetics’. It
decides methylation of CpG island
• CpG islands regulate expression of nearby genes.
• Proteins involved in
gene expression
can be repelled or
attracted by the
methyl group
7

8. Background: Epigenetics
• Environmental factors like what we do, what we eat, what we
smoke and how stressed we are decide the methyl group binding.
• Bad diet can actually lead methyl group binding to the wrong
place and with these bad instruction cell become abnormal and
become disease
• Epigenetics is also controlled by histones. Histones are protein that
are basically spools that DNA wind itself around . Histones can
change how tightly or loosely the DNA is around them.
• If loosely around — the gene get more expressed
• If tightly around — the gene get less expressed
8

9. 9

10. Background: Epigenetics
• So methyl group is more like a ‘switch’ and histones
are more like a ‘knob’
• Every cell of your body has a distinct methylation
and histones pattern that gives every cell its
marching order.
• DNA can be thought of as body ‘hardware’ and
epigenome is more like a software which tells the
hardware what work it has to do and hence justifies
its meaning.
10

11. Now Some Computer
Science……..
• Task - Design a method that, given a candidate
string (k-mer), score it according to how confident it
came from CpG Island.
• Apply, Sequence Model which is a probabilistic
model that associates probabilities with
sequences.
11

12. Sequence Models
• Sequence models learn from examples.
• Say we have sampled 100K 5-mers from inside
CpG islands and 100K 5-mers from outside.
• Can we guess whether CGCGC came from CpG
island.?
• P(inside) = 315/(315 + 12)
12
# CGCGC inside 315
# CGCGC outside 12

13. Sequence Models
• To estimate p(x) we count # times x appears in the
training set labelled INSIDE divided by total # of
times x appears in training set.
• But for sufficiently long k, we might not see any
occurrences of x, or very few.To overcome this
limitation we will go for joint probability distribution.
• P(X) = P(Xk,Xk-1,………X1) where P(X) is the
probability of sequence X
13

14. 14

15. 15

16. 16

17. • P(x) now equal product
of all the Markov chain
edge weights on our
string driven walk
through the chain
!
!
• Nodes label are symbol
and transition label are
conditional probability
17

18. 18

19. 19

20. 20

21. Hidden Markov Model
• In simpler Markov models (like a Markov chain), the
state is directly visible to the observer, and
therefore the state transition probabilities are the
only parameters.
• In a hidden Markov model, the state is not directly
visible, but the output, dependent on the state, is
visible. Each state has a probability distribution
over the possible output tokens. The adjective
'hidden' refers to the state sequence through which
the model passes.
21

22. 22

23. 23

24. 24

25. 25

26. 26

27. 27

28. 28
Hidden Markov Model-
Viterbi Algorithm
• Given flips can we say when the dealer was using
loaded coin.
• We want to find p* , the most likely path given the
emission.
!
• Viterbi algorithm is a dynamic programming algorithm
for finding the most likely sequence of hidden states –
called the Viterbi path – that results in a sequence of
observed events.

29. 29

30. 30

31. 31

32. 32

33. 33

34. 34

35. 35

36. 36

37. 37

38. 38

39. 39

40. Hidden Markov Model
40

41. Hidden Markov Model
41

42. 42
EMISSIONS

43. 43

44. 44
Hidden Markov Model

45. 45

46. 46

47. THANK YOU
47