1. Patterns, Profiles, and
Multiple Alignment

2. OUTLINE Profiles and Sequence Logos Profile Hidden Markov Models
Aligning Profiles
Multiple Sequence Alignments by Gradual Sequence Adition
Other Ways of Obtaining Multiple Alignments
Sequence Pattern Discovery

3. OUTLINE Profiles and Sequence Logos Profile Hidden Markov Models
Aligning Profiles
Multiple Sequence Alignments by Gradual Sequence Adition
Other Ways of Obtaining Multiple Alignments
Sequence Pattern Discovery

4. Profile Hidden Markov Models
Hidden Markow Models:
A hidden Markov model (HMM) is a statisticsl model,
in which the system being modeled is assumed to be a Markov process (Memoryless process: its future and past are independent ),
with hidden states.

5. Profile Hidden Markov Models
Hidden Markow Models:
Has a set of states each of which has limited number of transitions and emissions,
Each transition between states has an assisgned probability,
Each model strarts from start state and ends in end state,

6. Profile Hidden Markov Models
Hidden Markow Models parameters:
A set of finite number of states, Si,
The transition probability from state Si to Sj, aij,
The emission probability density of a symbol ω
in state Si

7. Profile Hidden Markov Models
Hidden Markow Models parameters:
Firstly discuss:
Morkov Models,
Markov Assumption

8. Profile Hidden Markov Models
Markow Models and Assumption (cont.):
To understand HMMs:
Talk about weather,
Assume there are three types of weather:
Sunny,
Rainy,
Foggy.
Assume weather does not change during the day (if it is sunny it will sunny all the day)

9. Profile Hidden Markov Models
Markow Models and Assumption (cont.): Weather prediction is about the what would be the weather tomorrow,
Based on the observations on the past.

10. Profile Hidden Markov Models
Markow Models and Assumption (cont.): Weather at day n is
qn depends on the known weathers of the past days (qn-1, qn-2,…)

11. Profile Hidden Markov Models
Markow Models and Assumption (cont.): We want to find that:
means given the past weathers what is the probability of any possible weather of today.

12. Profile Hidden Markov Models
Markow Models and Assumption (cont.): For example:
if we knew the weather for last three days was:
the probability that tomorrow would be is:
P(q4 = | q3 = , q2 = , q1 = )

13. Profile Hidden Markov Models
Markow Models and Assumption (cont.):
For example:
this probability could be infered from the statistics of past observations
the problem: the larger n is, the more observations we must collect.
for example: if n = 6 we need 3(6-1) = 243 past observations.

14. Profile Hidden Markov Models
Markow Models and Assumption (cont.):
Therefore, make a simplifying assumption Markov assumption:
For sequence:
the weather of tomorrow only depends on today (first order Markov model)

15. Profile Hidden Markov Models
Markow Models and Assumption (cont.): Examples:
The probabilities table:

16. Profile Hidden Markov Models
Markow Models and Assumption (cont.): Examples:
HMM:

17. Profile Hidden Markov Models
Markow Models and Assumption (cont.): Examples:
Given that day the weather is sunny, what is the probability that tomorrow is sunny and the next day is rainy ?
Markov assumption

18. Profile Hidden Markov Models
Markow Models and Assumption (cont.): Examples:
If the weather yesterday was rainy and today is foggy what is the probability that tomorrow it will be sunny?

19. Profile Hidden Markov Models
Markow Models and Assumption (cont.):
Examples:
If the weather yesterday was rainy and today is foggy what is the probability that tomorrow it will be sunny?
Markov assumption

20. Profile Hidden Markov Models
Hidden Markov Models (HMMs):
What is HMM:
Suppose that you are locked in a room for several days,
you try to predict the weather outside,
The only piece of evidence you have is whether the person who comes into the room bringing your daily meal is carrying an umbrella or not.

21. Profile Hidden Markov Models
Hidden Markov Models (HMMs):
What is HMM (cont.):
assume probabilities as seen in the table:

22. Profile Hidden Markov Models
Hidden Markov Models (HMMs):
What is HMM (cont.):
Now the actual weather is hidden from you.
You can not directly see what is the weather.

23. Profile Hidden Markov Models
Hidden Markov Models (HMMs):
What is HMM (cont.):
Finding the probability of a certain weather
is based on the observations xi:

24. Profile Hidden Markov Models
Hidden Markov Models (HMMs):
What is HMM (cont.):
Using Bayes rule:
For n days:

25. Profile Hidden Markov Models
Hidden Markov Models (HMMs):
What is HMM (cont.):
We can omit So:
With Markov assumptions:

26. Profile Hidden Markov Models
Hidden Markov Models (HMMs):
Examples:
Suppose the day you were locked in it was sunny. The next day, the caretaker carried an umbrella into the room.
You would like to know, what the weather was like on this second day.

27. Profile Hidden Markov Models
Hidden Markov Models (HMMs):
Examples:
Calculate 3 probabilities:

28. Profile Hidden Markov Models
Hidden Markov Models (HMMs):
Examples:
Consider the event with highest value. It is most likely to happen.

29. Profile Hidden Markov Models
Hidden Markov Models (HMMs):
Another Examples:
Suppose you do not know how the weather was when your were locked in. The following three days the caretaker always comes without an umbrella. Calculate the likelihood for the weather on these three days to have been

30. Profile Hidden Markov Models
Hidden Markov Models (HMMs):
Another Examples:
As you do not know how the weather is on the first day, you assume the 3 weather situations are equi-probable on this day and the prior probability for sun on day one is therefore

31. Profile Hidden Markov Models
Hidden Markov Models (HMMs):
Another Examples:
Assumption:

32. Profile Hidden Markov Models
Another Examples:

33. Profile Hidden Markov Models
HMMs to represent a family of sequences
Given a multiple alignment of sequences, we can use an HMM to model the sequences.
Each column of the alignment may be represented by a hidden state that produced represented by a hidden state that produced that column.
Insertions and deletions can be represented by other states

34. Profile Hidden Markov Models
HMMs to represent a family of sequences

35. Profile Hidden Markov Models
HMMs to represent a family of sequences

36. Profile Hidden Markov Models
HMMs to represent a family of sequences

37. Profile Hidden Markov Models
Determining the states of the HMM
The structure is usually fixed and only the number of “match” states is to be determined

38. Profile Hidden Markov Models
Determining the states of the HMM
An alignment column with no gaps can be considered as a “match” state considered as a match state.
An alignment column with a majority of gaps can be considered an “insert” state can be considered an insert state.

39. Profile Hidden Markov Models
Determining the transition probabilities
From a state u the transition to another state v is represented by t(u.v).
The summation over all states w that are connected to state u by transition gives one:
The transition probabilities from a state (except the end state) always add up to 1.

40. Profile Hidden Markov Models
Determining the transition probabilities

41. Profile Hidden Markov Models
Determining the transition probabilities
mu,v: number of transitions from state u to state v
Transition probabilities t(u,v) can be estimated:

42. Profile Hidden Markov Models
Determining the emission probabilities
Emission probabilities in a match or insert state also adds up to 1.

43. Profile Hidden Markov Models
Determining the emission probabilities
eMu: emission probability for a residue from the u th match state,

44. Profile Hidden Markov Models
Determining the emission probabilities
eIu: emission probability for a residue from the u th insert state,
The probabilities are usually taken from the overal amino acid comparison of a selected data set.

45. Profile Hidden Markov Models
HMMs examples:

46. Profile Hidden Markov Models
HMMs examples:

47. Profile Hidden Markov Models
HMMs examples:
5 transitions in gap region:
C  out,
G  out
AC,
CT,
T out
Out transition 3/5
Stay transition 2/5
gap region

48. Profile Hidden Markov Models
Scoring a sequence against a profile HMM
Given a profile HMM, any given path through the model will emit a sequence with an associated probability,
The path probability is the product of all transition and emission probabilities along the path.

49. Profile Hidden Markov Models
Scoring a sequence against a profile HMM
Viterbi algorithm:
Given a query sequence we can compute the most probable path that will emit that query sequence.

50. Profile Hidden Markov Models
Scoring a sequence against a profile HMM
Viterbi algorithm:
Another interesting question: What is the probability that a given sequence can be generated by the hidden Markov model
Solution:Calculated by summing over all possible path giving rise to a given sequence

51. Profile Hidden Markov Models
Scoring a sequence against a profile HMM
Viterbi algorithm:
Will be applied to profiles HMM similar to:

52. Profile Hidden Markov Models
Scoring a sequence against a profile HMM
Viterbi algorithm:
At a profile position u:
Mu : Match state,
Iu : Insertion state,
Du : Delete state,
t(Mu, Mu+1) : transition probability from state Mu to Mu+1

53. Profile Hidden Markov Models
Scoring a sequence against a profile HMM
Viterbi algorithm:
During the algorithm:
A record must be kept of the highest probability up to that point in the model and for a given amount of emited sequences.

54. Profile Hidden Markov Models
Scoring a sequence against a profile HMM
Viterbi algorithm:
During the algorithm:
When the sequence up to and including residue xi has been emited, the highest probability will be written as VDu(xi)

55. Profile Hidden Markov Models
Scoring a sequence against a profile HMM
Viterbi algorithm:
Dynamic Programming:
COLUMNS:
NUMBER OF STATES IN HMM
ROWS:
LENGTH OF THE QUERY OR EMITTED SEQUENCE

56. Profile Hidden Markov Models
Scoring a sequence against a profile HMM
Viterbi algorithm:
Recurrence relations:

57. Profile Hidden Markov Models
Scoring a sequence against a profile HMM
Viterbi algorithm:
Recurrence relations (log):

58. Profile Hidden Markov Models
Scoring a sequence against a profile HMM
Viterbi algorithm (EXAMPLE):

59. Profile Hidden Markov Models
Scoring a sequence against a profile HMM
Viterbi algorithm (EXAMPLE):
Start Probabilities:
Emission Probabilities:

60. Profile Hidden Markov Models
Scoring a sequence against a profile HMM
Viterbi algorithm (EXAMPLE):

61. Profile Hidden Markov Models
Scoring a sequence against a profile HMM
Viterbi algorithm (EXAMPLE):
GGCTGATT

62. Profile Hidden Markov Models
Scoring a sequence against a profile HMM
Viterbi algorithm (EXAMPLE):
G  G  C T GA T T
BeginM1M2I3I3I3I3I3M3End

63. References
M. Zvelebil, J. O. Baum, “Understanding Bioinformatics”, 2008, Garland Science
Andreas D. Baxevanis, B.F. Francis Ouellette, “Bioinformatics: A practical guide to the analysis of genes and proteins”, 2001, Wiley.
Barbara Resch, “Hidden Markov Models - A Tutorial for the Course Computational Intelligence”, 2010.