Applying
Hidden Markov Models
to
Bioinformatics
Conor
Buckley
Outline
What are Hidden Markov Models?
Why are they a good tool for Bioinformatics?
Applications in Bioinformatics
History of
Hidden
Markov Models
HMM
were first described in a series of statistical papers by
Leonard E. Baum and other authors in the second half of
the 1960s. One of the first applications of HMMs was
speech recogniation, starting in the mid

1970s. They are
commonly used in speech recognition systems to help to
determine the words represented by the sound wave forms
captured
In the second half of the 1980s, HMMs began to be applied
to the analysis of biological sequences, in particular DNA.
Since then, they have become ubiquitous in bioinformatics
Source
: http://en.wikipedia.org/wiki/Hidden_Markov_model#History
What are Hidden Markov Models?
HMM
: A formal foundation for making probabilistic models
of linear sequence 'labeling' problems.
They provide a conceptual toolkit for building complex
models just by drawing an intuitive
picture.
Source: http://www.nature.com/nbt/journal/v22/n10/full/nbt1004

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What are Hidden Markov Models?
Machine learning
approach in bioinformatics
Machine learning algorithms are presented with
training
data
, which are used to derive important insights about the
(often hidden) parameters.
Once an algorithm has been trained, it can apply these
insights to the analysis of a
test sample
As the amount of training data increases, the accuracy of the
machine learning algorithm typically increasess as well.
Source: http://www.nature.com/nbt/journal/v22/n10/full/nbt1004

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Hidden Markov Models
Has N states, called S1, S2, ...
Sn
There are discrete timesteps, t=0, t=1
S1
S2
S3
N = 3
t = 0
Source:
http://www.autonlab.org/tutorials/hmm.html
Hidden Markov Models
Has N states, called S1, S2, ...
Sn
There are discrete timesteps, t=0, t=1
For each timestep, the system is in exactly one of the
available states.
S1
S2
S3
N = 3
t = 0
Hidden Markov Models
S1
S2
S3
Bayesian network
with
time
slices
Bayesian Network Image: http://en.wikipedia.org/wiki/File:Hmm_temporal_bayesian_net.svg
A Markov Chain
Bayes'
Theory
•
(statistics) a theorem describing how the conditional probability of a set of
possible causes for a given observed event can be computed from
knowledge of the probability of each cause and the conditional probability
of the outcome of each cause

http://wordnetweb.princeton.edu/perl/webwn?s=bayes%27%20theorem
Building a Markov Chain
Concrete Example
Two friends, Alice and Bob, who live far apart from each other and who talk
together daily over the telephone about what they did that day.
Bob is only interested in three activities:
walking
in the park,
shopping
, and
cleaning
his apartment.
The choice of what to do is
determined exclusively by the weather on a given day
.
Alice has no definite information about the weather where Bob lives, but
she
knows general trends
.
Based on what Bob tells her he did each day,
Alice tries to guess what the weather
must have been like
.
Alice believes that
the
weather operates as a discrete Markov chain
.
There are two
states, "Rainy" and "Sunny", but she cannot observe them directly, that is, they
are hidden from her.
On each day, there is a certain chance that Bob will perform one of the following
activities, depending on the weather: "walk", "shop", or "clean". Since Bob tells
Alice about his activities, those are the observations.
Source: Wikipedia.org
Hidden Markov Models
Building a Markov Chain
What now?
* Find out the
most probable output sequence
Vertibi's algorithm
Dynamic programming algorithm for finding the most likely
sequence of hidden states
–
called the Vertibi path
–
that results
in a sequence of observed events.
Vertibi Results
http://pcarvalho.com/forward_viterbi/
Bioinformatics Example
Assume we are given a DNA sequence that begins in an exon,
contains one 5' splice site and ends in an intron
Identify where the switch from exon to intron occurs
Where is the splice site??
Sourece: http://www.nature.com/nbt/journal/v22/n10/full/nbt1004

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Bioinformatics Example
In order for us to guess, the sequences of exons, splice sites
and introns must have different statistical properties.
Let's say...
Exons have a uniform base composition on average
A/C/T/G: 25% for each base
Introns are A/T rich
A/T: 40% for each
C/G: 10% for each
5' Splice site consensus nucleotide is almost always a G...
G: 95%
A: 5%
Sourece: http://www.nature.com/nbt/journal/v22/n10/full/nbt1004

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Bioinformatics Example
We can build an
Hidden Markov Model
We have three states
"E" for Exon
"5" for 5' SS
"I" for Intron
Each State has its own
emission probabilities
which model the
base composition of exons, introns and consensus G at the
5'SS
Each state also has
transition probabilities
(arrows)
Sourece: http://www.nature.com/nbt/journal/v22/n10/full/nbt1004

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HMM: A Bioinformatics Visual
We can use HMMs to generate a sequence
When we visit a state, we emit a nucleotide bases on the
emission probability
distribution
We also choose a state to visit next according to the state's
transition
probability distribution.
Source: http://www.nature.com/nbt/journal/v22/n10/full/nbt1004

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We generate two strings of information
Observed Sequence
Underlying State Path
HMM: A Bioinformatics Visual
The state path is a
Markov Chain
Since we're only given the observed sequence, this underlying state
path is a
hidden Markov Chain
Therefore...
We can apply Bayesian Probability
Source: http://www.nature.com/nbt/journal/v22/n10/full/nbt1004

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HMM: A Bioinformatics Visual
S
–
Observed sequence
π
–
State Path
Θ
–
Parameters
The probability P(
S
,
π
HMM,
Θ
) is the product of all emission probabilites and
transition probilities.
Lets look at an example...
Source: http://www.nature.com/nbt/journal/v22/n10/full/nbt1004

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HMM: A Bioinformatics Visual
There are 27 transitions and 26 emissions.
Multiply all 53 probabilities together (and take the log, since these are small
numbers) and you'll calculate log P(
S
,
π
HMM,
Θ
) =

41.22
Source: http://www.nature.com/nbt/journal/v22/n10/full/nbt1004

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HMM: A Bioinformatics Visual
The model parameters and overall sequences scores are all probabilities
Therefore we can use Bayesian probability theory to manipulate these numbers in
standard, powerful ways, including optimizing parameters and interpreting the
signifigance of scores.
Source: http://www.nature.com/nbt/journal/v22/n10/full/nbt1004

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HMM: A Bioinformatics Visual
Posterior Decoding:
An alternative state path where the SS falls on the 6
th
G instead of the 5
th
(log
probabilities of

41.71 versus

41.22)
How confident are we that the fifth G is the right choice?
Source: http://www.nature.com/nbt/journal/v22/n10/full/nbt1004

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HMM: A Bioinformatics Visual
We can calculate our confidence directly.
The probability that nucleotide
i
was emitted by state
k
is the sum of the probabilities
of all the states paths use state
k
to generate
i
, normalized by the sum over all possible
state paths
Result:
We get a probability of 46% that the best

scoring fifth G is correct and 28% that
the sixth G position is correct.
Source: http://www.nature.com/nbt/journal/v22/n10/full/nbt1004

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Further Possibilites
The toy

model provided by the article is a simple example
But we can go further, we could add a more realistic consensus
GTRAGT at the 5' splice site
We could put a row of six HMM states in place of '5' state to
model a six

base ungapped consensus motif
Possibilities are not limited
The catch
HMM don't deal well with correlations between nucleotides
Because they assume that each emitted nucleotide depends only
on one underlying state.
Example of bad use for HMM:
Conserved RNA base pairs which induce long

range pairwise
correlations; one position might be any nucleotide but the base

paired partner must be complementary.
An HMM state path has no way of 'remembering' what a distant
state generated.
Source: http://www.nature.com/nbt/journal/v22/n10/full/nbt1004

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Credits
http://www.nature.com/nbt/journal/v22/n10/full/nbt10
04

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http://en.wikipedia.org/wiki/Viterbi_algorithm
http://en.wikipedia.org/wiki/Hidden_Markov_model
http://en.wikipedia.org/wiki/Bayesian_network
http://www.daimi.au.dk/~bromille/PHM/Storm.pdf
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