INTRODUCTION OF HIDDEN
MARKOV MODEL
Mohan Kumar Yadav
M.Sc Bioinformatics
JNU JAIPUR

HIDDEN MARKOV MODEL(HMM)
Real-world has structures and processes which have
observable outputs.
– Usually sequential .
– Cannot see the event producing the output.
Problem: how to construct a model of the structure or
process given only observations.

HISTORY OF HMM
• Basic theory developed and published in 1960s and 70s
• No widespread understanding and application until late
80s
• Why?
– Theory published in mathematic journals which were
not widely read.
– Insufficient tutorial material for readers to understand
and apply concepts.

Andrei Andreyevich Markov
1856-1922
Andrey Andreyevich Markov was a Russian
mathematician.
He is best known for his work on stochastic
processes.
A primary subject of his research later became
known as Markov chains and Markov processes .

HIDDEN MARKOV MODEL
• A Hidden Markov Model (HMM) is a statical model in
which the system is being modeled is assumed to be
a Markov process with hidden states.
• Markov chain property: probability of each
subsequent state depends only on what was the
previous state.

EXAMPLE OF HMM
• Coin toss:
– Heads, tails sequence with 2 coins
– You are in a room, with a wall
– Person behind wall flips coin, tells result
– Coin selection and toss is hidden
– Cannot observe events, only output (heads, tails) from events
– Problem is then to build a model to explain observed
sequence of heads and tails.

EXAMPLE OF HMM
• Weather
– Once each day weather is observed
– State 1: rain
– State 2: cloudy
– State 3: sunny
– What is the probability the weather for the next 7 days will be:
– sun, sun, rain, rain, sun, cloudy, sun
– Each state corresponds to a physical observable event

HMM COMPONENTS
• A set of states (x’s)
• A set of possible output symbols (y’s)
• A state transition matrix (a’s)
– probability of making transition from one state to the
next
• Output emission matrix (b’s)
– probability of a emitting/observing a symbol at a
particular state
• Initial probability vector
– probability of starting at a particular state
– Not shown, sometimes assumed to be 1

EXAMPLE OF HMM
0.3
0.7
Rain
Dry
0.2
• Two states : ‘Rain’ and ‘Dry’.
• Transition probabilities: P(‘Rain’|‘Rain’)=0.3 ,
P(‘Dry’|‘Rain’)=0.7 , P(‘Ra’)=0.6 .
• in’|‘Dry’)=0.2, P(‘Dry’|‘Dry’)=0.8
• Initial probabilities: say P(‘Rain’)=0.4 , P(‘Dry
0.8

CALCULATION OF HMM

HMM COMPONENTS

COMMON HMM TYPES
• Ergodic (fully connected):
– Every state of model can be reached in a single step from
every other state of the model.
• Bakis (left-right):
– As time increases, states proceed from left to right

HMM IN BIOINFORMATICS
• Hidden Markov Models (HMMs) are a
probabilistic model for modeling and
representing biological sequences.
• They allow us to do things like find genes, do
sequence alignments and find regulatory
elements such as promoters in a principled
manner.

PROBLEMS OF HMM
• Three problems must be solved for HMMs to be
useful in real-world applications
●
1) Evaluation
●
2) Decoding
●
3) Learning

EVOLUTION OF PROBLEM
Given a set of HMMs, which is the one most
likely to have produced the observation sequence?
GACGAAACCCTGTCTCTATTTATCC
p(HMM-3)?
p(HMM-1)?
p(HMM-2)?
HMM 1
HMM 2
HMM 3
p(HMM-n)?
…
HMM n

DECODING PROBLEM

TRAINING PROBLEM
From raw seqence data…
AATAGAGAGGTTCGACTCTGCAT
TTCCCAAATACGTAATGCTTACGG
TACACGACCCAAGCTCTCTGCTT
GAATCCCAAATCTGAGCGGACAG
ATGAGGGGGCGCAGAGGAAAAA
CAGGTTTTGGACCCTACATAAAN
AGAGAGGTTCGTAAATAGAGAGG
TTCGACTCTGCATTTCCCAAATAC
GTAATGCTTACGGTTAAATAGAGA
GGTTCGACTCTGCATTTCCCAAA
TACGTAATGCTTACGGTACACGA
CCCAAGCTCTCTGCTTGTAACTT
GTTTTNGTCGCAGCTGGTCTTGC
CTTTGCTGGGGCTGCTGAC
to Transition Probabilities


A+ C+
A+
H
o
w
?
C+
G+
T+
ACGT-
0.17
0.16
0.15
0.07
0.01
0.01
0.01
0.01
0.26
0.36
0.33
0.35
0.01
0.01
0.01
0.01
G+ T+
0.42
0.26
0.37
0.37
0.01
0.01
0.01
0.01
0.11
0.18
0.11
0.17
0.01
0.01
0.01
0.01
A-
C-
G-
T-
0.01
0.01
0.01
0.01
0.29
0.31
0.24
0.17
0.01
0.01
0.01
0.01
0.2
0.29
0.23
0.23
0.01
0.01
0.01
0.01
0.27
0.07
0.29
0.28
0.01
0.01
0.01
0.01
0.2
0.29
0.2
0.28



HMM-APPLICATION
• DNA Sequence analysis
• Protein family profiling
• Predprediction
• Splicing signals prediction
• Prediction of genes
• Horizontal gene transfer
• Radiation hybrid mapping, linkage analysis
• Prediction of DNA functional sites.
• CpG island

HMM-APPLICATION
• Speech Recognition
• Vehicle Trajectory Projection
• Gesture Learning for Human-Robot Interface
• Positron Emission Tomography (PET)
• Optical Signal Detection
• Digital Communications
• Music Analysis

HMM-BASED TOOLS
• GENSCAN (Burge 1997)
• FGENESH (Solovyev 1997)
• HMMgene (Krogh 1997)
• GENIE (Kulp 1996)
• GENMARK (Borodovsky & McIninch 1993)
• VEIL (Henderson, Salzberg, & Fasman 1997)

BIOINFORMATICS RESOURCES
• PROBE www.ncbi.nlm.nih.gov/
• BLOCKS www.blocks.fhcrc.org/
• META-MEME
www.cse.ucsd.edu/users/bgrundy/metameme.1.0.html
• SAM
www.cse.ucsc.edu/research/compbio/sam.html
• HMMERS hmmer.wustl.edu/
• HMMpro www.netid.com/
• GENEWISE www.sanger.ac.uk/Software/Wise2/
• PSI-BLAST www.ncbi.nlm.nih.gov/BLAST/newblast.html
• PFAM www.sanger.ac.uk/Pfam/

Refrences
• Rabiner, L. R. (1989). A Tutorial on Hidden Markov
Models and Selected Applications in Speech
Recognition. Proceedings of the IEEE, 77(2), 257-285.
• Essential bioinformatics, Jin Xion
• http://www.sociable1.com/v/Andrey-Markov108362562522144#sthash.tbdud7my.dpuf

Thank You!