Download HIDDEN MARKOV MODELS

Profiles for Sequences
Sequence Profiles
• Often, sequences are characterized by
similarities that are not well captured through
matching algorithms.
• For example, identification of genes in the
presence of exons/introns, gene features
(CpG islands, etc.), domain profiles in
proteins, among others.
• For such sequences, Markov chains provide
useful abstractions.
Markov Chains
Rain
Sunny
Cloudy
State transition matrix : The probability of
States : Three states - sunny, the weather given the previous day's
cloudy, rainy.
weather.
Initial Distribution : Defining the probability of the
system being in each of the states at time 0.
Hidden Markov Models
Hidden states : the (TRUE) states of a system that
may be described by a Markov process (e.g., the
weather).
Observable states : the states of the process that
are `visible’.
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Hidden Markov Models
Initial Distribution : Initial state probability vector.
State transition Matrix
Emission Probabilities: containing the probability of
observing a particular observable state given that the
hidden model is in a particular hidden state.
Hidden Markov Models
Transition
Prob.
Output Prob.
Observed sequences can be scored if their state transitions are
known.
The probability of ACCY along this path is:
.4 * .3 * .46 * .6 * .97 * .5 * .015 * .73 *.01 * 1 = 1.76x10-6.
Methods for Hidden Markov Models
Scoring problem:
Given an existing HMM and observed sequence , what is the probability
that the HMM can generate the sequence
Methods, contd.
Alignment Problem
Given a sequence, what is the optimal state sequence that the HMM
would use to generate it
Methods, contd.
Training Problem
How do we estimate the structure and parameters of a HMM from
data.
HMMs– Some Applications
•
•
•
•
•
Gene finding and prediction
Protein-Profile Analysis
Secondary Structure prediction
Copy Number Variation
Characterizing SNPs
Gene Template
(Left)
(Removed)
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HMMs: Applications
• Classification: Classifying observations within a
sequence
• Order: A DNA sequence is a set of ordered observations
• Structure : can be intuitively defined:
• Measure of success: # of complete exons correctly
labeled
• Training data: Available from various genome
annotation projects
HMMs for Gene Finding
• Training - Expectation Maximization (EM)
• Parsing – Viterbi algorithm
An HMM for unspliced genes.
x : non-coding DNA
c : coding state
Genefinders: a Comparison
Accuracy per nucleotide
Method
Sn
Sp
AC
Sn
GENSCAN
FGENEH
GeneID
GeneParser2
GenLang
GRAILII
SORFIND
Xpound
0.93
0.77
0.63
0.66
0.72
0.72
0.71
0.61
0.93
0.85
0.81
0.79
0.75
0.84
0.85
0.82
0.91
0.78
0.67
0.66
0.69
0.75
0.73
0.68
0.78
0.61
0.44
0.35
0.5
0.36
0.42
0.15
Accuracy per exon
(Sn+Sp)/
Sp
ME
2
0.81
0.8
0.09
0.61
0.61
0.15
0.45
0.45
0.28
0.39
0.37
0.29
0.49
0.5
0.21
0.41
0.38
0.25
0.47
0.45
0.24
0.17
0.16
0.32
Sn = Sensitivity
Sp = Specificity
Ac = Approximate Correlation
ME = Missing Exons
WE = Wrong Exons
GENSCAN Performance Data, http://genes.mit.edu/Accuracy.html
WE
0.05
0.11
0.24
0.17
0.21
0.1
0.14
0.13
Protein Profile HMMs
• Motivation
– Given a single amino acid target sequence of
unknown structure, we want to infer the structure
of the resulting protein. Use Profile Similarity
• What is a Profile?
–
–
–
–
Proteins families of related sequences and structures
Same function
Clear evolutionary relationship
Patterns of conservation, some positions are more
conserved than the others
HMMs From Alignment
ACA
TCA
ACA
AGA
ACC
- - - ATG
ACT ATC
C - - AGC
- - - ATC
G - - ATC insertion
Transition probabilities
Output Probabilities
A HMM model for a DNA motif alignments, The transitions are
shown with arrows whose thickness indicate their probability. In
each state, the histogram shows the probabilities of the four
bases.
HMMs from Alignments
Deletion states
Matching states
Insertion states
No of matching states = average sequence length in the family
PFAM Database - of Protein families
(http://pfam.wustl.edu)
Database Searching
• Given HMM, M, for a sequence family,
find all members of the family in data
base.
• LL – score LL(x) = log P(x|M)
(LL score is length dependent – must
normalize or use Z-score)
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Querying a Sequence
Suppose I have a query protein sequence, and I am interested
in which family it belongs to? There can be many paths
leading to the generation of this sequence. Need to find all
these paths and sum the probabilities.
Consensus sequence:ACAC - - ATC
P (ACACATC) = 0.8x1 x 0.8x1 x 0.8x0.6 x 0.4x0.6 x 1x1 x
0.8x1 x 0.8 = 4.7 x 10 -2
Multiple Alignments
• Try every possible path through the
model that would produce the target
sequences
– Keep the best one and its probability.
– Output : Sequence of match, insert and
delete states
• Viterbi alg. Dynamic Programming
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HMMs from Unaligned Sequences
• Baum-Welch Expectation-maximization
method
– Start with a model whose length matches the
average length of the sequences and with random
output and transition probabilities.
– Align all the sequences to the model.
– Use the alignment to alter the output and transition
probabilities
– Repeat. Continue until the model stops changing
• By-product: a multiple alignment
PHMM Example
An alignment of 30 short amino acid sequences chopped out
of a alignment of the SH3 domain. The shaded area are the
most conserved and were represented by the main states in
the HMM. The unshaded area was represented by an insert
state.