MNW2 course Introduction to Bioinformatics

MNW2 course

Introduction to Bioinformatics

Lecture 22: Markov models

Centre for Integrative BioinformaticsFEW/FALW

heringa@cs.vu.nl

Problem in biology

• Data and patterns are often not clear cut• When we want to make a method to recognise a

pattern (e.g. a sequence motif), we have to learn from the data (e.g. maybe there are other differences between sequences that have the pattern and those that do not)

• This leads to Data mining and Machine learning

Contents:

•Markov chain models (1st order, higher order andinhomogeneous models; parameter estimation; classification)

• Interpolated Markov models (and back-off models)

• Hidden Markov models (forward, backward and Baum-Welch algorithms; model topologies; applications to genefinding and protein family modeling

A widely used machine learning approach: Markov models

Markov Chain Models

• a Markov chain model is defined by:– a set of states

• some states emit symbols

• other states (e.g. the begin state) are silent

– a set of transitions with associated probabilities• the transitions emanating from a given state define a

distribution over the possible next states

Markov Chain Models• given some sequence x of length L, we can ask how

probable the sequence is given our model• for any probabilistic model of sequences, we can

write this probability as

• key property of a (1st order) Markov chain: the probability of each Xi depends only on Xi-1

Markov Chain Models

Pr(cggt) = Pr(c)Pr(g|c)Pr(g|g)Pr(t|g)

Markov Chain ModelsCan also have an end state, allowing the model to represent:

• Sequences of different lengths

• Preferences for sequences ending with particular symbols

Markov Chain Models

ii xxa1

)|Pr( 11 iixx xxa

ii

The transition parameters can be denoted by

where

Similarly we can denote the probability of a sequence x as

Where aBxi represents the transition from the begin state

Example Application• CpG islands

– CGdinucleotides are rarer in eukaryotic genomes than expected given the independent probabilities of C, G

– but the regions upstream of genes are richer in CG dinucleotides than elsewhere – CpG islands

– useful evidence for finding genes

• Could predict CpG islands with Markov chains– one to represent CpG islands– one to represent the rest of the genome

Example includes using Maximum likelihood and Bayes’ statistical data and feeding it to a HM model

Estimating the Model Parameters

• Given some data (e.g. a set of sequences from CpG islands), how can we determine the probability parameters of our model?

• One approach: maximum likelihood estimation– given a set of data D

– set the parameters to maximize

Pr(D | )– i.e. make the data D look likely under the model

Maximum Likelihood Estimation• Suppose we want to estimate the parameters Pr(a), Pr(c),

Pr(g), Pr(t)

• And we’re given the sequences: accgcgctta gcttagtgactagccgttac

• Then the maximum likelihood estimates are:

Pr(a) = 6/30 = 0.2 Pr(g) = 7/30 = 0.233Pr(c) = 9/30 = 0.3 Pr(t) = 8/30 = 0.267

These data are derived from genome sequences

Higher Order Markov Chains

• An nth order Markov chain over some alphabet is equivalent to a first order Markov chain over the alphabet of n-tuples

• Example: a 2nd order Markov model for DNA can be treated as a 1st order Markov model over alphabet:AA, AC, AG, AT, CA, CC, CG, CT, GA, GC, GG, GT, TA, TC, TG, and TT (i.e. all possible dipeptides)

A Fifth Order Markov Chain

Inhomogenous Markov Chains

• In the Markov chain models we have considered so far, the probabilities do not depend on where we are in a given sequence

• In an inhomogeneous Markov model, we can have different distributions at different positions in the sequence

• Consider modeling codons in protein coding regions

Inhomogenous Markov Chains

A Fifth Order InhomogenousMarkov Chain

Selecting the Order of aMarkov Chain Model

• Higher order models remember more “history”• Additional history can have predictive value• Example:

– predict the next word in this sentence fragment “…finish __” (up, it, first, last, …?)

– now predict it given more history

• “Fast guys finish __”

Selecting the Order of aMarkov Chain Model

• However, the number of parameters we need to estimate grows exponentially with the order– for modeling DNA we need parameters for an nth order model, with n 5 normally

• The higher the order, the less reliable we can expect our parameter estimates to be– estimating the parameters of a 2nd order homogenous Markov chain from the complete genome of E. Coli, we would see each word > 72,000 times on average– estimating the parameters of an 8th order chain, we would see each word ~ 5 times on average

Interpolated Markov Models

• The IMM idea: manage this trade-off by interpolating among models of various orders

• Simple linear interpolation:

Interpolated Markov Models

• We can make the weights depend on the history

– for a given order, we may have significantly more data to estimate some words than others

• General linear interpolation

Gene Finding: Search by Content•Encoding a protein affects the statistical properties of a DNA sequence

– some amino acids are used more frequently than others (Leu more popular than Trp)– different numbers of codons for different amino

acids (Leu has 6, Trp has 1)– for a given amino acid, usually one codon is used

more frequently than others

•This is termed codon preference

•Codon preferences vary by species

Codon Preference in E. Coli

AA codon /1000----------------------Gly GGG 1.89Gly GGA 0.44Gly GGU 52.99Gly GGC 34.55

Glu GAG 15.68Glu GAA 57.20

Asp GAU 21.63Asp GAC 43.26

• Common way to search by content– build Markov models of coding & noncoding

regions– apply models to ORFs (Open Reading Frames) or fixed-sized windows of sequence

• GeneMark [Borodovsky et al.]– popular system for identifying genes in bacterial

genomes– uses 5th order inhomogenous Markov chain

models

Search by Content

The GLIMMER System• Salzberg et al., 1998

• System for identifying genes in bacterial genomes

• Uses 8th order, inhomogeneous, interpolated Markov chain models

IMMs in GLIMMER• How does GLIMMER determine the values?• First, let us express the IMM probability

calculation recursively:

IMMs in GLIMMER

• If we haven’t seen xi-1… xi-n more than 400 times, then compare the counts for the following:

• Use a statistical test ( 2) to get a value d indicating our confidence that the distributions represented by the two sets of counts are different

IMMs in GLIMMER

2 score when comparing nth-order with n-1th-order Markov model (preceding slide)

The GLIMMER method

• 8th order IMM vs. 5th order Markov model

• Trained on 1168 genes (ORFs really)

• Tested on 1717 annotated (more or less known) genes

Hidden Markov models (HMMs)

Given say a T in our input sequence, which state emitted it?

Hidden Markov models (HMMs)

Hidden State

• We will distinguish between the observed parts of a problem and the hidden parts• In the Markov models we have considered previously, it is clear which state accounts for each part of the observed sequence • In the model above (preceding slide), there are multiple states that could account for each part of the observed sequence– this is the hidden part of the problem– states are decoupled from sequence symbols

HMM-based homology searching

HMM for ungapped alignment…

Transition probabilities and Emission probabilities

Gapped HMMs also have insertion and deletion states (next slide)

Profile HMM: m=match state, I-insert state, d=delete state; go from left to right. I and m states output amino acids; d states are ‘silent”.

d1 d2 d3 d4

I0 I2 I3 I4I1

m0 m1 m2 m3 m4 m5

Start End

Model for alignment with insertions and deletions

HMM-based homology searching

• Most widely used HMM-based profile searching tools currently are SAM-T99 (Karplus et al., 1998) and HMMER2 (Eddy, 1998)

• formal probabilistic basis and consistent theory behind gap and insertion scores

• HMMs good for profile searches, bad for alignment (due to parametrisation of the models)

• HMMs are slow

Homology-derived Secondary Structure of Proteins

(HSSP) Sander & Schneider, 1991

It’s all about trying to push “don’t know region” down…

The Parameters of an HMM

HMM for Eukaryotic Gene Finding

Figure from A. Krogh, An Introduction to Hidden Markov Models for Biological Sequences

A Simple HMM

Three Important Questions

• How likely is a given sequence?the Forward algorithm

• What is the most probable “path” for generating a given sequence?

the Viterbi algorithm

• • How can we learn the HMM parameters given a set of sequences?the Forward-Backward (Baum-Welch) algorithm

How Likely is a Given Sequence?

• The probability that the path is taken and the sequence is generated:

• (assuming begin/end are the only silent states on path)

How Likely is a Given Sequence?

How Likely is a Given Sequence?

The probability over all paths is:

but the number of paths can be exponential in the length of the sequence...

• the Forward algorithm enables us to compute this efficiently

How Likely is a Given Sequence:The Forward Algorithm

• Define fk(i) to be the probability of being in state k

• Having observed the first i characters of x we want to compute fN(L), the probability of being in the end state having observed all of x

• We can define this recursively

How Likely is a Given Sequence:

The forward algorithm• Initialisation:

f0(0) = 1 (start),

fk(0) = 0 (other silent states k)

• Recursion: fl(i) = el(i)k fk(i-1)akl (emitting states),

fl(i) = k fk(i)akl (silent states)

• Termination:

Pr(x) = Pr(x1…xL) = f N(L) = k fk(L)akN probability that we are in the end state and have observed the entire sequence

probability that we’re in start state and have observed 0 characters from the sequence

Forward algorithm example

Three Important Questions

• How likely is a given sequence?

• What is the most probable “path” for generating a given sequence?

• How can we learn the HMM parameters given a set of sequences?

Finding the Most Probable Path:The Viterbi Algorithm

• Define vk(i) to be the probability of the most probable path accounting for the first i characters of x and ending in state k

• We want to compute vN(L), the probability of the most probable path accounting for all of the sequence and ending in the end state

• Can be defined recursively• Can use DP to find vN(L) efficiently

Finding the Most Probable Path:The Viterbi Algorithm

Initialisation:

v0(0) = 1 (start), vk(0) = 0 (non-silent states)

Recursion for emitting states (i =1…L):

Recursion for silent states:

Finding the Most Probable Path:The Viterbi Algorithm

Three Important Questions

• How likely is a given sequence? (clustering)

• What is the most probable “path” for generating a given sequence? (alignment)

• How can we learn the HMM parameters given a set of sequences?

The Learning Task• Given:

– a model– a set of sequences (the training set)

• Do:– find the most likely parameters to explain the training sequences

• The goal is find a model that generalizes well to sequences we haven’t seen before

Learning Parameters

• If we know the state path for each training sequence, learning the model parameters is simple– no hidden state during training– count how often each parameter is used– normalize/smooth to get probabilities– process just like it was for Markov chain models

• If we don’t know the path for each training sequence, how can we determine the counts?– key insight: estimate the counts by considering every

path weighted by its probability

Learning Parameters:The Baum-Welch Algorithm

• An EM (expectation maximization) approach, a forward-backward algorithm

• Algorithm sketch:– initialize parameters of model– iterate until convergence

• Calculate the expected number of times each transition or emission is used

• Adjust the parameters to maximize the likelihood of these expected values

The Expectation step

The Expectation step

The Expectation step

The Expectation step

The Expectation step

• First, we need to know the probability of the i th symbol being produced by state q, given sequence x:

Pr( i = k | x)

•Given this we can compute our expected counts for state transitions, character emissions

The Expectation step

The Backward Algorithm

The Expectation step

The Expectation step

The Expectation step

The Maximization step

The Maximization step

The Baum-Welch Algorithm

• Initialize parameters of model

• Iterate until convergence– calculate the expected number of times each transition or emission is used– adjust the parameters to maximize the likelihood of these expected values

• This algorithm will converge to a local maximum (in the likelihood of the data given the model)

• Usually in a fairly small number of iterations