Title: Welcome to Introduction to Computational Genomics for Infectious Disease
1Welcome toIntroduction to Computational Genomics
for Infectious Disease
2Course Instructors
- Instructor
- James Galagan
- Teaching Assistants
- Lab Instructors
Brian Weiner Desmond Lun
Antonis Rokas Mark Borowsky Jeremy Zucker
Reinhard Engels Aaron Brandes Caroline Colijn
Other members of Broad Microbial Analysis Group Other members of Broad Microbial Analysis Group Other members of Broad Microbial Analysis Group
3Schedule and Logistics
Tues/Thurs 11-1230 Harvard School of Public
Health FXB-301 The François-Xavier Bagnoud
Center, Room 301
Wed/Fri 1-3 Broad Institute Olympus Room First
floor of Broad Main Lobby See front desk
attendant near entrance Individual computers and
software provided No programming experience
required
4Website
www.broad.mit.edu/annotation/winter_course_2006/
- Contact information
- Directions to Broad
- Lecture slides
- Lab handouts
- Resources
5Goals of Course
- Introduction to concepts behind commonly used
computational tools - Recognize connection between different concepts
and applications - Hands on experience with computational analysis
6Concepts and Applications
- Lectures will cover concepts
- Computationally oriented
- Labs will provide opportunity for hands on
application of tools - Nuts and bolts of running tools
- Application of tools not covered in lectures
7Computational Genomics Overview
Slide Credit Manolis Kellis
8Topics
- Probabilistic Sequence Modeling
- Clustering and Classification
- Motifs
- Steady State Metabolic Modeling
9Topics Not Covered
- Sequence Alignment
- Phylogeny (maybe in labs)
- Molecular Evolution
- Population Genetics
- Advanced Machine Learning
- Bayesian Networks
- Conditional Random Fields
10Applications to Infectious Disease
- Examples and labs will focus on the analysis of
microbial genomics data - Pathogenicity islands
- TB expression analysis
- Antigen prediction
- Mycolic acid metabolism
- But approaches are applicable to any organism and
to many different questions
11Probabilistic Modeling of Biological Sequences
- Concepts
- Statistical Modeling of Sequences
- Hidden Markov Models
- Applications
- Predicting pathogenicity islands
- Modeling protein families
- Lab Practical
- Basic sequence annotation
12Probabilistic Sequence Modeling
- Treat objects of interest as random variables
- nucleotides, amino acids, genes, etc.
- Model probability distributions for these
variables - Use probability calculus to make inferences
13Why Probabilistic Sequence Modeling?
- Biological data is noisy
- Probability provides a calculus for manipulating
models - Not limited to yes/no answers can provide
degrees of belief - Many common computational tools based on
probabilistic models
14Sequence Annotation
GCGTCTGACGGCGCACCGTTCGCGCTGCCGGCACCCCGGGCTCCATAATG
AAAATCATGT TCAGTAAGCTACACTCTGCATATCGGGCTACCAACGAAA
TGGAGTATCGGTCATGATCTT GCCAGCCGTGCCTAAAAGCTTGGCCGCA
GGGCCGAGTATAATTGGTCGCGGTCGCCTCGAAGTTAGCTTATGCAATGC
AGGAGGTGGGGCAAAGTTCAGGCGGATCGGCCGATGGCGGGCGTAGGTGA
AGGAGACAGCGGAGGCGTGGAGCGTGATGACATTGGCATGGTGGCCGCTT
CC CCCGTCGCGTCTCGGGTAAATGGCAAGGTAGACGCTGACGTCGTCGG
TCGATTTGCCACC TGCTGCCGTGCCCTGGGCATCGCGGTTTACCAGCGT
AAACGTCCGCCGGACCTGGCTGCC GCCCGGTCTGGTTTCGCCGCGCTGA
CCCGCGTCGCCCATGACCAGTGCGACGCCTGGACC GGGCTGGCCGCTGC
CGGCGACCAGTCCATCGGGGTGCTGGAAGCCGCCTCGCGCACGGCG ACC
ACGGCTGGTGTGTTGCAGCGGCAGGTGGAACTGGCCGATAACGCCTTGGG
CTTCCTG TACGACACCGGGCTGTACCTGCGTTTTCGTGCCACCGGACCT
GACGATTTCCACCTCGCG TATGCCGCTGCGTTGGCTTCGACGGGCGGGC
CGGAGGAGTTTGCCAAGGCCAATCACGTG GTGTCCGGTATCACCGAGCG
CCGCGCCGGCTGGCGTGCCGCCCGTTGGCTCGCCGTGGTC ATCAACTAC
CGCGCCGAGCGCTGGTCGGATGTCGTGAAGCTGCTCACTCCGATGGTTAA
T GATCCCGACCTCGACGAGGCCTTTTCGCACGCGGCCAAGATCACCCTG
GGCACCGCACTG GCCCGACTGGGCATGTTTGCCCCGGCGCTGTCTTATC
TGGAGGAACCCGACGGTCCTGTC GCGGTCGCTGCTGTCGACGGTGCACT
GGCCAAAGCGCTGGTGCTGCGCGCGCATGTGGAT ATGGAGTCGGCCAGC
GAAGTGCTGCAGGACTTGTATGCGGCTCACCCCGAAAACGAACAG GTCG
AGCAGGCGCTGTCGGATACCAGCTTCGGGATCGTCACCACCACAGCCGGG
CGGATC GAGGCCCGCACCGATCCGTGGGATCCGGCGACCGAGCCCGGCG
CGGAGGATTTCGTCGAT CCCGCGGCCCACGAACGCAAGGCCGCGCTGCT
GCACGAGGCCGAACTCCAACTCGCCGAG
15Sequence Annotation
GCGTCTGACGGCGCACCGTTCGCGCTGCCGGCACCCCGGGCTCCATAATG
AAAATCATGT TCAGTAAGCTACACTCTGCATATCGGGCTACCAACGAAA
TGGAGTATCGGTCATGATCTT GCCAGCCGTGCCTAAAAGCTTGGCCGCA
GGGCCGAGTATAATTGGTCGCGGTCGCCTCGAAGTTAGCTTATGCAATGC
AGGAGGTGGGGCAAAGTTCAGGCGGATCGGCCGATGGCGGGCGTAGGTGA
AGGAGACAGCGGAGGCGTGGAGCGTGATGACATTGGCATGGTGGCCGCTT
CC CCCGTCGCGTCTCGGGTAAATGGCAAGGTAGACGCTGACGTCGTCGG
TCGATTTGCCACC TGCTGCCGTGCCCTGGGCATCGCGGTTTACCAGCGT
AAACGTCCGCCGGACCTGGCTGCC GCCCGGTCTGGTTTCGCCGCGCTGA
CCCGCGTCGCCCATGACCAGTGCGACGCCTGGACC GGGCTGGCCGCTGC
CGGCGACCAGTCCATCGGGGTGCTGGAAGCCGCCTCGCGCACGGCG ACC
ACGGCTGGTGTGTTGCAGCGGCAGGTGGAACTGGCCGATAACGCCTTGGG
CTTCCTG TACGACACCGGGCTGTACCTGCGTTTTCGTGCCACCGGACCT
GACGATTTCCACCTCGCG TATGCCGCTGCGTTGGCTTCGACGGGCGGGC
CGGAGGAGTTTGCCAAGGCCAATCACGTG GTGTCCGGTATCACCGAGCG
CCGCGCCGGCTGGCGTGCCGCCCGTTGGCTCGCCGTGGTC ATCAACTAC
CGCGCCGAGCGCTGGTCGGATGTCGTGAAGCTGCTCACTCCGATGGTTAA
T GATCCCGACCTCGACGAGGCCTTTTCGCACGCGGCCAAGATCACCCTG
GGCACCGCACTG GCCCGACTGGGCATGTTTGCCCCGGCGCTGTCTTATC
TGGAGGAACCCGACGGTCCTGTC GCGGTCGCTGCTGTCGACGGTGCACT
GGCCAAAGCGCTGGTGCTGCGCGCGCATGTGGAT ATGGAGTCGGCCAGC
GAAGTGCTGCAGGACTTGTATGCGGCTCACCCCGAAAACGAACAG GTCG
AGCAGGCGCTGTCGGATACCAGCTTCGGGATCGTCACCACCACAGCCGGG
CGGATC GAGGCCCGCACCGATCCGTGGGATCCGGCGACCGAGCCCGGCG
CGGAGGATTTCGTCGAT CCCGCGGCCCACGAACGCAAGGCCGCGCTGCT
GCACGAGGCCGAACTCCAACTCGCCGAG
Gene
16Sequence Annotation
GCGTCTGACGGCGCACCGTTCGCGCTGCCGGCACCCCGGGCTCCATAATG
AAAATCATGT TCAGTAAGCTACACTCTGCATATCGGGCTACCAACGAAA
TGGAGTATCGGTCATGATCTT GCCAGCCGTGCCTAAAAGCTTGGCCGCA
GGGCCGAGTATAATTGGTCGCGGTCGCCTCGAAGTTAGCTTATGCAATGC
AGGAGGTGGGGCAAAGTTCAGGCGGATCGGCCGATGGCGGGCGTAGGTGA
AGGAGACAGCGGAGGCGTGGAGCGTGATGACATTGGCATGGTGGCCGCTT
CC CCCGTCGCGTCTCGGGTAAATGGCAAGGTAGACGCTGACGTCGTCGG
TCGATTTGCCACC TGCTGCCGTGCCCTGGGCATCGCGGTTTACCAGCGT
AAACGTCCGCCGGACCTGGCTGCC GCCCGGTCTGGTTTCGCCGCGCTGA
CCCGCGTCGCCCATGACCAGTGCGACGCCTGGACC GGGCTGGCCGCTGC
CGGCGACCAGTCCATCGGGGTGCTGGAAGCCGCCTCGCGCACGGCG ACC
ACGGCTGGTGTGTTGCAGCGGCAGGTGGAACTGGCCGATAACGCCTTGGG
CTTCCTG TACGACACCGGGCTGTACCTGCGTTTTCGTGCCACCGGACCT
GACGATTTCCACCTCGCG TATGCCGCTGCGTTGGCTTCGACGGGCGGGC
CGGAGGAGTTTGCCAAGGCCAATCACGTG GTGTCCGGTATCACCGAGCG
CCGCGCCGGCTGGCGTGCCGCCCGTTGGCTCGCCGTGGTC ATCAACTAC
CGCGCCGAGCGCTGGTCGGATGTCGTGAAGCTGCTCACTCCGATGGTTAA
T GATCCCGACCTCGACGAGGCCTTTTCGCACGCGGCCAAGATCACCCTG
GGCACCGCACTG GCCCGACTGGGCATGTTTGCCCCGGCGCTGTCTTATC
TGGAGGAACCCGACGGTCCTGTC GCGGTCGCTGCTGTCGACGGTGCACT
GGCCAAAGCGCTGGTGCTGCGCGCGCATGTGGAT ATGGAGTCGGCCAGC
GAAGTGCTGCAGGACTTGTATGCGGCTCACCCCGAAAACGAACAG GTCG
AGCAGGCGCTGTCGGATACCAGCTTCGGGATCGTCACCACCACAGCCGGG
CGGATC GAGGCCCGCACCGATCCGTGGGATCCGGCGACCGAGCCCGGCG
CGGAGGATTTCGTCGAT CCCGCGGCCCACGAACGCAAGGCCGCGCTGCT
GCACGAGGCCGAACTCCAACTCGCCGAG
Promoter Motif
Gene
Kinase Domain
17Probabilistic Sequence Modeling
- Hidden Markov Models (HMM)
- A general framework for sequences of symbols
(e.g. nucleotides, amino acids) - Widely used in computational genomics
- Hmmer HMMs for protein families
- Pathogenicity Islands
18Pathogenicity Islands
- Clusters of genes acquired by horizontal transfer
- Present in pathogenic species but not others
- Frequently encode virulence factors
- Toxins, secondary metabolites, adhesins
- (Flanked by repeats, gene content, phylogeny,
regulation, codon usage) - Different GC content than rest of genome
19Application Bacillus subtilis
20Modeling Sequence Composition
- Calculate sequence distribution from known
islands - Count occurrences of A,T,G,C
- Model islands as nucleotides drawn independently
from this distribution
P(SiMP)
21The Probability of a Sequence
- Can calculate the probability of a particular
sequence (S) according to the pathogenicity
island model (MP)
Example
S AAATGCGCATTTCGAA
22Sequence Classification
- PROBLEM Given a sequence, is it an island?
- We can calculate P(SMP), but what is a
sufficient P value? - SOLUTION compare to a null model and calculate
log-likelihood ratio - e.g. background DNA distribution model, B
Pathogenicity Islands
Background DNA
23Finding Islands in Sequences
- Could use the log-likelihood ratio on windows of
fixed size - What if islands have variable length?
- We prefer a model for entire sequence
TAAGAATTGTGTCACACACATAAAAACCCTAAGTTAGAGGATTGAGATTG
GCA GACGATTGTTCGTGATAATAAACAAGGGGGGCATAGATCAGGCTCA
TATTGGC
24A More Complex Model
Background
Island
TAAGAATTGTGTCACACACATAAAAACCCTAAGTTAGAGGATTGAGATTG
GCA GACGATTGTTCGTGATAATAAACAAGGGGGGCATAGATCAGGCTCA
TATTGGC
25A Generative Model
P
P
P
P
P
P
P
P
P
P
P
P
P
B
B
B
B
B
B
B
B
B
B
B
S
P(SP)
P(SB)
P(Li1Li)
Bi1 Pi1
Bi 0.85 0.15
Pi 0.25 0.75
26A Hidden Markov Model
Hidden States L 1, ..., K Transition
probabilities aij Transition probability from
state i to state j Emission probabilities ei(b)
P( emitting b statei) Initial state
probability p(b) P(first stateb)
Transition Probabilities
State i
State j
ej(b)
ei(b)
Emission Probabilities
27What can we do with this model?
- The model defines a joint probability over labels
and sequences, P(L,S) - Implicit in model is what labels tend to go
with what sequences (and vice versa) - Rules of probability allow us to use this model
to analyze existing sequences
28Fundamental HMM Operations
Computation
Biology
- Decoding
- Given an HMM and sequence S
- Find a corresponding sequence of labels, L
- Evaluation
- Given an HMM and sequence S
- Find P(SHMM)
- Training
- Given an HMM w/o parameters and set of
sequences S - Find transition and emission probabilities the
maximize P(S params, HMM)
Annotate pathogenicity islands on a new
sequence Score a particular sequence (not as
useful for this model will come back to this
later) Learn a model for sequence composed of
background DNA and pathogenicity islands
29The Hidden in HMM
- DNA does not come conveniently labeled (i.e.
Island, Gene, Promoter) - We observe nucleotide sequences
- The hidden in HMM refers to the fact that state
labels, L, are not observed - Only observe emissions (e.g. nucleotide sequence
in our example)
State i
State j
A A G T T A G A G
30Decoding With HMM
- Given observables, we would like to predict a
sequence of hidden states that is most likely to
have generated that sequence
Pathogenicity Island Example
Given a nucleotide sequence, we want a labeling
of each nucleotide as either pathogenicity
island or background DNA
31The Most Likely Path
- Given a sequence, one reasonable choice for a
labeling is -
The sequence of labels, L, (or path) that makes
the labels and sequence most likely given the
model
32Probability of a Path,Seq
P
P
P
P
P
P
P
P
L
B
B
B
B
B
B
B
B
G
C
A
A
A
T
G
C
S
33Probability of a Path,Seq
P
P
P
P
P
P
P
P
L
B
B
B
B
B
B
B
B
G
C
A
A
A
T
G
C
S
We could try to calculate the probability of
every path, but.
34Decoding
- Viterbi Algorithm
- Finds most likely sequence of labels, L, given
sequence and model - Uses dynamic programming (same technique used in
sequence alignment) - Much more efficient than searching every path
35Probability of a Single Label
P
P
P
P
P
P
P
P
L
B
B
B
B
B
B
B
B
G
C
A
A
A
T
G
C
S
P(Label5BS)
Forward algorithm (dynamic programming)
- Calculate most probable label, Li , at each
position i - Do this for all N positions gives us L1, L2,
L3. LN
36Two Decoding Options
- Viterbi Algorithm
- Finds most likely sequence of labels, L, given
sequence and model - Posterior Decoding
- Finds most likely label at each position for all
positions, given sequence and model - L1, L2, L3. LN
- Forward and Backward equations
37Application Bacillus subtilis
38Method
Second Order Emissions P(Si)P(SiState,Si-1,Si-2
) (capturing trinucleotide Frequencies) Train
using EM Predict w/Posterior Decoding
Three State Model
Gene
Gene-
AT Rich
Nicolas et al (2002) NAR
39Results
Gene on positive strand
Gene on negative strand
- A/T Rich
- Intergenic regions
- Islands
Each line is P(labelS,model) color coded by
label
Nicolas et al (2002) NAR
40Fundamental HMM Operations
Computation
Biology
- Decoding
- Given an HMM and sequence S
- Find a corresponding sequence of labels, L
- Evaluation
- Given an HMM and sequence S
- Find P(SHMM)
- Training
- Given an HMM w/o parameters and set of
sequences S - Find transition and emission probabilities the
maximize P(S params, HMM)
Annotate pathogenicity islands on a new
sequence Score a particular sequence (not as
useful for this model will come back to this
later) Learn a model for sequence composed of
background DNA and pathogenicity islands
41Training an HMM
Transition probabilities e.g. P(Pi1Bi) the
probability of entering a pathogenicity island
from background DNA Emission probabilities i.e.
the nucleotide frequencies for background DNA and
pathogenicity islands
P(Li1Li)
B
P
P(SP)
P(SB)
42Learning From Labelled Data
Maximum Likelihood Estimation
If we have a sequence that has islands marked, we
can simply count
P
P
P
P
P
P
P
P
L
B
B
B
B
B
B
B
B
G
C
A
A
A
T
G
C
S
P(SP)
P(SB)
P(Li1Li)
A T G C
A 1/5 T 0 G 2/5 C 2/5
Bi1 Pi1 End
Bi 3/5 1/5 1/5
Pi 1/3 2/3 0
Start 1 0 0
!
ETC..
43Unlabelled Data
How do we know how to count?
P
P
P
P
P
P
P
P
L
start
B
B
B
B
B
B
B
B
End
G
C
A
A
A
T
G
C
S
P(SP)
P(SB)
P(Li1Li)
A T G C
A T G C
Bi1 Pi1 End
Bi
Pi ?
Start
44Unlabeled Data
P
P
P
P
P
P
P
P
L
start
B
B
B
B
B
B
B
B
End
G
C
A
A
A
T
G
C
S
- An idea
- Imagine we start with some parameters
- We could calculate the most likely path, P,
given those parameters and S - We could then use P to update our parameters by
maximum likelihood - And iterate (to convergence)
45Expectation Maximization (EM)
- Initialize parameters
- E Step Estimate probability of hidden labels , Q,
given parameters and sequence - M Step Choose new parameters to maximize expected
likelihood of parameters given Q - Iterate
P(SModel) guaranteed to increase each iteration
46Expectation Maximization (EM)
- Remember the basic idea!
- Use model to estimate (distribution of) missing
data - Use estimate to update model
- Repeat until convergence
- EM is a general approach for learning models (ML
estimation) when there is missing data - Widely used in computational biology
- EM frequently used in motif discovery
- Lecture 3
47A More Sophisticated Application
Modeling Protein Families
- Given amino acid sequences from a protein family,
how can we find other members? - Can search databases with each known member not
sensitive - More information is contained in full set
- The HMM Profile Approach
- Learn the statistical features of protein family
- Model these features with an HMM
- Search for new members by scoring with HMM
We will learn features from multiple alignments
48Human Ubiquitin Conjugating Enzymes
- UBE2D2 FPTDYPFKPPKVAFTTRIYHPNINSN-GSICLDILR-----
--------SQWSPALTISK - UBE2D3 FPTDYPFKPPKVAFTTRIYHPNINSN-GSICLDILR-----
--------SQWSPALTISK - BAA91697 FPTDYPFKPPKVAFTTKIYHPNINSN-GSICLDILR-----
--------SQWSPALTVSK - UBE2D1 FPTDYPFKPPKIAFTTKIYHPNINSN-GSICLDILR-----
--------SQWSPALTVSK - UBE2E1 FTPEYPFKPPKVTFRTRIYHCNINSQ-GVICLDILK-----
--------DNWSPALTISK - UBCH9 FSSDYPFKPPKVTFRTRIYHCNINSQ-GVICLDILK-----
--------DNWSPALTISK - UBE2N LPEEYPMAAPKVRFMTKIYHPNVDKL-GRICLDILK-----
--------DKWSPALQIRT - AAF67016 IPERYPFEPPQIRFLTPIYHPNIDSA-GRICLDVLKLP---
------PKGAWRPSLNIAT - UBCH10 FPSGYPYNAPTVKFLTPCYHPNVDTQ-GNICLDILK-----
--------EKWSALYDVRT - CDC34 FPIDYPYSPPAFRFLTKMWHPNIYET-GDVCISILHPPVDD
PQSGELPSERWNPTQNVRT - BAA91156 FPIDYPYSPPTFRFLTKMWHPNIYEN-GDVCISILHPPVDD
PQSGELPSERWNPTQNVRT - UBE2G1 FPKDYPLRPPKMKFITEIWHPNVDKN-GDVCISILHEPGED
KYGYEKPEERWLPIHTVET - UBE2B FSEEYPNKPPTVRFLSKMFHPNVYAD-GSICLDILQN----
---------RWSPTYDVSS - UBE2I FKDDYPSSPPKCKFEPPLFHPNVYPS-GTVCLSILEED---
--------KDWRPAITIKQ - E2EPF5 LGKDFPASPPKGYFLTKIFHPNVGAN-GEICVNVLKR----
---------DWTAELGIRH - UBE2L1 FPAEYPFKPPKITFKTKIYHPNIDEK-GQVCLPVISA----
--------ENWKPATKTDQ - UBE2L6 FPPEYPFKPPMIKFTTKIYHPNVDEN-GQICLPIISS----
--------ENWKPCTKTCQ - UBE2H LPDKYPFKSPSIGFMNKIFHPNIDEASGTVCLDVIN-----
--------QTWTALYDLTN - UBC12 VGQGYPHDPPKVKCETMVYHPNIDLE-GNVCLNILR-----
--------EDWKPVLTINS
49Profile HMM
50Using Profile HMMs
Computation
Biology
- Decoding
- Find sequence of labels, L, that maximizes
P(LS, HMM) - Evaluation
- Find P(SHMM)
- Training
- Find transition and emission probabilities the
maximize P(S params, HMM)
Align a new sequence to a protein
family Score a sequence for membership in
family Discover and model family structure
51Example Modeling Globins
- Profile HMM from 300 randomly selected globin
genes - Score database of 60,000 proteins
52PFAM Collection of Profile HMMs
http//www.sanger.ac.uk/Software/Pfam/
53PFAM Resources
- 8957 curated protein families and domains
- Each with HMM profile(s)
- Coverage
- 73 of proteins in Swissprot and SP-TREMBLE
- 53 of typical genome sequence
54Example PFAM Entry
- Literature Links
- Protein Structure
- Domain Architectures
- GO Functional Categories
55HMMER
- Implementation of Profile HMM methods
- Given a multiple alignment, HMMER can build a
Profile HMM - Given a Profile HMM (i.e. from PFAM), HMMER can
score sequences for membership in the family or
domain
56HMMs in Context
- HMMs
- Sequence alignment
- Gene Prediction
- Generalized HMMs
- Variable length states
- Complex emissions models
- e.g. Genscan
- Bayesian Networks
- General graphical model
- Arbitrary graph structure
- e.g. Regulatory network analysis
57References
- Sean R Eddy, Hidden Markov models, Current
Opinion in Structural Biology, 6361-365, 1996. - Sean R Eddy, Profile hidden Markov models,
Bioinformatcis, 14(9)755-763, 1998. - Anders Krogh, An introduction to hidden Markov
models for biological sequences, In
computational Methods in Molecular Biology,
edited by S. L. Salzberg, D. B. Searls and S.
Kasif, pp. 45-63, Elsevier, 1998. - HMMER profile HMMs for protein sequence
analysis. http//hmmer.wustl.edu/ - Erik L. L. Sonnhammer et al, Pfam multiple
sequence alignments andHMM-profiles of protein
domains, Nucleic Acids Research, 26(1)320-322,
1998. - R. Durbin, S. Eddy, A. Krogh and G. Mitchison,
BIOLOGICAL SEQUENCE ANALYSIS, Cambridge
University Press, 1998.
58Tomorrows Lab
- Basic Sequence Analysis Tools
- Argo Genome Browser
- Blast
- Gene prediction using Glimmer
- Protein families with Hmmer and PFAM
- Comparative synteny analysis
- Identify virulence factors by annotating and
comparing virulent and avirulent bacterial
sequences
59(No Transcript)
60The Hidden in HMM
- DNA does not come conveniently labeled (i.e.
Pathogencity Island, Gene, Promoter) - All we observe are the nucleotide sequences
- The hidden in HMM refers to the fact that the
state labels, L, are not observed - Only observe emissions (e.g. nucleotide sequence
in our example)
61Relation between Viterbi and Forward
- VITERBI
- Vj(i) P(most probable path ending in state j
with observation i) - Initialization
- V0(0) 1
- Vk(0) 0, for all k gt 0
- Iteration
- Vj(i) ej(xi) maxk Vk(i-1) akj
- Termination
- P(x, ?) maxk Vk(N)
- FORWARD
- fl(i)P(x1xi,stateij)
- Initialization
- f0(0) 1
- fk(0) 0, for all k gt 0
- Iteration
- fl(i) el(xi) ?k fk(i-1) akl
- Termination
-
- P(x) ?k fk(N) ak0
Slide Credit Serafim Batzoglou