Hidden Markov Modelshisto.ucsf.edu/BMS270/BMS270a_2012/slides/Slides05_HMM.pdf · Hidden Markov...
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Hidden Markov Models Mark Voorhies 4/2/2012 Mark Voorhies Hidden Markov Models
Transcript of Hidden Markov Modelshisto.ucsf.edu/BMS270/BMS270a_2012/slides/Slides05_HMM.pdf · Hidden Markov...
Hidden Markov Models
Mark Voorhies
4/2/2012
Mark Voorhies Hidden Markov Models
Searching with PSI-BLAST
Mark Voorhies Hidden Markov Models
0th order Markov Model
Mark Voorhies Hidden Markov Models
1st order Markov Model
Mark Voorhies Hidden Markov Models
1st order Markov Model
Mark Voorhies Hidden Markov Models
1st order Markov Model
Mark Voorhies Hidden Markov Models
What are Markov Models good for?
Background sequence composition
Spam
Mark Voorhies Hidden Markov Models
Hidden Markov Models
Mark Voorhies Hidden Markov Models
Hidden Markov Models
Mark Voorhies Hidden Markov Models
Hidden Markov Models
Mark Voorhies Hidden Markov Models
Hidden Markov Models
Mark Voorhies Hidden Markov Models
Hidden Markov Models
Mark Voorhies Hidden Markov Models
Hidden Markov Model
Mark Voorhies Hidden Markov Models
The Viterbi algorithm: Alignment
Mark Voorhies Hidden Markov Models
The Viterbi algorithm: Alignment
Dynamic programming, likeSmith-Waterman
Sums best log probabilitiesof emissions and transitions(i.e., multiplyingindependent probabilities)
Result is most likelyannotation of the targetwith hidden states
Mark Voorhies Hidden Markov Models
The Forward algorithm: Net probability
Probability-weighted sumover all possible paths
Simple modification ofViterbi (although summingprobabilities means we haveto be more careful aboutrounding error)
Result is the probability thatthe observed sequence isexplained by the model
In practice, this probabilityis compared to that of a nullmodel (e.g., randomgenomic sequence)
Mark Voorhies Hidden Markov Models
Training an HMM
If we have a set of sequenceswith known hidden states(e.g., from experiment),then we can calculate theemission and transitionprobabilities directly
Otherwise, they can beiteratively fit to a set ofunlabeled sequences that areknown to be true matchesto the model
The most common fittingprocedure is theBaum-Welch algorithm, aspecial case of expectationmaximization (EM)
Mark Voorhies Hidden Markov Models
Training an HMM
If we have a set of sequenceswith known hidden states(e.g., from experiment),then we can calculate theemission and transitionprobabilities directly
Otherwise, they can beiteratively fit to a set ofunlabeled sequences that areknown to be true matchesto the model
The most common fittingprocedure is theBaum-Welch algorithm, aspecial case of expectationmaximization (EM)
Mark Voorhies Hidden Markov Models
Training an HMM
If we have a set of sequenceswith known hidden states(e.g., from experiment),then we can calculate theemission and transitionprobabilities directly
Otherwise, they can beiteratively fit to a set ofunlabeled sequences that areknown to be true matchesto the model
The most common fittingprocedure is theBaum-Welch algorithm, aspecial case of expectationmaximization (EM)
Mark Voorhies Hidden Markov Models
Profile Alignments: Plan 7
(Image from Sean Eddy, PLoS Comp. Biol. 4:e1000069)
Mark Voorhies Hidden Markov Models
Profile Alignments: Plan 7 (from Outer Space)
(Image from Sean Eddy, PLoS Comp. Biol. 4:e1000069)
Mark Voorhies Hidden Markov Models
Rigging Plan 7 for Multi-Hit Alignment
(Image from Sean Eddy, PLoS Comp. Biol. 4:e1000069)
Mark Voorhies Hidden Markov Models
HMMer3 speeds
Eddy, PLoSCompBiol 7:e1002195
Mark Voorhies Hidden Markov Models
HMMer3 sensitivity and specificity
Eddy, PLoSCompBiol 7:e1002195
Mark Voorhies Hidden Markov Models
Homework
Compare the performance of BLASTP, PSI-BLAST, phmmer,and jackhmmer on a difficult sequence such as AGA1p(CAA96325.1). Use the shuffling tool on the course websiteto generate negative controls with the same composition. Forpositive controls, see Euk. Cell 5:628.
Download Cluster3 and JavaTreeView
Read PNAS 95:14863
Mark Voorhies Hidden Markov Models
Stochastic Context Free Grammars
Can emit from both sides → base pairs
Can duplicate emitter → bifurcations
Mark Voorhies Hidden Markov Models
INFERNAL/Rfam
Modified from the INFERNAL User Guide – Nawrocki, Kolbe, and Eddy
Mark Voorhies Hidden Markov Models
INFERNAL/Rfam
Modified from the INFERNAL User Guide – Nawrocki, Kolbe, and Eddy
Mark Voorhies Hidden Markov Models
INFERNAL/Rfam
Modified from the INFERNAL User Guide – Nawrocki, Kolbe, and Eddy
Mark Voorhies Hidden Markov Models
INFERNAL/Rfam
Modified from the INFERNAL User Guide – Nawrocki, Kolbe, and Eddy
Mark Voorhies Hidden Markov Models
INFERNAL/Rfam
Modified from the INFERNAL User Guide – Nawrocki, Kolbe, and Eddy
Mark Voorhies Hidden Markov Models
INFERNAL/Rfam
Modified from the INFERNAL User Guide – Nawrocki, Kolbe, and Eddy
Mark Voorhies Hidden Markov Models
INFERNAL/Rfam
Modified from the INFERNAL User Guide – Nawrocki, Kolbe, and Eddy
Mark Voorhies Hidden Markov Models
