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- 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!

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