Applications of Hidden Markov Models

1. Applications of Hidden Markov Models (Lecture for CS397-CXZ Algorithms in Bioinformatics) March 6, 2004 ChengXiang Zhai Department of Computer Science University of Illinois, Urbana-Champaign

2. Today’s Lecture • HMM Applications • Profile HMMs (Classification) • HMMs for Multiple Sequence Alignment (Pattern discovery) • HMMs for Gene Finding (Segmentation) • Special issues in HMMs • Local Maximas • Model construction • Weighting training sequences

3. HMM Applications • Classification (e.g., Profile HMMs) • Build an HMM for each class (profile HMMs) • Classify a sequence using Bayes rule • Multiple sequence alignment • Build an HMM based on a set of sequences • Decode each sequence to find a multiple alignment • Segmentation (e.g., gene finding) • Use different states to model different regions • Decode a sequence to reveal the region boundaries

4. HMMs for Classification E.g., Protein families Assign a family to X p(X|C) is modeled by a profile HMM built specifically for C Assuming example sequences are available for C

5. HMMs for Multiple Alignment • Given a set of sequences S={X1, …,Xk} • Train an HMM, e.g., using Baum-Welch (finding the HMM that maximizes the probability of S) • Decode each sequence Xi • Assemble the Viterbi paths to form a multiple alignment (insertions are uncertain)

6. HMM-based Gene Finding • Design two types of states • “Within Gene” States • “Outside Gene” States • Use known genes to estimate the HMM • Decode a new sequence to reveal which part is a gene • Example software: • GENSCAN (Burge 1997) • FGENESH (Solovyev 1997) • HMMgene (Krogh 1997) • GENIE (Kulp 1996) • GENMARK (Borodovsky & McIninch 1993) • VEIL (Henderson, Salzberg, & Fasman 1997)

7. Exon HMM Model Upstream 3’ Splice Site Start Codon Exon Intron Stop Codon 5’ Splice Site Downstream 5’ Poly-A Site VEIL: Viterbi Exon-Intron Locator • Enter: start codon or intron (3’ Splice Site) • Exit: 5’ Splice site or three stop codons (taa, tag, tga) VEIL Architecture (Slide from N. F. Samatova’s lecture)

8. It is based on Generalized HMM (GHMM) Model both strands at once Other models: Predict on one strand first, then on the other strand Avoids prediction of overlapping genes on the two strands (rare) Each state may output a string of symbols (according to some probability distribution). Explicit intron/exon length modeling Special sensors for Cap-site and TATA-box Advanced splice site sensors GenScan Architecture Fig. 3, Burge and Karlin 1997

9. Special Issues • Local maxima • Optimal model construction • Weighting training sequences

10. Solutions to the Local Maxima Problem • Repeat with different initializations • Start with the most reasonable initial model • Simulated annealing (slow down the convergence speed)

11. Local Maxima: Illustration Global maxima Local maxima Good starting point Bad starting point

12. Optimal Model Construction Bayesian model selection: P(HMM) should prefer simpler models

13. Sequence Weighting • Avoid over-counting similar sequences from the same organisms • Typically compute a weight for a sequence based on an evolutionary tree • Many ways to incorporate the weights, e.g., • Unequal likelihood • Unequal weight contribution in parameter estimation

14. HMMs in Real Applications • SAM-T98 Tutorial: • http://www.cse.ucsc.edu/research/compbio/ismb99.tutorial.html • Pfam • http://www.sanger.ac.uk/Software/Pfam/