HMM – HMM Comparison

1. HMM – HMM Comparison A topic in Sequence analysis Presented by: Eric Schrag Giriprasad Sridhara Eschrag,giri@UDel.edu CISC 841 Spring 2006 MAY 16 2006

2. Organization of Presentation • Introduction • Experiment • Results • Ongoing (Future) Work

3. Introduction • Recap • The original paper work by Dr. Soding • HHPred • For protein remote homology detection • Align 2 HMMs • Use Viterbi algorithm • Calibrate the scores for better accuracy • Aligning 2 HMMs better than • Sequence-Sequence • Sequence–Profile • Profile–Profile • Sequence-HMM

4. Introduction • Recap (continued) • Calibration of score done by • Using secondary structure score • Biologically, secondary structures diverge more slowly than sequences • Even sequences that are distantly homologous will have similar secondary structures. • This can help distinguish real homologs from chance hits

5. Experiment • What happens if we use the Forward algorithm instead of the Viterbi algorithm? • Since the source code of HHSearch is not available, we use PRC for our experiment. • PRC • Profile Comparer • Developed by Martin Madera. • Stand-alone program for aligning and scoring two profile hidden Markov models • Source code in C.

6. Experiment • PRC allows to use • Viterbi • Forward • The Superfamily 1.69 database HMMER models were downloaded. • This has 10,085 different HMMER model files (.hmm files) • A Unix shell script was developed which picked 2 HMM models (files) at random

7. Experiment • These 2 (random) models were input to the PRC • The PRC was then run in different modes • Viterbi • Forward • The above was repeated a number of times (1000) in the shell script • The whole experiment was repeated many times, so that we had different runs of HMM-HMM comparison. • The source code was changed • To output the number of hits • We get > 1 match, since the Smith-Waterman-Eggert algorithm is used, which finds sub-optimal alignments as well. • The average of the scores corresponding to the matching regions • The average number of columns in the match region.

8. Results

9. Score Comparison Graph

10. Columns Comparison Graph

11. Match Comparison Graph

12. Results • Consistently, • The forward algorithm • Had more hits • Better scores • Better number of columns in the match region • This is consistent with the expectation that the Forward algorithm is better than the Viterbi, in this case. The Forward algorithm is in fact equivalent to finding the joint emission probability.

13. Ongoing (Future) Work • Incorporate secondary structure information to calibrate the score. • Score the seed sequence or the multiple sequence alignment from which the HMM was built • Using the PSIPRED, predict the secondary structure. • This gives the 3 states H, E or C for every residue along with confidence values 0-9. • Compare the predicted state and the confidence values of the residues in the 2 HMMs to arrive at a secondary structure calibration score. • Use concept of clustering of conserved columns, to further calibrate the score.

14. Acknowledgement • Dr. Li Liao for the idea of using Forward algorithm instead of Viterbi in the comparsion of the HMMs • Dr. Johannes Soding, the author of HHSearch for answering our queries and directing us to PRC. • Mr. Martin Madera, author of PRC, for making the source code available.

15. Thank you.