1. Hidden Markov Models in Bioinformatics Example Domain: Gene Finding Colin Cherry colinc@cs

2. To recap last episode… Hidden Markov Models (HMMs) Protein Family Characterization Profile HMMs for protein family characterization How profile HMMs can do homology search

3. ...picking up where we left off Profile HMMs were good to start with Today’s goal: Introduce HMMs as general tools in bioinformatics I will use the problem of Gene Finding as an example of an “ideal” HMM problem domain

4. Learning Objectives When I’m done you should know: 1. When is an HMM a good fit for a problem space? 2. What materials are needed before work can begin with an HMM? 3. What are the advantages and disadvantages of using HMMs? 4. What are the general objectives and challenges in the gene finding task?

5. Outline HMMs as Statistical Models The Gene Finding task at a glance Good problems for HMMs HMM Advantages HMM Disadvantages Gene Finding Examples

6. Statistical Models Definition: Any mathematical construct that attempts to parameterize a random process Example: A normal distribution Assumptions Parameters Estimation Usage HMMs are just a little more complicated…

7. HMM Assumptions Observations are ordered Random process can be represented by a stochastic finite state machine with emitting states.

8. HMM Parameters Using weather example Modeling daily weather for a year Ra Ra Su Su Su Ra.. Lots of parameters One for each table entry Represented in two tables. One for emissions One for transitions

9. HMM Estimation Called training, it falls under machine learning Feed an architecture (given in advance) a set of observation sequences The training process will iteratively alter its parameters to fit the training set The trained model will assign the training sequences high probability

10. HMM Usage Two major tasks Evaluate the probability of an observation sequence given the model (Forward) Find the most likely path through the model for a given observation sequence (Viterbi)

11. Gene Finding (An Ideal HMM Domain) Our Objective: To find the coding and non-coding regions of an unlabeled string of DNA nucleotides Our Motivation: Assist in the annotation of genomic data produced by genome sequencing methods Gain insight into the mechanisms involved in transcription, splicing and other processes

12. Gene Finding Terminology A string of DNA nucleotides containing a gene will have separate regions (lines): Introns – non-coding regions within a gene Exons – coding regions Separated by functional sites (boxes) Start and stop codons Splice sites – acceptors and donors

13. Gene Finding Challenges Need the correct reading frame Introns can interrupt an exon in mid-codon There is no hard and fast rule for identifying donor and acceptor splice sites Signals are very weak

14. What makes a good HMM problem space? Characteristics: Classification problems There are two main types of output from an HMM: Scoring of sequences (Protein family modeling) Labeling of observations within a sequence (Gene Finding)

15. HMM Problem Characteristics Continued The observations in a sequence should have a clear, and meaningful order Unordered observations will not map easily to states It’s beneficial, but not necessary for the observations follow some sort of grammar Makes it easier to design an architecture Gene Finding Protein Family Modeling

16. HMM Requirements So you’ve decided you want to build an HMM, here’s what you need: An architecture Probably the hardest part Should be biologically sound & easy to interpret A well-defined success measure Necessary for any form of machine learning

17. HMM Requirements Continued Training data Labeled or unlabeled – it depends You do not always need a labeled training set to do observation labeling, but it helps Amount of training data needed is: Directly proportional to the number of free parameters in the model Inversely proportional to the size of the training sequences

18. Why HMMs might be a good fit for Gene Finding Classification: Classifying observations within a sequence Order: A DNA sequence is a set of ordered observations Grammar / Architecture: Our grammatical structure (and the beginnings of our architecture) is right here: Success measure: # of complete exons correctly labeled Training data: Available from various genome annotation projects

19. HMM Advantages Statistical Grounding Statisticians are comfortable with the theory behind hidden Markov models Freedom to manipulate the training and verification processes Mathematical / theoretical analysis of the results and processes HMMs are still very powerful modeling tools – far more powerful than many statistical methods

20. HMM Advantages continued Modularity HMMs can be combined into larger HMMs Transparency of the Model Assuming an architecture with a good design People can read the model and make sense of it The model itself can help increase understanding

21. HMM Advantages continued Incorporation of Prior Knowledge Incorporate prior knowledge into the architecture Initialize the model close to something believed to be correct Use prior knowledge to constrain training process

22. How does Gene Finding make use of HMM advantages? Statistics: Many systems alter the training process to better suit their success measure Modularity: Almost all systems use a combination of models, each individually trained for each gene region Prior Knowledge: A fair amount of prior biological knowledge is built into each architecture

23. HMM Disadvantages Markov Chains States are supposed to be independent P(y) must be independent of P(x), and vice versa This usually isn’t true Can get around it when relationships are local Not good for RNA folding problems P(x) … P(y)

24. HMM Disadvantages continued Standard Machine Learning Problems Watch out for local maxima Model may not converge to a truly optimal parameter set for a given training set Avoid over-fitting You’re only as good as your training set More training is not always good

25. HMM Disadvantages continued Speed!!! Almost everything one does in an HMM involves: “enumerating all possible paths through the model” There are efficient ways to do this Still slow in comparison to other methods

26. HMM Gene Finders: VEIL A straight HMM Gene Finder Takes advantage of grammatical structure and modular design Uses many states that can only emit one symbol to get around state independence

27. HMM Gene Finders: HMMGene Uses an extended HMM called a CHMM CHMM = HMM with classes Takes full advantage of being able to modify the statistical algorithms Uses high-order states Trains everything at once

28. HMM Gene Finders: Genie Uses a generalized HMM (GHMM) Edges in model are complete HMMs States can be any arbitrary program States are actually neural networks specially designed for signal finding

29. Conclusions HMMs have problems where they excel, and problems where they do not You should consider using one if: Problem can be phrased as classification Observations are ordered The observations follow some sort of grammatical structure (optional)

30. Conclusions Advantages: Statistics Modularity Transparency Prior Knowledge Disadvantages: State independence Over-fitting Local Maximums Speed

31. Some final words… Lots of problems can be phrased as classification problems Homology search, sequence alignment If an HMM does not fit, there’s all sorts of other methods to try with ML/AI: Neural Networks, Decision Trees Probabilistic Reasoning and Support Vector Machines have all been applied to Bioinformatics

32. Questions Any Questions?