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JM - 1 Introduction to Bioinformatics: Lecture XIII Profile and Other Hidden Markov Models Jarek Meller Jarek Meller Division.

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Presentation on theme: "JM - 1 Introduction to Bioinformatics: Lecture XIII Profile and Other Hidden Markov Models Jarek Meller Jarek Meller Division."— Presentation transcript:

1 JM - 1 Introduction to Bioinformatics: Lecture XIII Profile and Other Hidden Markov Models Jarek Meller Jarek Meller Division of Biomedical Informatics, Children’s Hospital Research Foundation & Department of Biomedical Engineering, UC

2 JM - http://folding.chmcc.org2 Outline of the lecture Multiple alignments, family profiles and probabilistic models of biological sequences From simple Markov models to Hidden Markov Models (HMMs) Profile HMMs: topology and parameter optimization Finding optimal alignments: the Viterbi algorithm Other applications of HMMs

3 JM - http://folding.chmcc.org3 Web watch: personalized predictive medicine Targeting crucial signal transduction pathway in lung cancer: an inhibitor of the Epidermal Growth Factor Receptor (EGFR) catalytic activity that binds EGFRs with specific mutations. Genotyping the EGFR gene appears to be sufficient to predict the outcome of the therapy. Paez JG et. al. Science 304

4 JM - http://folding.chmcc.org4 Hidden Markov Models for biological sequences Problems with grammatical structure, such as gene finding, family profiles and protein function prediction, transmembrane domains prediction In general, one may think of different biases in different fragments of the sequence (due to functional role for example) or of different states emitting these fragments using different probability distributions Durbin et. al., Chapters 3 to 6

5 JM - http://folding.chmcc.org5 Example: Markov chain model for CpG islands Motivation: CpG dinucleotides (and not the C-G bas pairs across the two strands) are frequently methylated at C, with methyl-C mutating with a higher rate into a T; however, the methylation process is suppressed around regulatory sequences (e.g. promoters) where CpG islands occur more often. A CG T Transition probabilities: t T,G =P(a i =G | a i-1 =T) etc. The overall probability of a sequence defined as product of transition probabilities

6 JM - http://folding.chmcc.org6 Example: Hidden Markov model for CpG islands A* C*G* T* Adding four more states (A*,C*,T*,G*) to represent the “island” model, as opposed to non-island model with unlikely transitions between the models one obtains a “hidden” MM for CpG islands. There is no longer one-to-one correspondence between the states and the symbols and knowing the sequence we cannot tell state the model was in when generating subsequent letters in the sequence. A CG T

7 JM - http://folding.chmcc.org7 Probabilistic models of biological sequences For any probabilistic model the total probability of observing a sequence a 1 a 2 …a n may be written as: P(a 1 a 2 …a n ) = P(a n | a n-1 … a 1 ) P(a n-1 | a n-2 … a 1 ) … P(a 1 ) In Markov chain models we simply have: P(a 1 a 2 …a n ) = P(a n | a n-1 ) P(a n-1 | a n-2 ) … P(a 1 ) HMMs are generalization of Markov chain models, with some “hidden” states that “emit” sequence symbols according to certain probability distributions and (Markov) transitions between pairs of hidden states

8 JM - http://folding.chmcc.org8 HMMs as probabilistic linguistic models HMMs may be in fact regarded as probabilistic, finite automata that generate certain “languages”: sets of words (sentences etc.) with specific “grammatical” structure. For example, promoter, start, exon, splice junction, intron, stop “states” will appear in a linguistic model of a gene, whereas column (sequence position), insert and deletion states will be employed in a linguistic model of a (protein) family profile.

9 JM - http://folding.chmcc.org9 HMMs for gene prediction: an exon model

10 JM - http://folding.chmcc.org10 HMMs and the supervised learning approach Given a training set of aligned sequences find optimal transition and emission probabilities that maximize probability of observing the training sequences – Baum- Welch (Expectation Maximization) or Viterbi training algorithm In recognition phase, having the optimized probabilities, we ask what is the likelihood that a new sequence belongs to a family i.e. it is generated by the HMM with sufficiently high probability. The Viterbi algorithm, which is in fact dynamic programming in a suitable formulation, is used to find an optimal path through the states, which defines the optimal alignment

11 JM - http://folding.chmcc.org11 Ungapped profiles and the corresponding HMMs BegMjMj End …… Example AGAAACT AGGAATT TGAATCT P( AGAAACT )=16/81 P( TGGATTT )=1/81 1234567 A2/30 1 00 T1/3000 1 C000002/30 G011/30000 Each blue square represents a match state that “emits” each letter with certain probability e j (a) which is defined by frequency of a at position j: Typically, pseudo-counts are added in HMMs to avoid zero probabilities.

12 JM - http://folding.chmcc.org12 HMMs and likelihood optimization

13 JM - http://folding.chmcc.org13 Likelihood optimization …

14 JM - http://folding.chmcc.org14 Insertions and deletions in profile HMMs BegMjMj End IjIj Insert states emit symbols just like the match states, however, the emission probabilities are typically assumed to follow the background distribution and thus do not contribute to log-odds scores. Transitions I j -> I j are allowed and account for an arbitrary number of inserted residues that are effectively unaligned (their order within an inserted region is arbitrary).

15 JM - http://folding.chmcc.org15 Insertions and deletions in profile HMMs BegMjMj End DjDj Deletions are represented by silent states which do not emit any letters. A sequence of deletions (with D -> D transitions) may be used to connect any two match states, accounting for segments of the multiple alignment that are not aligned to any symbol in a query sequence (string). The total cost of a deletion is the sum of the costs of individual transitions (M->D, D->D, D->M) that define this deletion. As in case of insertions, both linear and affine gap penalties can be easily incorporated in this scheme.

16 JM - http://folding.chmcc.org16 Gap penalties: evolutionary and computational considerations Linear gap penalties:  (k) = - k d for a gap of length k and constant d Affine gap penalties:  (k) = - [ d + (k -1) e ] where d is opening gap penalty and e an extension gap penalty.

17 JM - http://folding.chmcc.org17 Profile HMMs as a model for multiple alignments BegMjMj End IjIj DjDj Example AG---C A-AG-C AG-AA- --AAAC AG---C ** *

18 JM - http://folding.chmcc.org18 Observed emission and transition counts C0C1C2C3 A-400 C-004 G-030 T-000 BegMjMj End IjIj DjDj AG...C A-AG.C AGAA.- --AAAC AG...C C0C1C2C3 A0060 C0000 G0010 T0000 Match emissionsInsert emissions 4 234 1 1 1 2 1 4 1 1 2 C0 C1 C2 C3

19 JM - http://folding.chmcc.org19 Computing emission and transition probabilities

20 JM - http://folding.chmcc.org20 Optimal alignment corresponds to a path with the highest probability (or log-odds score) BegMjMj End IjIj DjDj Problem Given the above model, with emission and transition probabilities obtained previously, find the optimal path (alignment) for the query sequence AGAC Problem Find emission and transition counts assuming that the 4 th column in the example of multiple alignment in slide 15 corresponds to another match state (and not an insert state)

21 JM - http://folding.chmcc.org21 Outline of the Viterbi algorithm BegMjMj End IjIj DjDj

22 JM - http://folding.chmcc.org22 Profile HMMs for local alignments MjMj IjIj DjDj Beg End QQ The trick consists of adding additional insert states Q that model flanking unaligned sequences using background frequencies q a and large t Q,Q

23 JM - http://folding.chmcc.org23 Summary In general, when the states generating training sequences (alignments) are not known an iterative procedure Problem with local minima, topology choice (length of the profile) Excellent results in family assignment (SAM, PFAM), gene prediction, trans-membrane domain recognition etc.

24 JM - http://folding.chmcc.org24 Outline of the lecture

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