1. 4. HMMs for gene finding HMM Ability to model grammar
Biological sequence problems: grammatical structure
Here, eukaryotic gene structure
Exon/intron: word
Constraints on gene structure
Sentence: never end with an intron
Exon can never follow an exon
Simply the grammar
Formal language theory
Applied to biological problems
David Searls: gene finding

2. 4. HMMs for gene finding Figure 8.
Represent only the simplest of grammars: regular grammar
Good enough for the gene finding problem
RNA folding problem: needs more complicated grammar
Figure 8.

3. 4.1 Signal sensors Apply HMM to many of signals in a gene structure
Acceptor/donor sites
Regions around the start/stop codons
Figure 9.
HMM: 19 states, equivalent to a weight matrix

4. 4.1 Signal sensors Dinucleotide preferences in DNA
16 probability parameters in each state instead of 4: conditional probability
First order states

5. 4.2 Coding regions
Codon structure: important feature of coding regions
Figure 10: model of bases in triplets

6. 4.2 Coding regions Codon model Modeling codon statistics
Last state: order two, 64 probabilities
Lack of stop codons: p(A|TA), p(G|TA), p(A|TG) = 0
Modeling codon statistics
First state: 0 order
Second state: 1 order
Higher order states: dependencies between neighboring codons

7. 4.3 Combining the models Discover genes Figure 11.
Combine the models in a way that satisfies the grammar of genes
Figure 11.
A state for intergenic regions
A model for the region around the start codon = acceptor model
The model for the coding region
A model for the region around the stop codon
One big HMM

8. 4.3 Combining the models Gene prediction in a sequence of DNA
Viterbi algorithm: find the most probable path through the model
Predict sensible genes that obey the grammatical rules
Minimum requirements for unspliced gene candidates
Gene will always start with a start codon and end with a stop codon
The length will always be divisible by 3
Never contain stop codons in the reading frame
Splicing model
Splicing can happen in three different reading frames
Reading frame in one exon has to fit the one in the next
Using three different models of introns, one for each frame

9. 4.3 Combining the models Figure 12.
Many possible variations of the model
more states to the signal sensors
models of promoter elements and untranslated regions of the gene