Gene Prediction

1. Doug Raiford Lesson 3 Gene Prediction

2. What’s the problem • Have a fully sequenced genome • How identify the genes? • What do we know so far? Gene Prediction

3. Look for start and stop codons • Remember • Start codon codes for methionine • Stop codons do not code for an amino acid • Does every ATG mark the beginning of a gene? • Does every TAG, TAA, or TGA mark the end? Start codon: ATG Stop codons: TAG, TAA, or TGA Gene Prediction

4. In frame • The start and stop codons must be “in frame” • A set of codons must fit between them • Length evenly divisible by three • Open reading frame • Series of codons bracketed by start and stop codons (in frame) Gene Prediction

5. Gene length • The distance between start and stop codons tends to be longer than expected • How long would we expect that distance to be? Gene Prediction

6. Randomly drawn nucleotides • There are 64 different codons • A given codon should show-up randomly around once every 64 codons or 192 nts (64*3) • 3 stop codons • Expect 3 in every 64 codons or once every 21 1/3 codons(21 1/3 * 3 = 64 nts) Gene Prediction

7. How far beyond 64? • Number of genes in E. coli is 4356 • Min 44 nts, max 8621 • 8 are < 64 • 143 < 128 (3%) • Good start but must be more • Approximately 77,000 ORFs > 2* expected on each strand Escherichia coli Gene Prediction

8. Parts of a gene • To “find” a gene would look for nt sequences that look like the parts of a gene RNA polymerase Promoter Region Coding region Terminator Region Start Codon ‘ATG’ = Methionine Stop Codon: non coding ‘TAA’, ‘TAG’, or ‘TGA’ Gene Prediction

9. Upstream region • Attract polymerase • Specific sequences • Gene regulation • Each promoter has unique pattern • Motifs for -35 sequence T T G A C A for -10 sequence T A T A A T -35 -10 Ribosomal binding site Coding region Polymerase binding Start Codon Transcription start site Gene Prediction

10. Genes of a feather… • Slightly different -35 and -10 motifs attract different sigma factors • Genes with similar upstream regions tend to be related: they express similarly Gene Prediction

11. Termination region (downstream) • Hairpin • Followed by U-run (A-run in the DNA) Gene Prediction

12. Termination • Week uracil bindings coupled with hairpin binding with nusA protein bound to polymerase mRNA Polymerase UUUUUUU DNA AAAAAAAA Gene Prediction

13. Motifs • How find? • Difficult: fuzzy, not carved in stone for -35 sequence T T G A C A for -10 sequence T A T A A T -35 -10 Ribosomal binding site Coding region Polymerase binding Start Codon Transcription start site Gene Prediction

14. Motiffs • Hidden Markov Models often used • All about the statistics • Markov Chain: series of events along with probabilities G or C or A or T Start Yay! I found one T A T A A T A Gene Prediction

15. Hidden Markov Models • Previous was a “state machine” representation • Should have states and observations • The states are “hidden” 1 A C G T A C G T 3 .25 .25 .25 .25 .25 .25 .25 .25 .5 1 1 1 .5 1 1 0 1 2 4 5 6 7 8 .1 1 1 1 1 1 1 .99 1 T A T A A T Gene Prediction

16. Probabilities • Each state has a probability of “emitting” any given observation • Each state has a probability of “transitioning” to any given next state 1 A C G T A C G T 3 .25 .25 .25 .25 .25 .25 .25 .25 .5 1 1 1 .5 1 1 0 1 2 4 5 6 7 8 .1 1 1 1 1 1 1 .99 1 T A T A A T Gene Prediction

17. Representing a model: two matrices • Transition probability matrix • Rows represent current state • Columns represent state to which a transition will occur • Entry is the probability associated with that transition • Emission probability matrix • Rows represent states • Columns represent which observation is emitted • Entry is the probability associated with that emission TRANS EMIS Gene Prediction

18. General approach • Requires a subject matter expert to build a model • Often start with a state for each position in a possible match • Example looking for something similar to • TATAAT • Might not have both A’s • Might have extra one in first slot • Never have G’s or C’s 1 A C G T A C G T 3 .25 .25 .25 .25 .25 .25 .25 .25 .5 1 1 1 .5 1 1 0 1 2 4 5 6 7 8 .1 1 1 1 1 1 1 .99 1 A T A T A T Gene Prediction

19. Model • Also need a state for non-participating regions 1 A C G T A C G T 3 .25 .25 .25 .25 .25 .25 .25 .25 .5 1 1 1 .5 1 1 0 1 2 4 5 6 7 8 .1 1 1 1 1 1 1 .99 1 T A T A A T Gene Prediction

20. Model • First guess as to probabilities • Maybe from state associated with first T to A 100% • Then 50% 50% whether A or T • Then 50% 50% whether A or T • Then 100% T 1 A C G T A C G T 3 .25 .25 .25 .25 .25 .25 .25 .25 .5 1 1 1 .5 1 1 0 1 2 4 5 6 7 8 .1 1 1 1 1 1 1 .99 1 A T A T A T Gene Prediction

21. Can “train” the model • Baum-Welch or Viterbi algorithm • Pass the algorithm a sequence of observations and first guess as to probabilities • It refines the probability matrices • Assumes that the sequence adheres to the • underlying probabilities. • Traverses states keeping track of actual • frequency of emissions and transitions • Adjusts matrices accordingly Gene Prediction

22. Using the model • Called checking the posterior probabilities • Given a sequence, check all possible paths through the model • Multiply the associated probabilities • Path with the highest probability is likely the path through the hidden states • Can use the “forward algorithm” to cut down the number of paths (dynamic programming) • Location in sequence where most probable states are “TATAAT” is a match A C G T A C G T .25 .25 .25 .25 .25 .25 .25 .25 1 1 0 1 2 3 4 5 1 1 1/17 1 1 1 1 16/17 1 T A T A Gene Prediction

23. Example using Matlab • Matlab very useful at matrix operations seq =['a','g','c','g','a','t','a','c','g','c','g','a','t','c','g','a','t','a','t','a','g','t','g','c'] seq =[1,3,2,3,1,4,1,2,3,2,3,1,4,2,3,1,4,1,4,1,3,4,3,2] EMIS = [.25,.25,.25,.25;#ACGT 0,0,0,1; 1,0,0,0; 0,0,0,1; 1,0,0,0; .25,.25,.25,.25] TRANS = [16/17,1/17,0,0,0,0; 0,0,1,0,0,0; 0,0,0,1,0,0; 0,0,0,0,1,0; 0,0,0,0,0,1; 0,0,0,0,0,1] A C G T A C G T .25 .25 .25 .25 .25 .25 .25 .25 1 1 0 1 2 3 4 5 1 1 1/17 1 1 1 1 16/17 1 T A T A Gene Prediction

24. Sites • Gene mark georgia institute • http://exon.biology.gatech.edu/ • Genscan • http://genes.mit.edu/GENSCAN.html • Genie Berkeley • http://www.fruitfly.org/seq_tools/genie.html • Glimmer university of maryland • http://www.cbcb.umd.edu/software/GlimmerHMM/ Gene Prediction

25. Elaborate models • Can include all regions in the model • States for each position in each region • Coding region could be simple set of three regions for -35 sequence T T G A C A for -10 sequence T A T A A T -35 -10 Ribosomal binding site Coding region Termination region Polymerase binding Start Codon Transcription start site Gene Prediction

26. Used in many applications • Classic example: states are rainy or sunny • If know whether someone is walking, shopping or cleaning, can predict state states Emissions Observations Gene Prediction

27. Gene Prediction

28. State is hidden • If something that is observable is dependent on an underlying state can use HMM • In motifs sequence is visible, whether or not a region is a promoter site is not Gene Prediction

29. Probabilities • Each state has a probability of emitting any given observation • Each state has a probability of transitioning to any given next state Probabilistic parameters of a hidden Markov model (example)x — statesy — possible observationsa — state transition probabilitiesb — output probabilities Gene Prediction