1. University of Maryland
Bacterial Gene Finding and Glimmer (also Archaeal and viral gene finding)
Arthur L. Delcher and Steven Salzberg Center for Bioinformatics and Computational Biology
University of Maryland

2. Outline A (very) brief overview of microbial gene-finding Glimmer1 & 2
Codon composition methods
GeneMark: Markov models
Glimmer1 & 2
Interpolated Markov Model (IMM)
Interpolated Context Model (ICM)
Glimmer3
Reducing false positives
Improving coding initiation site predictions
Running Glimmer3
Review basic ideas.
Talk about two fairly recent gene-finders that use codon composition.
Update on status of rewriting Glimmer.

3. Step One Find open reading frames (ORFs).
…TAGAAAAATGGCTCTTTAGATAAATTTCATGAAAAATATTGA…
Stop codon
Stop codon

4. Step One Find open reading frames (ORFs). But ORFs generally overlap …
Reverse strand
Stop codon
…ATCTTTTTACCGAGAAATCTATTTAAAGTACTTTTTATAACT…
…TAGAAAAATGGCTCTTTAGATAAATTTCATGAAAAATATTGA…
Stop codon
Shifted Stop
Stop codon

5. All ORFs on both strands shown - color indicates reading frame
Campylobacter jejuni RM %GC
Say what picture is.
Long orfs are likely to be genes.
All ORFs on both strands shown
- color indicates reading frame
Longest ORFs likely to be protein-coding genes
Note the low GC content

6. Purple lines are the predicted genes
Campylobacter jejuni RM %GC
Purple lines are the predicted genes
Purple ORFs show annotated (“true”) genes

7. Note what happens in a high-GC genome
Campylobacter jejuni RM %GC
Mycobacterium smegmatis MC %GC
Note what happens in a high-GC genome

8. Purple lines show annotated genes
Campylobacter jejuni RM %GC
Mycobacterium smegmatis MC %GC
Purple lines show annotated genes

9. The Problem Need to decide which orfs are genes.
Then figure out the coding start sites
Can do homology searches but that won’t find novel genes
Besides, there are errors in the databases
Generally can assume that there are some known genes to use as training set.
Or just find the obvious ones

10. Codon Composition
Find patterns of nucleotides in known coding regions (assumed to be available).
Nucleotide distribution at 3 codon positions
Hexamers
GC-skew
(G-C)/(G+C) computed in windows of size N
Amino-acid composition
Use these to decide which orfs are genes.
Prefer longer orfs
Must deal with overlaps

11. Bacterial Replication
Early replication
Theta structure

12. Termination of Replication
B. subtilis
E. coli

13. Borrelia burgdorferi (Lyme disease pathogen) GC-skew plot

14. Codon Composition Nucleotide variation at codon position:
Campylobacter jejuni
Codon Position 1 2 3 a
36% c
13%
17% 9% g
30%
14%
10% t
21%
33%
44%
Mycobacterium smegmatis
Codon Position 1 2 3 a
19%
23% 6% c
27%
28%
48% g
42%
20%
39% t
12% 7%

15. Codon-Composition Gene Finders
ZCURVE
Guo, Ou & Zhang, NAR 31, 2003
Based on nucleotide and di-nucleotide frequency in codons
Uses Z-transform and Fisher linear discriminant
MED
Ouyang, Zhu, Wang & She, JBCB 2(2) 2004
Based on amino-acid frequencies
Uses nearest-neighbor classification on entropies

16. Probabilistic Methods
Create models that have a probability of generating any given sequence.
Train the models using examples of the types of sequences to generate.
The “score” of an orf is the probability of the model generating it.
Can also use a negative model (i.e., a model of non-orfs) and make the score be the ratio of the probabilities (i.e., the odds) of the two models.
Use logs to avoid underflow

17. Fixed-Order Markov Models
k th-order Markov model bases the probability of an event on the preceding k events.
Example: With a 3rd-order model the probability of this sequence: would be:
Target
Target

18. Fixed-Order Markov Models
Advantages:
Easy to train. Count frequencies of (k+1)-mers in training data.
Easy to compute probability of sequence.
Disadvantages:
Many (k+1)-mers may be undersampled in training data.
Models data as fixed-length chunks.
Uses sliding window
Fixed-Length Context
Target

19. GeneMark Borodovsky & McIninch, Comp. Chem 17, 1993.
Uses 5th-order Markov model.
Model is 3-periodic, i.e., a separate model for each nucleotide position in the codon.
DNA region gets 7 scores: 6 reading frames & non-coding―high score wins.
Lukashin & Borodovsky, Nucl. Acids Res. 26, 1998 is the HMM version.

20. Interpolated Markov Models (IMM)
Introduced in Glimmer 1.0 Salzberg, Delcher, Kasif & White, NAR 26, 1998.
Probability of the target position depends on a variable number of previous positions (sometimes 2 bases, sometimes 3, 4, etc.)
How many is determined by the specific context.
E.g., for context ggtta the next position might depend on previous 3 bases tta .
But for context catta all 5 bases might be used.
Comes from speech recognition community.
ggtta

21. Real IMMs
Model has additional probabilities, λ, that determine which parts of the context to use.
E.g., the probability of g occurring after context atca is:
Comes from natural language community to compute probability of meanings of words. E.g., trunk.

22. Real IMMs
Result is a linear combination of different Markov orders: where
Can view this as interpolating the results of different-order models.
The probability of a sequence is still the probability of the bases in the sequence.

23. Real IMMs
Problem: How to determine the λ’s (or equivalently the bj’s)?
Traditionally done with EM algorithm using cross-validation (deleted estimation).
Slow
Hard to understand results
Overtraining can be a problem
We will cover EM later as part of HMMs

24. Glimmer IMM Glimmer assumes:
Longer context is always better
Only reason not to use it is undersampling in training data.
If sequence occurs frequently enough in training data, use it, i.e.,
Otherwise, use frequency and χ2 significance to set λ.
Interpolation is always between only 2 adjacent model lengths.

25. More Precisely
Suppose context of length k+1 occurs a times, a<400, with target distribution D1; and context of length k occurs ≥400 times, with target distribution D2
Let p be the χ2 probability that D1 differs from D2
Then the interpolated distribution will be:
This formula computes the probability of a base B at any position in a sequence, given the context preceding that position
This formula is repeatedly invoked for all positions in an ORF

26. IMMs vs Fixed-Order Models
Performance
IMM generally should do at least as well as a fixed-order model.
Some risk of overtraining.
IMM result can be stored and used like a fixed-order model.
IMM will be somewhat slower to train and will use more memory.
Variable-Length Context
Target

27. Interpolated Context Model (ICM)
Introduced in Glimmer 2.0 Delcher, Harmon, et al., Nucl. Acids Res. 27, 1999.
Doesn’t require adjacent bases in the window preceding the target position.
Choose set of positions that are most informative about the target position.
Consider pairs of positions—context & target. Choose max mutual-information position.
Then repeat
Variable-Position Context
Target

28. ICM
For all windows compare distribution at each context position with target position
Choose position with max mutual information
Compare
Like IMM should generally be at least as good as fixed Markov model (which is a subcase).

29. ICM Continue for windows with fixed base at chosen positions
Recurse until too few training windows
Result is a tree—depth is # of context positions used
Then same interpolation as IMM, between node and parent in tree
Compare

30. Sample ICM Model
ver = len = 12 depth = 7 periodicity = 3 nodes = 21845
0 ---|---|---|*-?
1 ---|---|---|a*?
2 ---|---|---|c*?
3 ---|---|---|g*?
4 ---|---|---|t*?
5 ---|---|--*|aa?
6 ---|---|--*|ac?
7 ---|---|--*|ag?
8 ---|---|*--|at?
9 ---|---|--*|ca?
|---|--*|cc?
|---|--*|cg?
|---|--*|ct?
|---|--*|ga?
|---|--*|gc?
|---|--*|gg?
|---|--*|gt?
|---|--*|ta?
|---|--*|tc?
|---|--*|tg?
|---|-*-|tt?
|---|-*a|aa?
|---|-*c|aa?
|---|-*g|aa?
|--*|--t|aa?
|---|-*a|ac?

31. ICM ICM’s not just for modeling genes
Can use to model any set of training sequences
Doesn’t even need to be DNA
Have used recently to finding contaminant sequences in shotgun sequencing projects.
Used a non-periodic (i.e., homogeneous) model

32. Fixed-Length Sequences and ICMs
Array of ICM’s―a different one for each position in fixed-length sequence.
Can model fixed regions around transcription start sites or splice sites.
Positions in region can be permuted.
Can do because code is now more object-oriented.
Growing window instead of sliding it.

33. Overlapping Orfs Glimmer1 & 2 used rules.
For overlapping orfs A and B, the overlap region AB is scored separately:
If AB scores higher in A’s reading frame, and A is longer than B, then reject B.
If AB scores higher in B’s reading frame, and B is longer than A, then reject A.
Otherwise, output both A and B with a “suspicious” tag.
Also try to move start site to eliminate overlaps.
Leads to high false-positive rate, especially in high-GC genomes.

34. Glimmer 2.0 Overlap Comments
OrfID Start End Frame Len Score Comments [+1 L=1191 r=-1.151] [ShadowedBy #3] [-1 L=1560 r=-1.317] [Contains #2] [LowScoreBy #4 L=257 S=0] [+2 L= 891 r=-1.156] [OlapWith #3 L=257 S=99] [+2 L=1152 r=-1.184] [+1 L= 582 r=-1.223] [DelayedBy #5 L=216] [+1 L=2115 r=-1.181] [OlapWith #9 L=2103 S=99] [-1 L=2121 r=-1.365] [LowScoreBy #8 L=2103 S=0] [+3 L=2526 r=-1.155] [+1 L= 780 r=-1.217] [+1 L= 798 r=-1.192] [+1 L=1029 r=-1.165] [ShorterThan #15 L=865 S=99] [-3 L=1449 r=-1.295] [LowScoreBy #13 L=865 S=0] [LowScoreBy # [+3 L=1056 r=-1.228] [OlapWith #15 L=585 S=99] [DelayedBy #13 L= [+2 L= 840 r=-1.161] [-3 L=1011 r=-1.230] [+1 L= 327 r=-1.284] [+3 L=1731 r=-1.175] [+2 L=1767 r=-1.266] [DelayedBy #20 L=1263] [+2 L=22569 r=-1.144] [-1 L=1002 r=-1.200] [ShadowedBy #26] [+1 L=1443 r=-1.401] [Contains #25] [LowScoreBy #27 L=167 S=0] [+2 L= 453 r=-1.261] [ShorterThan #26 L=167 S=99] [+1 L=1335 r=-1.169] [+1 L= 525 r=-1.153] [-1 L= 567 r=-1.298] [DelayedBy #29 L=363] [-2 L= 405 r=-1.275] [-1 L= 282 r=-1.273] [+3 L= 792 r=-1.178]

35. Glimmer3
Uses a dynamic programming algorithm to choose the highest-scoring set of orfs and start sites.
Not quite an HMM
Allows small overlaps between genes
“small” is user-defined
Scores of genes are not necessarily probabilities.
Score includes component for likelihood of start site

36. Reverse Scoring
In Glimmer3 orfs are scored from 3’ end to 5’ end, i.e., from stop codon back toward start codon.
Helps find the start site.
The start should appear near the peak of the cumulative score in this direction.
Keeps the context part of the model entirely in the coding portion of gene, which it was trained on.

37. Reverse Scoring
Green lines are start positions. Red is cumulative log-odds score from stop codon.

38. Finding Start Sites
Can specify a position weight matrix (PWM) for the ribosome binding site.
Used to boost the score of potential start sites that have a match.
Would like to try:
A fixed-length ICM model of start sites.
A decision-tree model of start sites.
Hard to get enough reliable data.
Glimmer2 had a hybridization-energy calculation with 16sRNA tail sequence that didn’t work well.
Can get PWM’s for RBS signals from program like ELPH.
Where may want to diverge from HMM formalism by overweighting or thresholding the PWM score value.

39. Glimmer3 vs. Glimmer2
Table 3. Probability models trained on the output of the long-orfs program. Predictions compared to genes with annotated function.
Table 4. Probability models trained on the output of the long-orfs program. Predictions compared to all annotated genes.
Trained on all non-hypothetical orfs.

40. Other Glimmer3 Features
Can specify any set of start or stop codons
Including by NCBI translation table #
Can specify list of “guaranteed” genes
Or just orfs and let Glimmer choose the starts
Can specify genome regions to avoid
Guarantee no prediction will overlap these regions
Useful for RNA genes (tRNAs, rRNAs)
Output more meaningful orf scores.
Allow genes to extend beyond end of sequence
important for annotation of incomplete genomes
Program is now more modular and easier to modify.
Unfortunately, have slowed down the program.

41. Glimmer3 Output
----- Start Length Scores ID Frame of Orf of Gene Stop of Orf of Gene Raw Frame NC

42. Finding Initial Training Set
Glimmer1 & 2 have program long-orfs
It finds orfs longer than min-length that do not overlap other such orfs.
Current version automatically finds min-length.
Works OK for low- to medium-GC genomes
Terrible for high-GC genomes
Up to half the orfs can be wrong.
Uses first possible start site―even if orf is correct much of sequence isn’t.
Can use codon composition information in Glimmer3 version (similar to MED).

45. Running Glimmer3
#1 Find long, non-overlapping orfs to use as a training set
long-orfs --no_header -t 1.0 tpall.1con tp.longorfs
#2 Extract the training sequences from the genome file
extract --nostop tpall.1con tp.longorfs > tp.train
#3 Build the icm from the training sequences
build-icm -r tp.icm < tp.train
#4 Run first Glimmer
glimmer3 -o50 -g110 -t30 tpall.1con tp.icm tp.run1
#5 Get training coordinates from first predictions
tail +2 tp.run1.predict > tp.coords
#6 Create a position weight matrix (PWM) from the regions
# upstream of the start locations in tp.coords
upstream-coords.awk 25 0 tp.coords | extract tpall.1con - > tp.upstream
elph tp.upstream LEN=6 | get-motif-counts.awk > tp.motif
#7 Determine the distribution of start-codon usage in tp.coords
set startuse = `start-codon-distrib --3comma tpall.1con tp.coords`
#8 Run second Glimmer
glimmer3 -o50 -g110 -t30 -b tp.motif -P $startuse tpall.1con tp.icm tp

46. A novel application of Glimmer
P. didemni is a photosynthetic microbe that lives as an endosymbiont in the sea squirt > patella
P. didemni can only be cultured in L. patella cells
L. patella (sea squirt)

47. A novel application of Glimmer
Generated 82,337 shotgun reads
Bacterial genome 5 Mb
Host genome estimated at 160 Mb
Depth of coverage therefore much greater for bacterial contigs
Singleton reads primarily belong to host

48. A novel application of Glimmer
Create training sets by classifying reads from scaffolds > 10kb as bacterial
36,920 reads
Reads where both read and mate were singletons were treated as sea squirt
21,276 reads
21,141 reads unclassified

49. A novel application of Glimmer
Train a non-periodic IMM on both sets of data
2 IMMs created
Then classify reads using the ratio of scores from the two IMMs
In a 5-fold cross-validation, classification accuracy was
98.9% on P. didemni reads
99.9% on L. patella reads

50. A novel application of Glimmer
Finally, re-assemble using ONLY bacterial reads
Original assembly:
65 scaffolds of 20 Kb or longer
total scaffold length 5.74 Mb
Improved assembly:
58 scaffolds of 20 Kb or longer
total scaffold length 5.84 Mb

51. Acknowledgements
Art Delcher Steven Salzberg Owen White (TIGR) Simon Kasif (Boston U.) Doug Harmon (Loyola College) Kirsten Bratke Edwin Powers (Johns Hopkins U.) Dan Haft (TIGR) Bill Nelson (TIGR)