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
Outline
• A (very) brief overview of microbial gene-finding
– 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
Step One
• Find open reading frames (ORFs).
…TAGAAAAATGGCTCTTTAGATAAATTTCATGAAAAATATTGA…
Stop
codon
Stop
codon
Step One
• Find open reading frames (ORFs).
Reverse
strand
Stop
codon
…ATCTTTTTACCGAGAAATCTATTTAAAGTACTTTTTATAACT…
…TAGAAAAATGGCTCTTTAGATAAATTTCATGAAAAATATTGA…
Stop
codon
Shifted
Stop
• But ORFs generally overlap …
Stop
codon
Campylobacter jejuni RM1221 30.3%GC
All ORFs on both strands shown
- color indicates reading frame
Longest ORFs likely to be protein-coding genes
Note the low GC content
Campylobacter jejuni RM1221 30.3%GC
Purple lines are the predicted genes
Purple ORFs show annotated (“true”) genes
Campylobacter jejuni RM1221 30.3%GC
Mycobacterium smegmatis MC2 67.4%GC
Note what happens in a high-GC genome
Campylobacter jejuni RM1221 30.3%GC
Mycobacterium smegmatis MC2 67.4%GC
Purple lines show annotated genes
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
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
Bacterial Replication
Early replication
Theta structure
Termination of Replication
E. coli
B.
subtilis
Borrelia burgdorferi (Lyme disease pathogen)
GC-skew plot
Codon Composition
Nucleotide variation at codon position:
Mycobacterium smegmatis
Campylobacter jejuni
Codon Position
Codon Position
1
2
a
19%
23%
6%
9%
c
27%
28%
48%
14%
10%
g
42%
20%
39%
33%
44%
t
12%
28%
7%
1
2
3
a
36%
36%
36%
c
13%
17%
g
30%
t
21%
3
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
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 nonorfs) and make the score be the ratio of the
probabilities (i.e., the odds) of the two models.
– Use logs to avoid underflow
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
Target
sequence:
Context
CTAGAT
would be:
P(G | CTA)  P(A | TAG)  P(T | AGA)
Target
Context
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.
Fixed-Length
Context
Target
…ACGTAGTTCAGTA…
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.
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.
ggtta
• 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.
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:
 (atca)P (g | atca)
 (1   (atca))[(tca)P (g | tca)
 (1   (tca))[(ca)P (g | ca)
 (1   (ca))[ (a)P (g | a)
 (1   (a))P (g)]]]
Real IMMs
• Result is a linear combination of different
Markov orders:
b4P(g | atca)  b3P(g | tca)  b2P(g | ca)
 b1P(g | a)  b0P(g)
where
b0  b1  b2  b3  b4  1
• 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.
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
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.,   1
• Otherwise, use frequency and χ2 significance to
set λ.
• Interpolation is always between only 2 adjacent
model lengths.
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:
pa 
 pa 


 D1   1 
 D2
 400 
 400 
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
IMMs vs Fixed-Order Models
• Performance
– IMM generally should do at least as well as a fixedorder model.
– Some risk of overtraining.
• IMM result can be stored and used like a fixedorder model.
• IMM will be somewhat slower to train and will
use more memory.
Variable-Length
Context
Target
…ACGTAGTTCAGTA…
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.
Variable-Position
Context
Target
…ACGTAGTTCAGTA…
ICM
• For all windows compare distribution at each context position
with target position
*************
Compare
• Choose position with max mutual information
p ( x, y )
I ( X ; Y )   p( x, y ) log
p( x) p( y )
x
y
ICM
• Continue for windows with fixed base at chosen
A
positions
**** ********
Compare
• 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
Sample ICM Model
ver = 2.00 len = 12 depth = 7
0 ---|---|---|*-? 0.0260
1 ---|---|---|a*? 0.0769
2 ---|---|---|c*? 0.0243
3 ---|---|---|g*? 0.0597
4 ---|---|---|t*? 0.0277
5 ---|---|--*|aa? 0.0051
6 ---|---|--*|ac? 0.0046
7 ---|---|--*|ag? 0.0101
8 ---|---|*--|at? 0.0037
9 ---|---|--*|ca? 0.0019
10 ---|---|--*|cc? 0.0090
11 ---|---|--*|cg? 0.0035
12 ---|---|--*|ct? 0.0083
13 ---|---|--*|ga? 0.0057
14 ---|---|--*|gc? 0.0057
15 ---|---|--*|gg? 0.0056
16 ---|---|--*|gt? 0.0096
17 ---|---|--*|ta? 0.0034
18 ---|---|--*|tc? 0.0062
19 ---|---|--*|tg? 0.0052
20 ---|---|-*-|tt? 0.0076
21 ---|---|-*a|aa? 0.0035
22 ---|---|-*c|aa? 0.0037
23 ---|---|-*g|aa? 0.0052
24 ---|--*|--t|aa? 0.0060
25 ---|---|-*a|ac? 0.0031
periodicity =
0.225 0.240
0.243 0.165
0.237 0.294
0.219 0.240
0.209 0.245
0.320 0.202
0.195 0.118
0.180 0.207
0.236 0.147
0.199 0.246
0.297 0.189
0.180 0.406
0.279 0.327
0.277 0.326
0.233 0.158
0.119 0.252
0.207 0.232
0.205 0.170
0.179 0.238
0.183 0.338
0.244 0.244
0.376 0.199
0.313 0.197
0.283 0.199
0.259 0.231
0.176 0.138
3 nodes = 21845
0.358 0.177
0.501 0.092
0.264 0.205
0.407 0.134
0.291 0.256
0.478 0.000
0.541 0.146
0.613 0.000
0.400 0.217
0.303 0.253
0.308 0.207
0.267 0.148
0.189 0.206
0.397 0.000
0.473 0.136
0.417 0.213
0.359 0.203
0.400 0.224
0.290 0.293
0.323 0.156
0.194 0.318
0.425 0.000
0.490 0.000
0.517 0.000
0.510 0.000
0.519 0.166
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
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.
ACGTAGTTCAGTAG
ACGTAGTTCAGTAG
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.
Glimmer 2.0 Overlap Comments
OrfID
2
3
4
5
7
8
9
10
11
12
13
15
16
17
18
19
20
21
23
25
26
27
28
29
30
31
32
33
Start
499
1977
1721
2624
3775
4501
6633
6648
9229
10411
11215
12827
12243
13298
15143
15193
15519
17249
19052
42693
41623
42899
43351
45223
45261
46261
46701
46752
End
1689
418
2611
3775
4356
6615
4513
9173
10008
11208
12243
11379
13298
14137
14133
15519
17249
19015
41620
41692
43065
43351
44685
45747
44695
45857
46420
47543
Frame Len
Score
[+1 L=1191 r=-1.151]
[-1 L=1560 r=-1.317]
[+2 L= 891 r=-1.156]
[+2 L=1152 r=-1.184]
[+1 L= 582 r=-1.223]
[+1 L=2115 r=-1.181]
[-1 L=2121 r=-1.365]
[+3 L=2526 r=-1.155]
[+1 L= 780 r=-1.217]
[+1 L= 798 r=-1.192]
[+1 L=1029 r=-1.165]
[-3 L=1449 r=-1.295]
[+3 L=1056 r=-1.228]
[+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]
[+2 L=22569 r=-1.144]
[-1 L=1002 r=-1.200]
[+1 L=1443 r=-1.401]
[+2 L= 453 r=-1.261]
[+1 L=1335 r=-1.169]
[+1 L= 525 r=-1.153]
[-1 L= 567 r=-1.298]
[-2 L= 405 r=-1.275]
[-1 L= 282 r=-1.273]
[+3 L= 792 r=-1.178]
Comments
[ShadowedBy #3]
[Contains #2] [LowScoreBy #4 L=257 S=0]
[OlapWith #3 L=257 S=99]
[DelayedBy #5 L=216]
[OlapWith #9 L=2103 S=99]
[LowScoreBy #8 L=2103 S=0]
[ShorterThan #15 L=865 S=99]
[LowScoreBy #13 L=865 S=0] [LowScoreBy #16
[OlapWith #15 L=585 S=99] [DelayedBy #13 L=
[DelayedBy #20 L=1263]
[ShadowedBy #26]
[Contains #25] [LowScoreBy #27 L=167 S=0]
[ShorterThan #26 L=167 S=99]
[DelayedBy #29 L=363]
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
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.
Reverse Scoring
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.
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.
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
Glimmer3 Output
ID
0007
0008
0009
0010
0011
0012
0013
Frame
...
-2
-3
-3
-2
-3
+3
-2
+1
-2
-3
-2
-2
+3
+1
-3
+1
+2
-2
+3
-2
+1
+1
-3
+3
+1
-2
-2
-2
----- Start ----of Orf of Gene
...
...
10516
10459
10787
10781
11132
11120
12058
12055
12437
12371
8109
8142
12763
12724
12850
12877
13165
13036
13649
13634
13675
13663
13972
13972
12633
12642
14194
14323
14669
14663
14482
14548
14570
14642
14947
14935
14655
14718
15115
15100
15004
15070
15364
15475
15701
15671
15531
15591
15631
15649
15922
15898
16561
16561
16741
16711
Stop
...
10337
10629
11004
11936
12249
12632
12545
12996
12905
13473
13256
13850
14087
14454
14088
14673
14806
14696
14954
14975
15288
15594
15000
15728
15762
15719
16307
16562
--- Length ---of Orf of Gene
...
...
177
120
156
150
126
114
120
117
186
120
4521
4488
216
177
144
117
258
129
174
159
417
405
120
120
1452
1443
258
129
579
573
189
123
234
162
249
237
297
234
138
123
282
216
228
117
699
669
195
135
129
111
201
177
252
252
177
147
-------------- Scores -----------Raw Frame +1 +2 +3 -1 -2 -3 NC
...
...
1.14
0 - - 99 - 0 - 0
-10.69
0 - - 99 - - 0 0
-13.54
0 - - 99 - - 0 0
-2.25
0 - - 99 - 0 - 0
-13.68
0 - - 99 0 - 0 0
16.05
99 - - 99 - - - 0
4.17
99 - - - - 99 - 0
-16.13
0 0 - 99 - - - 0
-1.31
0 - - 99 - 0 - 0
-7.11
0 - - 99 - 0 0 0
2.83
0 - - 99 - 0 - 0
4.25
0 - - 99 - 0 - 0
15.20
99 - - 99 - - - 0
1.62
0 0 - - - - 99 0
13.36
99 - - - - - 99 0
3.07
0 0 - - - - 99 0
-4.35
0 - 0 - - - - 99
16.17
99 - - 0 - 99 - 0
3.06
99 - - 99 - - - 0
-7.02
0 - - - - 0 - 99
-2.46
0 0 - - - - 99 0
-5.48
0 0 - - - - 99 0
13.07
99 - - - - - 99 0
-8.84
0 - - 0 - - - 99
-0.69
3 3 - - - - - 96
-0.41
6 - - - - 6 - 93
1.73
0 - - 99 - 0 - 0
1.72
0 - - 99 - 0 - 0
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).
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
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)
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
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
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
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
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)