Gene 372 (2006) 171 – 181
www.elsevier.com/locate/gene

Prediction of non-coding and antisense RNA genes in
Escherichia coli with Gapped Markov Model
Nozomu Yachie a,b , Koji Numata a,b , Rintaro Saito a,c,⁎, Akio Kanai a,c , Masaru Tomita a,b,cabInstitute for Advanced Biosciences, Keio University, Tsuruoka, 997-0035, Japan
Bioinformatics Program, Graduate School of Media and Governance, Keio University, Fujisawa, 252-8520, Japanc
Department of Environmental Information, Keio University, Fujisawa, 252-8520, Japan
Received 19 October 2005; received in revised form 2 December 2005; accepted 28 December 2005
Available online 24 March 2006
Received by T. Gojobori

Abstract
A new mathematical index was developed to identify and characterize non-coding RNA (ncRNA) genes encoded within the Escherichia coli
(E. coli) genome. It was designated the GMMI (Gapped Markov Model Index) and used to evaluate sequence patterns located at the separate
positions of consensus sequences, codon biases and/or possible RNA structures on the basis of the Markov model. The GMMI was able toseparate a set of known mRNA sequences from a mixture of ncRNAs including tRNAs and rRNAs. Consequently, the GMMI was employed to
predict novel ncRNA candidates. At the beginning, possible transcription units were extracted from the E. coli genome using consensus sequences
for the sigma70 promoter and the rho-independent terminator. Then, these units were evaluated by using the GMMI. This identified 133 candidate
ncRNAs, which contain 29 previously annotated small RNA genes and 46 possible antisense ncRNAs. Furthermore 12 transcripts (including fiveantisense RNAs) were confirmed according to the expression analysis. These data suggests that the expression of small antisense RNAs might bemore common than previously thought in the E. coli genome.
© 2006 Elsevier B.V. All rights reserved.
Keywords: Bioinformatics; Markov model; Small RNA (sRNA); Sigma70 promoter; Rho-independent terminator

1. Introduction
Genome sequencing projects have identified numerous
examples of non-coding RNA (ncRNA) genes (Eddy, 2001).
ncRNA molecules do not encode protein products, but arethought to have structural, regulatory or catalytic properties.
Along with well-known RNA molecules, such as transfer RNAs(tRNAs) and ribosomal RNAs (rRNAs), many classes ofncRNAs have been identified by the experimental and bioinformatics approaches (Eddy, 2001). Among bacteria, particu-

Abbreviations: ncRNA, non-coding RNA; sRNA, small RNA; saRNA,small antisense RNA; tRNA, transfer RNA; E. coli, Escherichia coli.
⁎ Corresponding author. Institute for Advanced Biosciences, Keio University,
Tsuruoka, Yamagata 997-0017, Japan. Tel.: +81 235 29 0522; fax: +81 235 29
0525.
E-mail address: rsaito@sfc.keio.ac.jp (R. Saito).
0378-1119/$ - see front matter © 2006 Elsevier B.V. All rights reserved.
doi:10.1016/j.gene.2005.12.034

larly Escherichia coli (E. coli), many studies of small RNAs(sRNAs) have focused on the intergenic regions (Wassarman etal., 1999; Argaman et al., 2001; Carter et al., 2001; Rivas et al.,
2001; Wassarman et al., 2001; Chen et al., 2002; Tjaden et al.,2002). Recent articles have estimated that approximately 1000sRNA candidates of unknown function are encoded in the E. coligenome (Hershberg et al., 2003; Vogel et al., 2003). However,
only 62 of these have been detected experimentally throughanalysis of RNA expression in E. coli. Vogel et al. performed
global sRNA identification using the shotgun cloning approach
known as experimental RNomics. They suggested that 5% of the
final contigs mapped to the strand complementary to coding
regions within the E. coli genome (Vogel et al., 2003). Althoughseveral small antisense RNAs (saRNAs) are known to betranscribed from loci which differ from their target mRNAs in
E. coli (trans-antisense) (Carpousis, 2003), few specificexamples transcribed in the opposite direction from the same

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N. Yachie et al. / Gene 372 (2006) 171–181

Fig. 1. Transcription-unit model. The transcription-unit model is defined as the region between the sigma70 promoter motif +10 (‘start pos.’) and the end of the rhoindependent-terminator motif (‘end pos.’), which ranges in size from 70 to 500 nt. The ‘filtering start pos.’, which is the original start position of the promoter motif,
and the ‘filtering end pos.’, which is 7nt upstream from the original end of the terminator motif, are defined after filtering out those that overlap with ORF regions on
the same DNA strand.

genomic loci (cis-encoded saRNAs) have been documented(Kawano et al., 2002; Vogel et al., 2003). Moreover, all previoussRNA predictions based on the bioinformatics approach havefocused only on the intergenic regions as defined by geneannotation. A bioinformatics based approach for the predictionof saRNAs has yet to be reported.

Fig. 2. Isolation of each RNA category using the GMMI. The histograms represent the distributions of the Z score of GMMI for each RNA category (A, B and D). The
Markov-order (e) was 2 and the gap space parameter (g) was 10. (A) The GMMI is trained using tRNA sequences. (B) The GMMI is trained using rRNA sequences.
(C) The best correlation coefficients (BCC) for the quality measure of every gap space parameter (g) where GMMI is trained using mRNAs. (D) Separation of thencRNAs from the mRNA category. The GMMI is trained using mRNA sequences. An enlargement of the histogram is presented on the left. The arrow indicates thethreshold value (− 2.34) that isolates ncRNAs with over 80% sensitivity and selectivity.

N. Yachie et al. / Gene 372 (2006) 171–181

173

possible structural motifs, based on the Markov model. The
biochemical functions of ncRNAs such as tRNA and rRNA
molecules are thought to depend on not only their sequencemotifs but also their higher order structures. As nucleotides at
separate positions can interact with one another, RNA sequence,
structure and spatial relationships, such as hydrogen bonding
should be considered. Let R be the RNA sequence to beevaluated (‘test sequence’), which is presented as a string of ncharacters: R = r1r2…rk…rn (rk ⊂ (A, C, G, U), 1 ≤ k ≤ n). The
segment from ri to rj in the sequence can be presented as: Ri,
j = riri+1…rj (1 ≤ i ≤ j ≤ n). The GMMI score (G) with theconstant numbers of the Markov-order (e) and the gap spaceparameter (g) of R is defined as the product of the conditional
probability of observing each nucleotide P(rk|Rk-f-e,k-f-1), given asequence pattern composed of e nt located in f nt upstream(0 ≤ f ≤ g) divided by the frequency of each nucleotide P(rk) inthe ‘training set’:G¼

1Pðrk jRk−f −e;k−f −1 Þ nk;fPðrk Þ

j

The score G evaluates the likelihood of the given sequence
being related to our Gapped Markov Model which isconstructed using the ‘training set’. A score below 1 indicatesthat a given sequence has a specific pattern that is not observedin the training set, whereas a score above 1 indicatesconcordance (for more details, see Supplementary materials).
Fig. 3. Schematic representation of the computational procedure used to extract
putative ncRNAs.

2.2. Sequence data
We have developed a new approach for the detection ofsmall ncRNA which we have designated the Gapped Markov
Model Index (GMMI). This allows the detection of ncRNAs not
only in intergenic regions, but also in the antisense strands ofcoding regions. The GMMI evaluates whether an RNA
sequence is coding or non-coding on the basis of the Markov
model. The successful identification of previously known
ncRNA sequences from the total RNA sequences was used tovalidate the GMMI. Subsequently, the GMMI allowed theidentification of novel ncRNA candidates within predictedtranscription units, and some of these molecules were encoded
as antisense RNAs. We detected the expression of some ncRNA
candidates, including saRNAs using RT-PCR and Northernhybridization.

The complete E. coli genome sequence and a set of mRNAs,
documented ncRNAs and sRNAs, were downloaded from
GenBank via the National Center for Biotechnology Information (NCBI) ftp server. In addition, details of 62 previously
identified sRNAs of E. coli K12MG1655 were obtained fromthe literature (Hershberg et al., 2003; Vogel et al., 2003). Openreading frames (ORFs) were categorized as mRNAs. The RNA
list we constructed consisted of 4311 mRNAs (ORFs) and 163
ncRNAs (86 tRNAs, 22 and 55 sRNAs). Recently, researchers
have compiled a set of 62 sRNA genes in E. coli and we adopted
55 of these, excluding those from transcription units that wereneither predicted nor identified experimentally (Hershberg etal., 2003; Vogel et al., 2003).
2.3. Prediction of putative transcription units

2. Materials and methods
2.1. Gapped Markov Model Index (GMMI)
A novel approach designated the Gapped Markov Model
Index (GMMI) was developed to mathematically distinguishncRNA sequences from other mRNA sequences. The aim wasto evaluate the specificity of a RNA sequence (‘test sequence’)by the certain set of RNAs (‘training set’) with the combinationsof specific nucleotide sequence patterns located at separate
positions of consensus sequences, codon biases and/or its

At the genome level within E. coli K12MG1655 we initiallypredicted transcription units between the sigma70 promotersand rho-independent terminators. The prediction of putativetranscription units was performed using a recently reported
method (Chen et al., 2002). The program pftools2.2, obtained
from the ftp server of the Swiss Bioinformatics Institute, wasused to identify the putative sigma70 promoters; a profile
describing the major class of E. coli sigma70 promoters was
included in the software. We used the reported threshold of
50, which is capable of identifying approximately 70% of174

N. Yachie et al. / Gene 372 (2006) 171–181

Table 1
Intergenic ncRNA candidates
Name

Minutes

StrandLeft boundary

Right boundary

LengthAdjacent genes

NC004
NC005
NC007
NC009
NC010
NC011
NC012
NC013
NC014
NC015
NC016
NC019
NC020
NC021
NC022
NC023
NC025
NC027
NC030
NC032
NC034
NC035
NC037
NC038
NC039
NC040
NC041
NC044
NC046
NC048
NC050
NC051
NC052
NC053
NC056
NC059
NC060
NC061
NC063
NC064
NC065
NC066
NC068
NC069
NC072
NC073
NC076
NC077
NC078
NC079
NC082
NC083
NC084
NC087
NC089
NC093
NC094
NC095
NC096
NC097
NC098
NC099
NC101
NC102

4.89
4.93
5.1
6.82
7.12
9.29
9.92
12.01
12.28
12.61
16.21
16.8
16.83
18.42
19.39
19.94
23.64
25.77
26.09
26.21
26.41
27.06
31.24
31.53
31.53
31.54
32.1
33.74
34.4
35.27
35.97
37.49
37.99
38.88
41.02
43.97
44.02
44.31
44.4
44.91
46.36
46.69
49.23
53.04
53.2
53.49
53.7
54.23
54.29
55.8
57.96
58.15
59.35
60.28
63.48
66.79
67
69.26
71.47
72.39
73.45
73.74
74.74
78.79

−
+
+
+
−
+
−
+
−
+
+
+
−
+
+
−
−
−
+
−
−
+
+
+
−
+
−
+
+
−
−
+
−
−
+
+
+
−
+
+
+
+
+
−
−
−
+
−
+
−
−
+
+
−
+
+
+
+
−
−
−
−
+
−

227,062
228,966
236,915
316,420–316,554
330,773431,272
460,539557,197–557,214
569,952
584,926–585,013
752,115–752,189
779,739
780,911–781,067
854,993
899,841925,104–925,114
1,096,423–1,096,795
1,195,651
1,210,552
1,216,280
1,225,360
1,255,698
1,449,530
1,463,146
1,463,154
1,463,146–1,463,296
1,489,517
1,565,327–1,565,393
1,596,200
1,636,282–1,636,293
1,669,100
1,739,231–1,739,268
1,762,744
1,804,162
1,903,333–1,903,411
2,040,100
2,042,565
2,055,997
2,060,082–2,060,276
2,083,596
2,151,204
2,166,152–2,166,429
2,284,229
2,460,719
2,468,523–2,468,526
2,481,604
2,491,623–2,491,661
2,516,049–2,516,062
2,518,865–2,518,946
2,588,772–2,588,779
2,689,214
2,697,660–2,698,056
2,753,612
2,796,832
2,945,350–2,945,408
3,098,850
3,108,325–3,108,385
3,213,241
3,315,695–3,315,746
3,358,426
3,407,693
3,420,887–3,421,065
3,467,665–3,467,686
3,655,593

227,343
229,048–229,058
237,080
316,626
330,958
431,448460,641
557,328570,038–570,045
585,219752,395
780,109781,243855,137
899,956–900,055
925,225–925,337
1,096,897–1,096,937
1,195,827–1,196,071
1,210,856–1,210,864
1,216,367–1,216,383
1,225,440
1,255,895
1,449,620
1,463,221–1,463,398
1,463,301–1,463,317
1,463,398
1,489,624
1,565,515
1,596,325
1,636,395
1,669,214
1,739,378
1,762,848
1,804,289
1,903,517
2,040,182
2,042,774
2,056,130
2,060,398–2,060,405
2,083,717
2,151,377
2,166,509
2,284,353
2,460,827–2,461,092
2,468,598
2,481,743–2,481,798
2,491,746
2,516,271
2,519,316–2,519,385
2,589,005–2,589,064
2,689,355
2,698,138
2,754,061
2,796,960
2,945,740
3,098,919
3,108,501–3,108,591
3,213,359
3,315,933–3,316,010
3,358,513–3,358,567
3,407,767–3,408,031
3,421,330
3,467,764
3,655,672–3,655,904

282
83–93
166
73–207
186
177
103
115–132
87–94
207–294
207–281371
177–333
145
116–215
112–234
103–515
177–421
305–313
88–10481
198
91
76–253
148–164
103–253108
123–189126
103–114115
111–148
105128
107–18583
210
134
123–324122
174
81–358125
109–374
73–76
140–195
86–124
210–223
371–521
227–293142
83–479450129
333–39170
117–267
119
188–316
88–14275–339266–444
79–10080–312yaeD+/yafB+
yaeD+/yafB+
dnaQ+/yafT+
ykgA−/ykgB−
betT+/yahA+
tsx−/yajI−
lon+/hupB+
folD−/sfmA+
ybcK+/ybcL+
ompT−/envY−
ybgD−/gltA−
ybgF+/nadA+
ybgF+/nadA+
ybiS−/ybiT+
artJ−/artM−
clpA+/infA−
ycdU+/ycdW+
icdA+/ymfD−
mcrA+/ycgW−
ymgC+/b1168+
minC−/ycgJ+b1202−/ychF−
tynA−/maoC−
b1400+/yi222−
b1400+/yi222−
b1400+/yi222−
cybB+/ydcA+
b1490−/b1491−
ydeK−/ydeV−b1551−/cspI−
ynfM+/asr+
ydhC+/cfa+
ydiC−/b1685−
b1722−/pfkB+b1820+/b1821+b1973+/b1974+b1976+/b1978+b1983+/yeeO−nac−/erfK−
yeeE−/yeeF−
b2073−/b2074+
b2085−/b2086+
yejM+/yejO−
fadL+/b2345+
b2352+/b2353+
emrK−/evgA+b2374−/b2375−
b2395−/yfeC+
gltX−/xapR−
acrD+/yffB+
yfhK−/purL−
yfhL+/acpS−
smpB+/intA+
stpA−/b2670+
mltA−/b2817−
ansB−/yggN−
yqgA+/yghD−
ygjF−/yqjH−
b3170−/argG+
gltD+/gltF+
prmA+/yhdG+
yhdZ+/b3279+
yheB−/tufA−
hdeD+/yhiE+

N. Yachie et al. / Gene 372 (2006) 171–181

175

Table 1 (continued)
Name

Minutes

StrandLeft boundary

Right boundary

LengthAdjacent genes

NC103
NC106
NC107
NC111
NC114
NC115
NC116
NC117
NC119
NC120
NC121
NC122
NC123
NC124
NC125
NC126
NC127
NC128
NC129
NC131
NC132
NC133

78.79
79.15
79.88
81.19
84.98
85.78
85.78
85.87
87
87.04
89.79
89.83
89.87
90.72
90.76
91.8
91.8
93.98
94.07
95.6
96.86
98.16

−
−
−
−
−
+
−
+
−
+
−
−
+
−
+
−
+
−
+
−
+
+

3,655,593–3,655,654
3,672,054
3,706,245
3,766,712
3,942,630
3,979,979
3,979,984
3,983,895–3,983,935
4,036,401
4,038,191
4,165,949
4,167,523
4,169,310
4,208,925
4,210,713
4,258,959
4,258,840–4,259,054
4,360,127
4,364,372
4,435,129
4,493,956
4,553,924–4,553,945

3,655,725–3,655,904
3,672,228
3,706,321
3,766,784–3,766,866
3,942,911
3,980,460
3,980,295
3,984,256
4,036,682
4,038,264–4,038,274
4,166,028–4,166,158
4,167,804
4,169,383–4,169,393
4,209,206
4,210,784
4,259,184
4,259,130
4,360,202
4,364,454
4,435,243–4,435,325
4,494,099
4,554,181

72–312
175
77
73–155282
482
312
322–362282
74–84
80–210
282
74–84
282
72
226
77–291
7683
115–197144
237–258

hdeD+/yhiE+
yhjD+/yhjE+
dppA−/yhjW−
yibG+/b4549+
yiePs−/yifDA−
yifK+/aslB+
yifK+/aslB+
aslA−/hemY−
hemG+/mobB−
hemG+/mobB−
murI+/murB+
murI+/murB+
murI+/murB+
purH−/yjaA+
purH−/yjaA+
yjbM+/yjbN+
yjbM+/yjbN+
cadC−/yjdC−
dcuA−/b4139−
cysQ+/ytfI+
b4269−/intB+
b4325−/b4326+

Intergenic ncRNA candidates are listed along with their position, strand, length and the names of the adjacent genes. Candidates located on the plus strand are
indicated by ‘+’ and candidates located on the minus strand are indicated by ‘−’ in the E. coli genome. The left boundary denotes the transcription start position on
the ‘+’ strand or the transcription stop position on the ‘−’ strand. The right boundary denotes the transcription start position on the ‘−’ strand or the transcription
stop position on the ‘+’ strand. Where multiple positions were detected, the range is indicated. The length is equal to the absolute value of the ‘left boundary’minus the ‘right boundary’. The intergenic region is defined by the both sides of ‘adjacent genes’. For example, NC013 is encoded in the intergenic region
delineated by the folD gene on the left-hand side, which is transcribed from the ‘−’ strand, and the sfmA gene on the right-hand side, which is transcribed from the‘+’ strand.

previously annotated promoters (Chen et al., 2002). In order to
identify putative rho-independent terminators, we predicted
their sequences and structural motifs using the RNAMotif
software (Macke et al., 2001). The structural specificationsof the motif model for the program and the optimal cut-off score
were obtained from previous work (Lesnik et al., 2001).
2.4. Identification of putative ncRNA genes using the GMMI
We used 4311 ORF sequences as the training set for ourmodel. The GMMI score was then calculated for every
putative transcription unit. The size of a transcription unit was
defined as the distance between the transcription start position, which is 10 nt downstream of the 3′ end of the sigma70 promoter (Fig. 1, ‘start pos.’), and the transcription stop
position, which is at the 3′ end of the rho-independent terminator (‘end pos.’). We limited the length of transcription units according to the sizes of the previously identified
sRNAs at between 70 and 500 nucleotides. We extracted
those having a Z score of the GMMI (e = 2 and g = 10) below
the threshold −2.34 (see Results section). We then discarded
candidates in which the 5′ ends of the sigma70 promoters(‘filtering start pos.’), or position − 7 with respect to the 3′
ends of the rho-independent terminators (‘filtering end pos.’),overlapped any ORF regions on the same DNA strand. This
computational schema and the GMMI program is implemented by Perl version 5 and runs under the G-language GAE
which provides a generic analysis workbench for bioinfor-

matics (Arakawa et al., 2003). The source codes are available
upon request.
2.5. Bacterial culture, RNA isolation
E. coli strain K12 MG1655 was used in this study. Cells weregrown anaerobically at 37°C in L broth (10 g tryptone, 5g yeast
extract and 0.5 g NaCl/l). After an overnight incubation, 10 mlof the bacterial culture (stationary phase) was transferred to
100ml of fresh medium and incubated to an approximate opticaldensity of 600 nm. Cells were harvested in two different growthphases; late log (0.7 b O.D.600 b 1.0) and early stationary phase(1.0 b O.D.600 b 2.0). Total RNA for RT-PCR was isolated using
the TRIzol Reagent (Invitrogen, Carlsbad, CA, USA) according
to the manufacturer's instructions. For Northern blot hybridization, E. coli pellets were initially treated with the RNAprotect
Bacteria Reagent (Qiagen GmbH, Hilden, Germany) forstabilizing RNA and then total RNA was isolated essentiallyusing the RNeasy Midi Kit (Qiagen), except that phenolchloroform extraction of RNA was performed for purifying
RNA instead of using the RNeasy Midi column.
2.6. RT-PCR
RT-PCR for the analysis candidate ncRNA expression was
carried out using the ReverTra Dash Kit (TOYOBO Biochemicals, Japan). PCR was performed for 25 cycles at 98°C (10s),
55°C (2s) and 74°C (30s). The PCR products were separated

176

N. Yachie et al. / Gene 372 (2006) 171–181

Table 2
Antisense ncRNA candidates
Name

Minutes

StrandLeft boundary

Right boundary

LengthSense genes

NC001
NC002
NC003
NC006
NC008
NC017
NC018
NC024
NC026
NC028
NC029
NC031
NC033
NC036
NC042
NC043
NC045
NC047
NC049
NC054
NC055
NC057
NC058
NC062
NC067
NC070
NC071
NC074
NC075
NC080
NC081
NC085
NC086
NC088
NC090
NC091
NC092
NC100
NC104
NC105
NC108
NC109
NC110
NC112
NC113

0.18
3.13
4.13
5.03
6.31
16.33
16.73
20.56
25.21
25.8
25.83
26.16
26.29
30.88
32.42
33.41
34.23
35.23
35.47
40.21
40.87
41.9
43
44.35
47.62
53.12
53.15
53.54
53.6
57.09
57.7
59.47
59.6
60.54
64.49
65.14
66.16
73.99
78.8
78.96
80.17
80.5
81.16
81.88
81.96

−
−
−
+
+
−
−
+
+
+
−
+
−
+
−
+
+
−
−
−
−
−
+
+
−
−
−
−
+
+
+
+
−
−
−
−
+
−
−
+
−
+
−
+
+

8471
145,382
191,751
233,493
293,013–293,138
757,821776,452–776,456
953,999–954,005
1,169,665
1,197,016–1,197,275
1,198,645
1,213,898
1,219,712
1,432,950
1,504,268
1,550,245
1,588,443
1,634,837
1,645,927
1,865,821
1,896,373
1,944,079
1,994,949–1,994,956
2,057,869
2,209,374–2,209,375
2,464,731
2,465,791
2,483,994
2,487,019
2,648,981
2,677,127
2,759,270
2,765,252
2,808,664
2,991,938
3,022,243
3,069,309
3,432,775
3,656,170
3,663,407
3,719,671
3,734,659–3,734,695
3,765,221
3,798,670–3,798,700
3,802,677–3,802,746

8575
145,470
191,935
233,843
293,508
758,150
776,537
954,097–954,104
1,169,745
1,197,437
1,198,751
1,214,112
1,219,823–1,220,056
1,433,214
1,504,343
1,550,428
1,588,652
1,635,085–1,635,206
1,646,313
1,866,092
1,896,538
1,944,183–1,944,219
1,995,086
2,057,991
2,209,572
2,464,921
2,466,224
2,484,232–2,484,410
2,487,246
2,649,059
2,677,477
2,759,665
2,765,409
2,808,743
2,992,101–2,992,111
3,022,327
3,069,486
3,432,935
3,656,242–3,656,368
3,663,802
3,719,812–3,719,834
3,735,077
3,765,463–3,765,491
3,798,769
3,803,001

105
89
185
351
371–496330
82–86
93–10681
163–422
107
215
112–345
265
76
184
210
249–370
387
272
166
105–141
131–138123
198–199
191
434
239–417
228
79
351
396
158
80
164–17485
178
161
73–199396
142–164
383–419
243–271
70–100
256–325

NC118
NC130

86.23
95

+
−

4,000,752
4,407,633

4,000,888
4,407,737–4,407,794

137
105–162

talB+ (M)yadE+ (M)pyrH+ (N)dniR− (M)yagL− (N), yagM− (C)b0725+ (C), sucA+ (N)tolA+ (M)ycaO− (C)mfd− (C)ymfE− (M)lit+ (M)b1163− (M)b1169+ (M)ynaF− (C)ydcN+ (M)yddM− (C)b1506− (N)ydfO+ (N)
dicA+ (N)yeaG+ (M)b1815+ (N)yebB+ (N)yecC− (C)cbl− (C)yehR+ (M)
intC+ (M)b2350+ (N)evgS+ (M)yfdE− (N)b2520− (M)yphF− (N)yfjK− (C)yfjO+ (C)ygaH+ (C)b2854+ (N)ygfO+ (N)pgk− (C)
fmt+ (C), sun+ (N)yhiE+ (M)yhiX− (N)t150+ (C)bax− (N)yibJ+ (M)rfaJ− (M)rfaS− (M),rfaP− (C)yigG− (N)yjfI+ (N)Antisense ncRNA candidates are listed along with their position, strand, length (for details, see the footnote to Table 1), and the names of the genes on the sense strand.
(N), (C) and (M) indicate the amino-terminal, carboxy-terminal and middle section of a sense gene, respectively. For example, NC113 is encoded on two antisense
regions, the middle of rfaS and the carboxy-terminal of rfaP.

using 3% NuSieve 3 : 1 agarose gel electrophoresis (Cambrex Bio
Science Rockland, CA, USA) and stained using ethidium
bromide (EtBr).
2.7. Northern blot hybridization
Total RNA (20 g per lane) was separated by electrophoresis
in 6% polyacrylamide gel containing 8 M Urea and transferred
to positively charged nylon membrane Hybond+ (Amersham

Biosciences, Piscataway, NJ, USA) by electroblotting. The
specific oligonucleotide probes (45 mer) for each gene werelabelled with BrightStar Psoralen-Biotin, nonisotopic labellingkit (Ambion, Austin, TX, USA). Membranes were hybridized
with these probes using a NorthernMax Hybridization kit (Ambion). The washing temperature was 42°C for genes NC051 andNC065, and 45 °C for genes NC086 and NC092, respectively.
Detection procedures followed the instructions of BrightStar
BioDetect (Ambion), a nonisotopic detection kit. Images were

N. Yachie et al. / Gene 372 (2006) 171–181
Table 3Documented sRNAs in our candidates
Name

Promoter score Terminator score Z score Annotations

NC005
NC007
NC019
NC023
NC025
NC043
NC052
NC060
NC061
NC062
NC063
NC068
NC077
NC078
NC082
NC084
NC089
NC094
NC095
NC096
NC099
NC107
NC115
NC117
NC120
NC123
NC125
NC128
NC132

52.65
65.1
58.58
74
77.56
51.46
50.27
74
72.81
64.51
62.14
62.14
72.81
69.25
53.83
59.76
59.76
66.29
58.58
69.85
52.65
69.25
60.95
54.43
52.65
52.65
52.65
65.1
55.02

− 9.82− 10.82− 4.1− 16.88− 15.35− 8.03− 13.26− 17.99− 11.82− 5.82− 8.78− 5.33− 10.88− 5.18− 11.95− 19.94− 6.36− 12.41− 16.31− 7.47− 18.42− 9.58− 12.56− 14.87− 20.21− 20.21− 11.52− 12.98− 6.71− 3.63− 2.46−3− 4.05− 3.21− 2.53− 2.38− 3.1− 3.96− 2.44− 3.49− 2.36− 3.01− 2.96− 2.73− 2.95− 2.85− 3.61− 2.6− 3.46− 3.29− 2.43− 3.19− 2.53− 3.21− 3.13− 3.11− 3.93− 2.68

aspU
aspV
lysT, valT, lysW
serW
serX
C0362
rydB/tpe7/IS082
asnT
asnW
asnU
asnV
proL
alaX, alaW
valU, valX, valY, lysV
tke1/sroF
ssrA
metZ, metW, metV
pheV
ileX
metY
thrV, rrfF, rrfD
proK
argX, hisR, leuT, proM
sraJ/ryiA/k19
rrfA
rrfB
rrfE
pheU
leuX

Previously reported sRNAs, according to GenBank annotations and Hershberg
et al. (9), are listed along with their promoter score, terminator score and Z scoreof GMMI, together with the E. coli genome annotations. The promoter score
represents the scores from pftool2.2 and the terminator score represents thescores from RNAMotif (for further details, see the Materials and methods
section). Five sRNAs (NC043, NC052, NC082, NC084 and NC117) are
documented in Hershberg et al. and the others are tRNAs and rRNAs
documented in GenBank annotations. Separations made using a comma indicate
correspondence with multiple sRNAs, whereas separations made using a slash(or solidus) indicate synonymous sRNAs.

visualized and analyzed with a Molecular Imager FX Pro (BioRad Laboratories, Hercules, CA, USA).
2.8. Oligonucleotides
The oligonucleotides for RT-PCR analysis and Northern
analysis were designated using Primer3, which is implemented
on the E. coli genome database coliBase (http://colibase.bham.
ac.uk). The list of oligonucleotides is provided as Supplementary material.
3. Results
3.1. Analysis of previously annotated ncRNA sequences using
the GMMI
3.1.1. Test the GMMI using tRNAs and rRNAs
The GMMI performance test was provided using a set of
known ncRNA sequences referred to as the ‘training set’,177

together with an independent set of RNAs designated the ‘test
set’. We could then investigate whether the GMMI correctly
identified ncRNAs that were absent from the training set. Twosets of well-known ncRNAs, tRNAs and rRNAs (Fig. 2A andB) were used to train our model. Due to the limited number ofsequences, the following procedure was adopted. Initially, one
sequence was removed from the set and the remainders wereused to train the model. The excluded sequence was then used totest the model. The procedure was repeated to evaluate eachsequence in the set. The Markov-order (e) of 2 and the gap spaceparameter (g) of 10 were used. Accordingly, the majority of thesequences in the ‘training set’ category had a GMMI scoreabove 1, whereas those in the other categories had GMMI
scores below 1 (data not shown). The average Z score for tRNAwas 5.88 and the average Z score for the sequences in othercategories was – 0.12 when the tRNA category was used as thetraining set. In contrast, when the rRNA category was employed
as the training set, the average Z scores for rRNA and for theother categories were 5.17 and – 0.03, respectively. Thus wesuggest that GMMI was able to model sequence patterns
specific to tRNAs and rRNAs, and successfully separated
tRNAs and rRNAs from other RNA molecules based on GMMI
scores.
3.1.2. Parameter optimization for ncRNAs isolation by training
mRNAs
In order to predict all of the ncRNA families, we chosemRNAs, the negative controls of ncRNA, as the ‘training set’ in
the GMMI calculations. Because very small number of
documented sRNAs (small RNAs) might not have any common
features for the efficient training and might contain possible
tiny-peptide-coding RNAs (4), we avoided training such classof RNAs. Intergenic regions were also avoided, because there
may be un-annotated coding regions.
We needed to define the threshold, which would allow the
GMMI to identify ncRNA sequences efficiently. The randomly
selected 10% of 4311 mRNA sequences were used as the‘training set’. The remaining 90% of the mRNA sequences, inaddition to the tRNA, rRNA and sRNA (ncRNA) sequenceswere used as the ‘test set’. To investigate the efficiency of ourGapped Markov Model and to estimate the optimal gap spaceparameter (g), we measured how known ncRNAs could be
isolated from mRNAs depending on the parameter of g by thebest correlation coefficient (BCC), which is defined as max CC(t), where CC(t) is correlation coefficient (Burset and Guigo,
1996) calculated according to the numbers of modeled/nonmodeled RNAs having GMMI score above/below thethreshold t (Fig. 2C). The parameters of 0 to 10 were tested
according to the order of sequence length of documented
ncRNAs (∼500). The separative power of gapped model (g N 0)
was substantially higher rather than the non-gapped model(g = 0). For the separation of mRNAs and the others, e = 2 andg = 10 were adopted in the GMMI calculation (Fig. 2D). While
97% of the mRNAs had GMMI scores above 1, 97% of thencRNAs had GMMI scores below 1 (data not shown). The
Z score average of the mRNAs was 0.11, and that of thencRNAs was – 2.94. Therefore, we clearly separated scores for178

N. Yachie et al. / Gene 372 (2006) 171–181

previously annotated ncRNA sequences from the total set of
RNA sequences. We then calculated the correlation coefficients, to determine the threshold value for the identificationof ncRNA sequences. The BCC for the quality measure of theisolation was 0.82 and the corresponding threshold score was− 2.34; achieving 82.0% sensitivity, 83.5% selectivity and99.5% specificity.

any ORFs (as indicated in GenBank annotations) on the same
DNA strand, we finally identified 133 novel ncRNA candidates, denoted NC001 to NC133 (step 4). Among these 133
candidates, 87 were encoded by regions that were bordered by
two genes (intergenic regions; Table 1), 46 were on thecomplementary strand of an annotated ORF (cis-antisense;
Table 2). They included 29 previously annotated ncRNAs(Table 3).

3.2. Extraction of novel candidate ncRNAs
3.3. Experimental validations of ncRNA candidates
Novel ncRNA candidates were predicted using the following strategy (Fig. 3). We predicted an initial total of 14,068
sigma70 promoters and 6606 rho-independent terminators (step1). As the length of previously identified sRNAs transcription
units ranged from 70 to 500 nt, we chose 4579 of the putativetranscription units which agreed with these sizes (step 2). We
trained the GMMI using 4311 ORF sequences and extracted
433 transcriptional units with a Z score of − 2.34 or less as
initial candidates (step 3). This set of initial candidates contained redundant fragments that overlapped on the same DNA
strand. Therefore, 184 sequences were extracted by sorting
overlapping candidates as follows. We initially selected theinnermost DNA region between the sigma70 promoter and therho-independent terminator, restricting the size to above 70 nt,
then we chose the best scores according to the GMMI, pftool2.2
and RNAMotif. After deselecting candidates that overlapped

RNA expression analysis using RT-PCR against total RNA
from the E. coli late log phase was initially performed to detectexpression of corresponding genomic region of our novel
ncRNA candidates. This detected nine major PCR products(from ncRNA candidates NC008, NC020, NC028, NC030,
NC051, NC079, NC086, NC109 and NC116) in an RTdependent manner (Fig. 4). These PCR products matchedeach predicted sizes according to the locations of the primer
pairs. We also confirmed the nucleotide sequences of the
products after subcloning. Two PCR products generated fromprimers directed to candidates, NC011 and NC065 had someunspecific size amplicons, however, the remaining amplicons
were the predicted size and their nucleotide sequences provided
further confirmation that they matched the candidate ncRNAs.
We also detected the documented sRNA dsrA (Sledjeski and

Fig. 4. RT-PCR analysis of the putative ncRNAs in log phase. PCR products were separated using 3% NuSieve agarose-gel electrophoresis and stained with
EtBr. The arrows indicate the predicted size according to the designated primers. The lane 1 contains the product of the PCR reaction (RT+). The lane 2
represents the product of the same reaction without reverse transcriptase (RT−, negative control). DsrA is a documented sRNA for the positive control. The size(bp) of the φX174/Hinf I marker (STRATAGENE, USA) is shown on the right. The same results were obtained in at least two independent experiments foreach gene.

N. Yachie et al. / Gene 372 (2006) 171–181

Gottesman, 1995). These results show that genomic region ofsome candidate ncRNAs are actually transcribed in E. coli,
although they are previously unknown. Notably, NC008,
NC028, NC086 and NC109 were located on the complementarystrands of annotated ORFs. This suggests that these RNAs
might regulate their neighbour mRNAs through possible
antisense-mediated mechanism.
A signal detected by RT-PCR confirms that the target regionis transcribed but does not reflect the size of the transcript.
Therefore we also performed Northern hybridization analyses
with strand specific probes from the selected ncRNAs
candidates against total RNA from the E. coli late log andearly stationary phases. The expressions of three novel smalltranscripts, NC051, NC065 and NC092, were detected (Fig. 5).
NC092 was differentially expressed in log and stationary phase,
which is in agreement with previous sRNAs in E. coli
(Argaman et al., 2001; Wassarman et al., 2001; Vogel et al.,2003). The documented sRNA IS061 (Chen et al., 2002) was
also detected (data not shown). The sizes of three transcripts
were larger than our predictions. Because we selected
innermost DNA region between the sigma70 promoter andthe rho-independent terminator as candidates, it is likely to betranscribed from different combination of promoter and terminator. The predicted transcription unit of NC092 transcript
overlaps with carboxy-terminal of neighbouring pgk mRNA on
the opposite strand by 13 nt suggesting that NC092 may act as
small RNA regulator by base pairing with pgk mRNA.
We could not get any positive signals for some transcriptsthat have not previously been identified by the Northern
analysis (data not shown). Although the absence of detection
might be caused by their unstabilities, low abundances and/or
certain culture/growth condition, total of 11 transcripts, including four antisense RNAs, were detected by RT-PCR under the

Fig. 5. Northern blot analysis of new small RNAs in E. coli. Total RNAs wereprepared from either stationary phase (lane 1) or log phase (lane 2). Membranes
were hybridized with gene specific oligonucleotides for NC051, MC065 andNC092. Numbers at left indicate length of marker RNA (BioDynamics, Japan)
in base. The same results were obtained in at least two independent experiments
for each gene.

179

single set of culture/growth condition. The expression of othertranscripts may be detectable in further studies using different
experimental conditions.
4. Discussion
This report described a novel index (GMMI), which
evaluates the specificity of sequence pattern with the combinations of patterns located on separate positions of consensus
sequences, codon biases and/or consisting possible structural
motifs, based on the Gapped Markov Model. Training setscomprising tRNAs and rRNAs were used to test the GMMI
performance, and successfully separated the sequences in the
training set from other categories of RNA, as expected. Thismight have been due to strong biases in the training set resulting
from the domain sequences and/or the high stabilities of thesestructures (for example, the cloverleaf structure of tRNA). The
successful isolation of ncRNAs from a total set of RNAs implies
that the GMMI is useful for the detection of novel ncRNAs.
Despite the fact that the scores for the majority of the sRNAs fellwithin the range of most ncRNAs, some of sRNAs were
distributed within the range of the mRNAs. We therefore
suspect that the sRNA category might include small peptidecoding RNAs. Most of these sRNAs have not been proved tofunction as ncRNAs in previous computational or experimental
studies.
Twenty-nine previously annotated sRNA genes were included among our candidates. In previous studies, tRNAs (Lowe andEddy, 1997; Rivas and Eddy, 2001; Sakakibara, 2003),
microRNAs (miRNAs) (Grad et al., 2003; Lai et al., 2003;
Lim et al., 2003a, b), small nucleolar RNAs (snoRNAs) (Lianget al., 2002; Chen et al., 2003) and the other characteristic
ncRNA families have been modeled using complicatedmethods, such as stochastic context-free grammars, hidden
Markov models, and/or comparative genomics with calculationof the structural formation and its free energy. However, ourpresent results imply that a simpler approach based on theMarkov model is useful in isolating the ncRNA cluster, whichconstitutes a superfamily of functional RNA molecules.
Although most of the 29 candidates matched transcribed regionsof tRNAs and rRNAs, our set also included five sRNA-encoding
genes recently identified: rydB/tpe7/IS082, ssrA, sraJ/ryiA/k19,
C0362 and tke1/sroF. High expression of RydB causes
decreased expression of the sigma38 factor RpoS (Wassarman
et al., 2001). SsrA releases a stalled ribosome from the end of‘broken’ mRNA, which lacks a stop codon, by acting both as atRNA and an mRNA via a ‘trans-translation’ mechanism(transfer-messenger RNA; tmRNA) (Muto et al., 1996). SraJ
is subject to RNase III processing (Argaman et al., 2001) and aninteraction with the Hfq protein has been reported (Wassarman etal., 2001). The functions of the remaining two genes, C0362 andtke1, are not known.
According to our results, 46 (approximately 40%) of ourcandidates were encoded on the complementary DNA strandsof annotated ORFs (cis-antisense). Antisense RNAs are
diffusible regulatory RNAs that bind to the complementary
sequences of mRNAs in order to control their biological

180

N. Yachie et al. / Gene 372 (2006) 171–181

function at the post-transcriptional level. Our experimental
results showed that four of novel antisense transcripts weredetected using RT-PCR. Although these bands may not correspond to the predicted size of the transcription units, expressions
and their sizes of two transcripts including the one detected in
RT-PCR analyses are confirmed by Northern hybridization.
Computational methods have not yet been established to
predict cis-encoded saRNAs from the bacterial genome sequence. Because most of the genome-based methods previously
used to predict ncRNAs or sRNAs have been applied only inintergenic regions, antisense transcripts overlapping withneighbour ORFs in the complementary strands could not be
detected. Although several saRNAs transcribed from different
loci in the target mRNAs (trans-antisense) have been describedin the E. coli genome (Carpousis, 2003), few small cis-encoded
saRNAs have been documented (Kawano et al., 2002; Vogel etal., 2003; Kawano et al., 2005). For example, the LdrD peptide
and the RdlD antisense regulatory RNA act as a toxin–antitoxin
pair (Kawano et al., 2002). In addition, the interaction between
two newly identified sRNAs, RyeB (Wassarman et al., 2001)
and SraC/RyeA (Argaman et al., 2001; Wassarman et al.,2001), has been reported to result in RNase-III-dependentcleavage (Vogel et al., 2003). However, the existence of large
amounts of such transcripts has been predicted using the recent
shotgun-cloning approach (experimental RNomics), whichsuggested that 5% of all sRNAs are cis-antisense (Vogel etal., 2003). Likewise, Carpusis pointed out that cis-encoded
saRNAs with extensive complementarity to coding regions
would have been overlooked using previous computational
methods (Carpousis, 2003). Among eukaryotes, many chromosomal cis-antisense transcripts are involved in aspects ofgene-expression regulation, such as genomic imprinting (Reikand Walter, 2001) and circadian clocks (Crosthwaite, 2004).
However, in bacterial species, cis-antisense transcripts are
principally known in extrachromosomal elements (for example,
transposons, plasmids and bacteriophages). An abundance ofchromosomal small cis-antisense RNAs and their regulating
mechanisms in E. coli or other bacterial species might be
expected on the basis of our results. The products of mRNAs
encoded on the opposite strand of our 46 antisense candidates
included two ATP-binding proteins and eight metabolic
enzymes (Supplementary Table 3). It is possible that some ofthese antisense candidates can regulate metabolic pathways at
the post-transcriptional level.
NC092, newly detected by Northern hybridization, overlaps
with 3′ end of pgk in opposite strand. Previously identified
sRNA GadY regulates the expression of GadX mRNA, and thepossible regulation mechanism via base pairing between 3′
sequences of GadY and GadX has been suggested (Opdyke etal., 2004). We hereto suggest that NC092 acts as small RNA
regulator by base paring with 3′ sequence of mRNAs encoded
in opposite strand. Since two RNAs are encoded on same lociand overlapped, any mutations arising in the double strand
DNA may not affect their regulatory mechanisms.
In summary, here we present the novel ncRNA candidates inE. coli, isolated by the prediction of transcription units followedby the GMMI-based evaluation and the prediction of these units.

Although the experimental validation of the candidates is needed
more widely, our list will be informative for the further RNomics
approaches such as tilling array. Five cis-antisense smalltranscripts (one is detected by Northern hybridization) wereverified under the certain culture/growth conditions. We showedthat our computational approach may enable the discovery ofnovel sRNAs, including cis-antisense RNAs, in the genomes ofbacteria without requiring comparisons with other species orother experimental resources. Finally we suggest that the cisantisense RNAs might be more common than previously
thought, some of which may have crucial roles such as RNA
metabolisms within E. coli and other bacterial species.
Acknowledgements
We are grateful to Satoshi Harada, Asako Sato and
Dr. Yoko Fukuda for excellent experimental support, Yuka
Watanabe for assistance and performing computational tests of
the GMMI, and Dr. Takeshi Ara and Dr. Tomoya Baba for
helpful advice. The authors also thank Dr. Elena Lesnik forproviding the descriptor file for the rho-independent terminator prediction.
Appendix A. Supplementary data
Supplementary data associated with this article can be found,in the online version, at doi:10.1016/j.gene.2005.12.034.
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