Vol.2, No.6, 412-418 (2009)
doi:10.4236/jbise.2009.26059
SciRes Copyright © 2009 Openly accessible at http://www.scirp.org/journal/JBISE/
JBiSE
A MCMC strategy for group-specific 16S rRNA probe
design
Yi-Bo Wu1*, Li-Rong Yan2*, Hui Liu1, Han-Chang Sun1, Hong-Wei Xie
1Department of Automatic Control, College of Mechatronics and Automation, National University of Defense Technology, Changsha ,
Hunan, China; 2Department of Information, Wuhan General Hospital of Guangzhou Command, Wuhan, Hubei, China; *These au-
thors contributed equally to this work; §Corresponding author.
Email: wybbok@hotmail.com
Received 17 April 2008; revised 6 July 2009; accepted 17 July 2009.
ABSTRACT
Revealing biodiversity in microbial communities
is essential in metagenomics researches. With
thousands of sequenced 16S rRNA gene avail-
able, and advancements in oligonucleotide mi-
croarray technology, the detection of microor-
ganisms in microbial communities consisting of
hundreds of species may be possible. Many of
the existing strategies developed for oligonu-
cleotide probe design are dependent on the re-
sult of global multiple sequences alignment,
which is a time-consuming task. We present a
novel program named OligoSampling that uses
MCMC method to design group-specific oli-
gonucleotide probes. The probes generated by
OligoSampling are group specific with weak
cross-hybridization potentials. Furthermore a
high coverage of target sequences can be ob-
tained. Our method does not need to globally
align target sequences. Locally aligning target
sequences iteratively based on a Gibbs sam-
pling strategy has the same effect as globally
aligning sequences in the process of seeking
group-specific probes. OligoSampling provides
more flexibility and speed than other software
programs based on global multiple sequences
alignment.
Keywords: 16S Rrna; Probe Desig n; MCMC
1. INTRODUCTION
Metagenomics is a new field combining molecular biol-
ogy and genetics in an attempt to reveal the vast scop e of
biodiversity in a wide range of environment, as well as
new functional capacities of individual cells and com-
munities, and the complex evolutionary relationships
between them [1,2,3,4]. Apparently, revealing biodiver-
sity in microbial communities is the first step [1,5]. The
vast majority of microbial diversity had been missed by
cultivation-based methods [2]. The analysis of 16S
rRNA gene sequences is the most common approach to
determine microbial diversity [6].
Oligonucleotide microarrays now afford an idea tool
for identifying sequence variants (even single-base-pair
var ian t) in 1 6S rRNA gene copies of diverse micro o rgan-
isms simultaneously in a single assay [7,8,9,10]. Many
16S rRNA-based oligonucleotide microarrays have been
designed to detect multiple pathogens simultaneously [11,
12,13,14,15]. Such technology has the potential to revolu-
tio n iz e cl inical diagnostics [15,16,17,18,19,20,21,22,23].
A critical issue for oligonucleotide microarray design
is to find appropriate oligonucleotide probes specific to
their target sequences. To improve efficiency in probe
design, many softwares or databases have been devel-
oped. Kaderali et al. proposed a combination of suffix
trees and dynamic programming based alignment algo-
rithms to compute melting temperature (T), and pre-
sented an efficient algorithm to select probes with high
specificity in detecting the target [24]. Loy et al. built a
comprehensive database containing more than 700 pub-
lished rRNA-targeted oligonucleotide probe sequences
with supporting bibliographic and biological annotation
[25]. Kumar et al. provided a software package ARB to
evaluate sequence alignments and oligonucleotide
probes with respect to three-dimensional structure of
ribosomal RNA [26]. DeSantis et al. proposed an align-
ment compression algorithm, NAST (Nearest Alignment
Space Termination), to find Operational Taxonomic
Units (OTUs) for automated design of effective probes
[27,28].
m
Global multiple sequences alignment plays an im-
portant role in comprehensive analysis of group-spe-
cific oligonucleotide probe for those methods men-
tioned above. It is a challenge for personal computer
to align a large amount of sequences. Here we present
a novel program named OligoSampling that uses
MCMC method to design group-specific oligonucleo-
Y. B. Wu et al. / J. Biomedical Science and Engineering 2 (2009) 412-418
SciRes Copyright © 2009 Openly accessible at http://www.scirp.org/journal/JBISE/
413
tide probes. OligoSampling does not need to globally
align target sequences. This MCMC method is more
flexible and efficient.
2. RESULTS
2.1. Algorithm Overview
Figure 1 summarizes the global overview of the Oligo-
Sampling algorithm. We assume that we are given a set
of N sequences
1,,
N
SSS and that those N se-
quences are divided into m group. We seek a group-spe-
cific oligonucleotide probe for each group. For a par-
ticular probe of specified width W segments in probe
binding sites within each sequence are mutually similar.
Conversely we seek within each sequence mutually
similar segments. The segments can be regarded as
binding sites for a particular probe. If the probe is spe-
cific to group A, segments within sequences in group A
are more mutually similar. And to avoid cross-hybridi-
zation, there need to be more sequence variants between
segments within sequences of group A and segments
within sequences in any other group. In the process
seeking mutually similar segments, patterns shared by
multiple sequences in each group are obtained.
The algorithm maintains an evolving data structures.
The data structure is a set of positions , for k from 1
to N. For a particular set of positions we obtain the
pattern descriptions of mutually similar segments in each
k
a
k
a
group i from 1 to m, in the form of a probabilistic model
of base pair frenquencies for each position j from 1 to W,
and consisting of the variables .
,,1 ,,4
,,
ij ij
qq
Figure 1. Algorithm overview: The algorithm is initialized by choosing starting positions , for k from 1 to N, within the various
sequences. A Gibbs sampling-based local multiple alignment algorithm [29] is applied to update . After an identical jump for each
position , the Gibbs sampling-based local multiple alignment algorithm [29] is applied again to update . An objective function
defined to evaluate sensitivity and specificity of probes is calculated based on positions before jump and after alignment respec-
tively. The ratio of objective values is compared to a random number
k
a
k
k
a
k
ak
a
a
uniformly distributed in [0, 1]. The result of comparison
determine whether the update of are rejected or not. Then go back to step 1 and start a new iteration loop.
k
a
Y. B. Wu et al. / J. Biomedical Science and Engineering 2 (2009) 412-418
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Y. B. Wu et al. / J. Biomedical Science and Engineering 2 (2009) 412-418
SciRes Copyright © 2009 http://www.scirp.org/journal/JBISE/
415
In the process of seeking group-specific probe for
group i the algorithm is initialized by choosing starting
positions
0
k
a within the various sequences. A Gibbs
sampling-based local multiple alignment algorithm [29]
is applied to update
0
k
a to
1
k
a. After alignment the
algorithm then proceeds through many iterations to exe-
cute the following three steps:
Openly accessible at
1) Jump step. Draw a sample d from a normal distri-
bution. Positions
1
k
a are updated to
21
kk
aad.
2) Local multiple alignment step. Gibbs sampling-
based local multiple alignment algorithm is applied re-
garding po sitions
2
k
a as initial s tarting point. Positio ns
2
k
a are updated to
3
k
a.
Rejection step. We define an objective function (Eq.1).
Based on positions
1
k
a and
3
k
a, the objective func-
tion
previous
F
i and
tnex
i are calculated respective-
ly. We generate a random number
uniformly distrib-
uted in [0, 1]. If

next previous
F
iF i
, we accept
3
k
a.
Otherwise, positions resume to
1
k
a. Then, go back to
step 1.


44
,,
,, ,,,,
1
1111 ,,
log minlog
WW
jk
j ijkijkjjk
m
jkjk ijk
i
q
Fiq qqq


 



(1)
In the objective function sensitivity of the probe is
measured by entropy, and specificity is measured by
Kullback-Leibler divergence. By selecting a set of
that maximizes t he objecti ve funct i on, t he algori t hm fi nds
the most group-specific oli gonucleoti de probe for group i.
k
a
2.2. Design Group-Specific Oligonucleotide
Probes for 5 Bacterial Species
To examine the performance of OligoSampling on
group-specific probe design, we select 5 bacteria species
(Afipia sp., Bordetella pertussis, Brucella sp., Es-
cherichia coli O157:H7, and Mycobacterium tuberculo-
sis) with a total of 40 sequences. For OligoSampling, the
input dataset is 5 clusters of homolog ous sequences. The
objective of this algorithm is to find a group-specific
oligonucleotide probe for each cluster. As shown in Fig-
ure 2, the probes generated by OligoSamplin g are group
specific with weak cross-hybridization potentials. Oligo-
Sampling also obtains a high c overage of ta r get sequences.
3. DISCUSSION
The secondary structure model of 16S rRNA consists of
a conserved core that is interspersed with a number of
variable areas. 16S rRNA evolves slowly and is often not
very convenient to resolve bacterial strains at the species
level. Therefore, multiple copies of 16S rRNA gene in
different species share high sequence similarity. In jump
step each aligned position is updated by jumping a
same distance in the same direction. Convergence will
be achieved rapidly in the following alignment due to
high sequence similarity between sequences (Figure 3).
In the process of updating positions , Gibbs sam-
pling-based local multiple alignment algorithm is ap-
plied to keep sequences locally aligned. And then, sensi-
tivity and specificity of probe is evaluated based on en-
tropy and Kullback-Leibler divergence. This process is
functionally equivalent to oligonucleotide probe design
based on global multiple sequences alignment, but has
less calculation amount.
k
a
k
a
Through the iterative algorithm mentio ned above, dif-
ferent initializations sometimes lead to different local
optimal solutions. Therefore, searches beginning with
multiple initializations have more possibility to achieve
global optimal solution. Searches beginning with differ-
ent initializations are mutually independent and can be
executed in parallel. It is convenient to parallelize this
algorithm.
Gibbs sampling-based local multiple alignment algo-
rithm is sensitive to starting positions k. Appropriate
starting positions can quicken convergence speed.
a
k
a
Figure 3. Convergence of local alignment: Optimal ob-
jective values converge in 10000 iterations under three
conditions: after jump, beginning with starting positions
proportional to sequence length, and beginning with
random starting positions. To make the comparison of
convergence speed independent of initialization, ten
alignment procedures beginning with different sets of
starting positions are executed under each condition. Av-
erage optimal objective values in iterations 1 to 10000
were normalized to that in last iteration.
Y. B. Wu et al. / J. Biomedical Science and Engineering 2 (2009) 412-418
SciRes Copyright © 2009 Openly accessible at http://www.scirp.org/journal/JBISE/
416
Sets of target sequences are highly similar to each other.
We choose initial starting positions by letting the dis-
tance from 5’ termini to initial starting position in each
target sequence be proportional to sequence length. As
shown in Figure 3, Local alignment beginning with
proportional starting positions can achieve faster con-
vergence speed than that beginning with random starting
positions. In this method, the algorithm is initialized by
choosing a random proportion. And the proportion is
used to find starting positio n in each target sequence.
It is noted that if a probe can match well with several
segments in a 16S rRNA, local multiple alignment can
not analyze this situation comprehensively. We have
analyzed the possibility that a probe matches well with
several segments in a 16S rRNA. At present, the Ribo-
somal Database Project [30] (RD P) has collected around
280,952 16S rRNA sequences in bacteria superkingdom.
Among the 280,952 16S rRNA sequences we picked out
a 16S rRNA sequence and chose a segment in this 16S
rRNA randomly. And then in this 16S rRNA sequence
another segment which is most similar to the original
segment was found. Mismatches of the two segments
were counted. The procedure mentioned above was iter-
ated 10,000 times. As shown in Figure 4, there is little
possibility that a probe can match well with several
segments in a 16S rRNA.
The problem of group-specific probe selection is fur-
ther complicated as all probes must work under the same
hybridization condition. To ensure the closer optimum
Figure 4. Mismatch in similar segments: Among 280,952
16S rRNA sequences in bacteria superkingdom collected
by RDP, we picked out a 16S rRNA sequence and chose
a segment in this 16S rRNA randomly. And then in this
16S rRNA sequence another segment which is most
similar to the original segment was found. Mismatches of
the two segments were counted. The procedure men-
tioned above was iterated 100,000 times.
hybridization condition in one chip including all oligos,
probe lengths must be variable. By adjusting probe
lengths (increase or decrease base-pairs in 5’ and 3’
ends), probe whose melting temperatures () are closer
to a predefined optimum hybridization condition will be
generated. As shown in Table 1, probe lengths are se-
lected based on a predefined melting temperature. Melt-
ing temperatures of all probes can be close enough to
work well under the same hybridization conditio n.
m
T
4. CONCLUSIONS
OligoSampling provides an efficient alternative for
group-specific oligonucleotide probe design. Using this
method we do not need to globally align target se-
quences. Locally aligning target sequences iteratively
based on a Gibbs sampling strategy has the same effect
as globally aligning sequences in the process of seeking
group-specific probes. OligoSampling provides more
flexibility and speed than o ther software programs based
on global multiple sequences alignment. Furthermore,
search for multiple local optimal solutions beginning
with multiple initializations can improve the effect of
this algorithm in group-specific prob e design. This algo-
rithm can be parallelized conveniently.
5. METHODS
5.1. A Gibbs Sampling Strategy for Local
Multiple Alignment
Lawrence et al. have described a Gibbs sampling algo-
rithm for local multiple alignment of protein sequences
[29]. We applied Lawrence’s method to locally align
target sequences.
Pattern shared by multiple sequences is described in
the form of a probabilistic model of base pair frequen-
cies for each position i from 1 to W, and consisting of
the variables . This pattern description is
accompanied by an analogous probabilistic description
of the “background frequencies” with which
base pairs occur in sites not described by the pattern.
,1 ,4
,,
i
qqi
4
p
1
,,p
Through many iterations to execute two steps of the
Gibbs sampler an objective function (Eq.2) evaluating
the alignment is maximized. In the study, the two steps
of the Gibbs sampler were executed to locally align tar-
get sequences.
4,
,
11
log
wij
ij
ij
j
q
Fc
p

 (2)
First step, one of the N sequences, z, is chosen in
specified order. The pattern description and back-
ground frequencies
,ij
q
j
p are calculated, as described in
Eq.3, from the current positions in all sequences
excluding z. k
a
Y. B. Wu et al. / J. Biomedical Science and Engineering 2 (2009) 412-418
SciRes Copyright © 2009 http://www.scirp.org/journal/JBISE/
417
Table 1. Adjust prob e leng t h s a c c o r d i n g to a predefined optimum hybridization condition (70).
Original probe m
T Adjusted probe m
T
3’-GTTGGGGCCTTGACGGAAAC-5’ 74.98 3’-GGGGCCTTGACGGA-5’ 71.07
3’-AACCCCGGAAGCCCGGAACC-5’ 80.38 3’-CCGGAAGCCCGGA-5’ 70.93
3’-CTTGTTGTGTCCCTTTGAAC-5’ 66.40 3’-CCTTGTTGTGTCCCTTTGAAC-5’ 70.05
3’-CCGAAGGCCTCGATTGCGCA-5’ 78.59 3’-GAAGGCCTCGATTGCGC-5’ 70.69
3’-ACAACCACCCCACTGCCGGA-5’ 82.37 3’-ACCACCCCACTGCCG-5’ 71.66
,
,1
ij j
ij
cb
qNB
 (3)
For the ith position of the pattern we have 1N
ob-
served nucleotides, because sequence z has been ex-
cluded; let be the coun t of nucleotide j in this posi-
tion. These are supplemented with nucleotide-dep-
endent “pseudocounts” to yield pattern probabilities.
B is the sum of the
,ij
c
,ij
c
j
b
j
b.
Openly accessible at
Second step, the probability
x
Q of generating seg-
ment x in position
z
a according to the current pattern
description are calculated, as are the probability
,ij
q
x
P of generating this segment by the background prob-
abilities . The weight
j
p
x
xx
A
QP is assigned to
current segment x. And then draw a sample d from a
normal distribution. Positions
z
a are updated to
. In the updated position weight
z
ad
x
A
is calculated
in the same way. We generate a random number
uniformly distributed in [0, 1]. If
x
x
A
A
, we accept
the update of positions
z
a. Otherwise, we reject the
update. Then go back to first step.
5.2. Selection of Weight Set in Sensitivity
and Specificity Evaluation
Pozhitkov et al. examined the effects of single-base pair
mismatch (all possible types and positions) on signal
intensities of hybridization through a series of calibra-
tion experiments [31]. The results of experiments indi-
cated that the most optimal discrimination of mismatch
from perfect match probe-target duplexes is provided
with the mismatch in the middle of the duplex. To sup-
press non-specific binding of probe to target, the posi-
tion of the mismatch should be moved from the 5’ or 3’
termini to the center of the probe. Therefore, in evalua-
tion of sensitivity and specificity of group-specific
oligonucleotide probes the weight coefficients
j
(in
Eq.1) should dependent on the signal intensity values of
mismatches in different positions. Based on normalized
signal intensity values of mismatch duplexes at position
1 to 20 provided by Pozhitkov et al., the weight coeffi-
cients
j
are assigned. Position in the middle has the
highest weight.
3. ACKNOWLEDGEMENTS
This work was supported by a grant from the National Natural Science
Foundation of China (No. 30800253). The authors thank anonymous
referees and the associate editor for many helpful comments.
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