1. African Journal of Science and Research,2014,(3)5:08-12
ISSN: 2306-5877
Available Online: http://ajsr.rstpublishers.com/
PROBES SPECIFICITY IN ARRAY DESIGN INFLUENCES THE AGREEMENT
BETWEEN MICROARRAY AND RNA-Seq IN GENE EXPRESSION ANALYSIS.
Dago Dougba Noel 1,3*, Alberto Ferrarini 3, Diarassouba Nafan 1, Fofana Inza Jésus 1, Silué Souleymane 1,
Giovanni Malerba 2 andMassimo Delledonne 3
1)UPGC University of Korhogo Cote d’Ivoire Unité Formation Recherche Sciences Biologiques, Côte d'Ivoire.
2 )Department of Life and Reproduction sciences, Section of Biology and Genetics, University of Verona, Italy.
3) Department of Biotechnology, University of Verona, Italy Strada le Grazie 15, Cà vignal 1,Italy.
Email:dgnoel7@gmail.com
Received:04, Aug,2014 Accepted: 19 ,Sep,2014
Abstract
More than a decade, oligonucleotide microarrays have been the method of choice for transcriptional profiling studies, used to characterize
biological systems. The power of microarray platforms depends on the number, identity and specificity of the oligonucleotide probes for their
target gene models. In recent time, however, researchers have been increasingly focusing high-throughput sequencing RNA-Seq which offers
advantages when examining transcriptome fine structure; for example, in the detection of allele-specific expression and splice junctions. We are
investigating how the specificity of oligonucleotide probes in an array design influences the agreement between RNA-Seq and microarray
platforms in gene expression, differential analysis. Hence, we are essaying the agreement between a custom microarray platform, based on
multiple long oligonucleotide probes (60 mer) per gene model transcript (Grape custom microarray platform), and RNA-Seq, by removing
microarray, design less specific probes discriminating differential expressed genes (DEGs), analyzing two Vitis vinifera berry developmental
stages. We were able to demonstrate that the agreement between both RNA-Seq and microarray platforms calling DEGs, depend on the high
rate of probes set with specific oligonucleotide probes. Furthermore, this investigation confirmed the superiority of RNA-Seq next generation
sequencing (NGS) technology, as opposed to microarray in gene expression differential analysis.
Keywords: Microarray, Probe set, oligonucleotide probe, RNA-Seq.
INTRODUCTION
Microarray technology has revolutionized molecular biology. It
uses hundreds of millions of highly organized probes on a limited solid
surface to simultaneously interrogate the multiple RNA or DNA
molecules, defined as target within an individual sample [1]. The
microarray technologies are universal tools that can be applied
throughout the life sciences [2-4]. mRNA-expression profiling is the
most frequent application. The principle behind of a microarray
experiment, is that mRNAs from a given cell line or tissue is labeled
sample, and hybridized in parallel to a large number of DNA
sequences, it is then immobilized on a solid surface in an ordered
array. The number of immobilized probes can vary from hundreds
designed to monitor the expression of few specific genes to hundreds
of thousands representing complete transcriptomes. Microarray
expression technology is strongly influenced by the number, identity
and quality (specificity) of the oligonucleotide probes on the array.
Tracing and maintaining the identity of the thousands of probes
requires an informatic system throughout the fabrication process.
Gene-specific oligonucleotide probes are currently used in microarrays
to avoid cross-hybridization of highly similar sequences. If the probes
are not optimized for sequence specificity, microarrays can generate
false-positive data due to non-specific crosshybridization to highly
similar sequences, gene families [5], or alternatively spliced variants
[6]. Long oligonucleotide probes are prone to cross-hybridization and
thus often exhibit poor discrimination and hybridize to similar
sequences. Studies have suggested that the percentage sequence
homology is a reasonable predictor of cross-hybridization [7] and to
overcome this crosshybridization problem, several laboratories have
adopted the practice of designing oligonucleotide probes that target
regions of low sequence similarity [8]. The literature data [9] indicate
that longer oligonucleotides provide significantly better detection
sensitivity than shorter probes. Single or multiple probes per genes can
be designed. Cheng-Chung Chou et al. [10] demonstrated that a single
longer oligonucleotide probe for a gene model could be sufficient for
accurate expression measurement if the probe is validated
experimentally. However, oligonucleotide probes binding to different
regions of a gene yield different signal intensities [1], and it is difficult to
predict whether an oligonucleotide probe will bind efficiently to its
target sequence and yield a good hybridization signal on the basis of
sequence information alone [11].
As a result, multiple probes per gene model transcript have been
used in oligonucleotide array designs to obtain reliable quantitative
information of gene expression. Furthermore it has been shown that
the measurement bias decreased with an increase in the number of
probes per gene. Fewer probes per gene were required for the longer
probes to achieve the same bias reduction as shorter probes. As
reported by Dago N., 2012 work [12], Grape custom microarray
designs based on multiple longer probes (60 mer) per gene model
transcript exhibited a higher sensitivity than their corresponding
microarray designs based on shorter multiple probes (35-40 mer) per
gene model transcript in detecting DEGs. The same investigation and
unpublished data form the Functional Genomic Center of the university
of Verona-Italy showed that as regards this microarray design, several
probe sets from 6538 genes out of 29582 demonstrated a different
behavior among themselves; a considerable number of these genes
(4146 in total) claimed to be DEGs. Thus, in order to determine the
specificity of the analyzed oligonucleotide probes, a blast analysis with
no mismatch by setting Tm ≥ 85±15°C threshold to predict
oligonucleotide probes secondary structure was performed against all
represented gene model transcripts of Vitis vinifera grape 12x
assembly transcriptome [13].
Next, we selected the specific probes (oligonucleotide probes
and/or probe set that recognized only their gene model transcript at a

2. African Journal of Science and Research , 2014,(3)5:08-12
Tm ≥ 85±15°C with no mismatch) from each analyzed probe set and
compared the gene expression data of their corresponding genes with
those of RNA-Seq. In recent times, RNA-Seq has emerged as a
powerful new technology for transcriptome analysis [14-15]. Although
RNA-Seq is still a technology under active development, it offers
several key advantages over microarray technology. First, unlike
hybridization-based approaches, RNA-Seq is not limited to detecting
transcripts that correspond to existing genomic sequence. This makes
RNA-Seq particularly attractive for non model organisms with genomic
sequences that are yet to be determined. RNA-Seq can reveal the
precise location of transcription boundaries, to a single-base
resolution. Furthermore, several studies showed that RNA-Seq can
accurately reveal gene expression difference [16]. These factors make
RNA-Seq useful for studying complex transcriptomes [17]. Hence, we
explored the number of specific and less specific oligonucleotide
probes (i) when both RNA-Seq and microarray platforms agree in
discriminating DEGs; (ii) when DEGs have been discriminated
exclusively by RNA-Seq, and/or (iii) by microarray.
MATERIALS AND METHODS
Samples from two development stages of Vitis vinifera grape
(veraison and ripening) have been profiled for global gene
expression using Grape custom microarray platform based on
multiple (4) longer oligonucleotide probes (60 mer) for the genes
essayed [12]. The same samples were previously profiled using NGS
technology (RNA-Seq) as reported in Zenoni et al., 2010 [18].
Microarrays expression data have been pre-processed by RMA
module [19] and analyzed by limma moderated statistical t-test [12].
Agreement assessment between microarray and RNA-Seq platforms
in gene expression differential analysis have been measured in term
of oligonucleotide probes specificity. To that effect, we employed a
thermodynamic approach based on OligoArray 2.0 software [20] to
select probes that exhibited a melting temperature (Tm) ≥85±15°C
as threshold to predict oligonucleotide probes secondary structure.
Next, the selected probes were processed for local alignment
analysis (megaBLAST http://www.ncbi.nlm.nih.gov/blast/Blast)
against Vitis vinifera grape 12x assembly transcriptome [13]. Then
oligonucleotide probes that recognized exclusively their gene model
transcript with no mismatch at Tm ≥ 85±15 have been classified as
specific.
RNA preparation
Sample of Vitis vinifera (grape) berry at the development stages
of veraison and ripening were collected [18] and total RNA has been
extracted as described in Anita Zamboni et al., 2010 work [21]. The
RNA amount and integrity were essayed using a Nanodrop 2000
instrument (Thermo Scientific) and a Bio-analyzer Chip RNA 6000
(Agilent), respectively.
Hybridization of Grape custom array based on long multiple
probes per gene
Grape custom array hybridization experiment based on One
Color-DNA labeling system with Cy3 fluorescent performed by
processing the same total RNA amount (10μg) of the three analyzed
technical replicates of veraison and ripening development stage [12].
RNA processing, labeling, hybridization and slide wash step were
accomplished following the NimbleGen Arrays User’s Guide Gene
Expression Analysis version 3.1 protocol manufacturer’s instructions.
Hybridization image scanning have been performed with the aid of
the axon scanner Instruments GenePix 4200A at 535 wave length.
For Grape custom array design based on multiple long
9
oligonucleotide probes (60 mer) per gene model transcript, the
summarization of the normalized intensities of the different probes
per transcript was performed according to the Robust Multichip
Average (RMA) algorithm from NimbleScan software [19].
RESULTS
Rate of specific probes for DEGs detected by both microarray
and RNA-Seq platforms
In total 117999 probes from 29582 gene model transcripts of
Grape custom microarray design were analyzed. 85.66% of them
have been selected as specific for their respective gene model
transcript (oligonucleotide probes that recognized exclusively their
gene model transcript at a Tm ≥85±15°C with no mismatch).
However, the survey of specific oligonucleotide probes of DEGs
detected by (i) both microarray and RNA-Seq platforms, (ii)
microarray platform exclusively and (iii) RNA-Seq platform
exclusively have been performed on 17850 genes (expressed genes
called by both Grape custom microarray and RNA-Seq) [12].
Considering microarray data for which, internal replicate probes have
been summarized by RMA algorithm as described by Irizarry et al.
2003 work [19], 6542 genes have been detected as DEGs by both
Grape custom array design and RNA-Seq platforms [12]. Focusing
on these DEGs, 57.56% of the analyzed probe set exhibited 3 or 4
specific oligonucleotide probes against 42.44% with 2, 1 or 0 specific
oligonucleotide probes (see Figure 1). This result suggests that the
agreement between both microarray and RNA-Seq tools in gene
expression differential analysis is carried by the high number of
probe set with specific oligonucleotide probes.
Figure 1 Percentage of probe set with their corresponding specific
oligonucleotide probes for DEGs discriminated by RNA-Seq
microarray and/or by both microarray and RNA-Seq platforms in
gene expression differential analysis.
Rate of specific probes for DEGs detected exclusively by
microarray and/or RNA-Seq
We next analyzed the percentage of specific oligonucleotide
probes in Grape custom microarray design focusing on DEGs called
exclusively by (i) microarray (3518 genes) and (ii) RNA-Seq (3053
gene) [12]. The results of this analysis have been summarized in
Figure 1 (see above). In fact, for DEGs discriminated exclusively by
RNA-Seq platform, 62.28% of the analyzed probe set exhibited 4 or 3
specific oligonucleotide probes against 37.72% with 2, 1 or 0 specific
oligonucleotide probes as showed in Figure 1 (see above). These
results suppose a large involvement of specific oligonucleotide
probes in the discrimination of DEGs previously recognized as such
by RNA-Seq tool exclusively. Further, for DEGs wholly discriminated
by Grape custom microarray design, 53% of the analyzed probe set
display 2, 1 or 0 specific oligonucleotide probes against 47% with 4
or 3 specific oligonucleotide probes. Hence, for DEGs called
exclusively by Grape custom microarray design, the number of

3. 10
specific oligonucleotide probes was comparable with those less
specific oligonucleotide probes, supporting an involvement of a
substantial number of less specific oligonucleotide probes in the
discrimination of DEGs previously recognized as such by Grape
custom microarray design exclusively. In view of the foregoing these
analysis suggest that disagreement between RNA-Seq and
microarray technologies in gene expression differential analysis
could be due to the considerable number of less specific
oligonucleotide probes in the Grape custom microarray design.
Moreover, these results suggest the high specificity and sensitivity of
RNA-Seq in detecting DEGs in gene expression differential analysis
[22].
Correlation between Grape custom microarray and RNA-Seq
Level of gene expression in microarray depend on both spot
intensity (transcript abundance) and oligonucleotide probes
specificity (cross-hybridization) in signal detection. Consequently,
oligonucleotide probes intensity in microarray experiment cannot
represent the absolute gene expression level and is not suitable for
analysis across different platforms. We then introduced a fold
change (FC) parameter. There, FC parameter has been calculated
as the ratio of the measured genes expression values of ripening
sample to the genes expression values of véraison sample. Pearson
correlation of the 17850 expressed genes between Grape custom
microarray design and RNA-Seq platforms in FC measurement have
been previously estimated at 0.72 (R2=0.72) [12]. The deletion of
less specific oligonucleotide probes out the set of analyzed probes of
Grape custom microarray design allows an increase in correlation
between both microarray and RNA-Seq platforms in FC
measurement analysis (from R2=0.72 to R2=0.90); this shows that
microarray designed oligonucleotide probes could be best selected
by the integration of both oligonucleotide probe design strategy
based on score and thermodynamic parameters. This analysis also
proves that a good selection of specific oligonucleotide probes in
Grape custom microarray design improves strongly its agreement
with RNA-Seq in gene expression differential analysis.
Assessment of microarray accuracy and specificity by using
specific probes in array design
To further explore the agreement between microarray and RNA-
Seq in gene expression differential analysis by selecting the Grape
custom microarray specific oligonucleotide probes, a Receiver
Operating Characteristics (ROC) curve was constructed for the
analyzed microarray design (Grape custom microarray design)
assuming RNA-Seq gene expression data set as reference. Each
point on the ROC curve of the Grape custom array represents the
sensitivity on Y-axis (True Positive Rate) and the specificity on X-
axis (False Positive Rate). Deletion of less specific oligonucleotide
probes out the Grape custom microarray design enlarged the area
under the curve (AUC from 0.70 to 90.4%) as showed in Figure 2.
These results demonstrated that less specific oligonucleotide
probes in a microarray design give false signal detection in gene
expression level measurement. By contrast, a high proportion of
specific oligonucleotide probes in microarray design increased it
specificity and accuracy in the discrimination of DEGs when RNA-
Seq gene expression data was assumed as reference. Taking
together, these analysis showed that Grape custom microarray
platform enriched by gene target specific oligonucleotide probes
exhibits a quite similar performance (specificity and accuracy) to
RNA-Seq discriminating DEGs in gene expression differential
analysis (see Figure 2).
Dago Dougba Noel et.al
Figure 2 ROC curve of 17850 genes of Grape custom array platform
in detecting DEGs with all oligonucleotide probes (left) and with only
specific oligonucleotide probes (right). RNA-Seq gene expression
data have been used as reference.
DISCUSSION AND CONCLUSION
The recent development of transcriptomic approaches based on
Next Generation Sequencing (NGS) is gaining popularity as they
provide a genome-wide, precise, quantitative measure of gene
expression. Although RNA-Seq is still a technology under active
development, it offers several key advantages over microarray
technology. Furthermore, several studies showed that RNA-Seq can
accurately reveal gene expression difference [16]. However, because
of the extensive legacy of the data and installed instrument base,
microarrays may still be widely used in the foreseeable future.
Although microarrays have been extensively used as discovery tools
for biological and biomedical studies, the challenge remains whether
this technology can be applied reliably in clinical practice and
regulatory decision making, where high precision and accuracy in
performance are required. Moreover, the power of this technology
depends on the number, identity and quality of the oligonucleotide
probes [23]. In the present paper we investigated how
oligonucleotide probes specificity in Grape custom microarray design
based on longer multiple oligonucleotide probes per gene model
transcript could influence its reliability in gene expression analysis.
Hence, we established a relationship between DEGs indentified by
(i) Grape custom microarray design, (ii) RNA-Seq or (iii) both
platforms and their respective specific oligonucleotide probes
number. However, oligonucleotide probes that recognized
exclusively their gene model transcript at a Tm ≥85±15°C with no
mismatch have been classified as specific.
Our findings showed an involvement of a substantial number of
specific oligonucleotide probes in the discrimination of DEGs
previously recognized as such by both microarray and RNA-Seq
platforms suggesting that accord between the two analyzed gene
expression platforms (Grape custom microarray design and RNA-
Seq) can be improved by increasing the ratio of specific
oligonucleotide probes in microarray design. Next, we showed that
more specific oligonucleotide probes were involved in the
discrimination of DEGs previously recognized as such by RNA-Seq
platform. By contrast a similar percentage of specific and less
specific oligonucleotide probes claimed to be associated with DEGs
exclusively detected by the Grape custom microarray design.
Considered as a whole, these results support a higher specificity of
RNA-Seq technology in the discrimination of DEGs in gene
expression differential analysis [22]. Furthermore, the deletion of less
specific oligonucleotide probes from the set of analyzed
oligonucleotide probes of Grape custom microarray design, improved
the correlation between both microarray and RNA-Seq platforms in
gene expression differential analysis FC measurement showing that

4. African Journal of Science and Research , 2014,(3)5:08-12
for oligonucleotide probes that are not optimized for sequence
specificity, microarrays can generate false positive data due to non
specific cross-hybridization to highly similar sequences gene families
[5-6].
The ROC curve is a two-dimensional depiction of classifier
performance. To compare classifiers we may want to reduce ROC
performance to a single scalar value representing expected
performance. A common method is to calculate the area under the
ROC curve, abbreviated AUC [24-25]. Indeed, ROC curve analysis
performed in this investigation suggested that an accurate selection
of oligonucleotide probes by integrating a thermodynamic parameter,
(selection of strongly specific oligonucleotide probe) allowed the
Grape custom microarray design to achieve a quite similar
performance to that of the RNA-Seq in calling DEGs in gene
expression differential analysis. Therefore, we demonstrated that
microarray designed oligonucleotide probes could be best selected
by the integration of thermodynamic parameters [20]. Moreover, our
investigation showed that discrepancy between RNA-Seq and
microarray platforms in gene expression differential analysis could
be due to the presence of less specific oligonucleotide probes in
microarray design. As expected, we also showed that a high rate of
specific oligonucleotide probes in a microarray design increase it
specificity and accuracy in discriminating DEGs in gene expression
differential analysis. Then, stringent selection of gene model target
specific oligonucleotide probes based on score and thermodynamic
parameters, improved strongly the performance of microarray
technology in gene expression measurement analysis.
In conclusion this investigation holds that microarray design enriched
by gene target specific oligonucleotide probes exhibits a quite similar
performances to that of RNA-Seq in gene expression differential
analysis, suggesting that (i) the accurate detection of DEGs by
microarray platform and that (ii) the agreement between both
microarray and RNA-Seq gene expression platforms is strongly
correlated with the best selection of gene target specific
oligonucleotide probes based on thermodynamic parameters.
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