BACKGROUND
transcriptome of an organism represents the entire repertoire of transcripts encoded by the genes as a phenotypic response to the condition in which they exist.
the sheer ability to simultaneously quantify the expression levels for a vast number of genes has revolutionized the biomedical research, facilitating the analysis of global gene expression patterns at the genome-wide scale  <cit> .
in the past decade, there has been a tremendous progress in the development of methods to deduce and quantify the gene expression levels at the whole transcriptome level  <cit> .
among the several transcriptome profiling methods, rna-seq and dna microarray stand out as the two widely used genome-wide gene expression quantification methods  <cit> .
rna-seq method involves the conversion of isolated transcripts into the complementary dna , which is then directly sequenced in a massively parallel deep-sequencing-based approach  <cit> .
by mapping the resulting short sequencing reads onto the reference genome, the expression levels of genes relative to the condition of interest or absolute levels can be quantified  <cit> .
this method has been implemented in different platforms like illumina’s genome analyzer, roche  <dig> genome sequence, and applied biosystems’ solid  <cit> .
on the other hand, microarray is based on the hybridization of specimen target strands onto the immobilized complementary probe strands.
for example, in a two-color microarray, transcripts extracted from different conditions are labeled with distinct fluorescent dyes while being converted to cdna.
these labeled samples are then hybridized to the immobilized complementary probe strands in an array representing the genes.
by measuring the light intensity of the distinct fluorescent dyes, the relative abundance of each transcript in the two different conditions can be measured  <cit> .
affymetrix and agilent are the two prevalent platforms in microarray technology  <cit> .
even though, initially microarray has been instrumental in whole transcriptome analysis, currently rna-seq is becoming a preferred method of choice, since it is considered to effectively surmount the limitations of microarray  <cit> .
rna-seq technology, unlike microarray, does not depend on the prerequisite knowledge of the reference transcriptome  <cit> .
further, rna-seq data contains very low background signal, a higher dynamic range of expression levels, and also relatively small amount of total rna required for quantification, when compared to microarray  <cit> .
despite these advantages, the efficiency of rna-seq is marred with the problem of overwhelming amount of ribosomal rna  in the data, short reads, less base accuracy, and variation of read density along the length of the transcript, posing a challenge for this high-throughput method  <cit> .
however, in spite of their strengths and limitations, rna-seq and microarray have become the default popular methods of choices for genome-wide transcriptome studies  <cit> .
currently several studies have been conducted to compare the performance of rna-seq and microarray in quantifying the expression level of genes, by focusing on various aspects like reproducibility, accuracy, statistical issues, technical and biological variabilities  <cit> .
the main conclusion from these studies has been that the expression levels quantified by these two methods correlated to a large extent, and overall favored the rna-seq because of high reproducibility, accuracy, and dynamic range  <cit> .
however, none of these comparison studies have addressed if these two methods complement each other in transcriptome profiling given the strengths and limitations associated with them.
in order to address this question, we require an already well characterized dataset.
the hrpx regulome from xanthomonas citri subsp.
citri  serves as an ideal data model in this regard  <cit> .
xcc is a causal agent of citrus canker, one of the serious and destructive diseases in citrus that is resulting in significant losses to citrus industry worldwide  <cit> , while hrpx is a key global transcription factor that regulates the expression of hrp  cluster of genes, which are considered as the major pathogenicity factors  <cit> .
hrpx contains arac-type of dna binding domain, which specifically recognizes the plant-inducible promoter  box  and imperfect pip box  present in the cis-regulatory regions of hrp gene cluster  <cit> .
since hrpx has a key role in pathogenicity, tremendous progress has been made in cataloguing the target genes of hrpx  <cit> .
we therefore assessed the performance of rna-seq and microarray in their ability to detect known hrpx target genes.
we chose illumina and agilent as the corresponding platforms for rna-seq and microarray, as they are the most popular platforms for these technologies  <cit> .
RESULTS
in order to uncover the regulome of hrpx transcription regulator by profiling the wild-type and the hrpx mutant strains transcriptome, we had designed a microarray chip covering the whole genome under agilent platform in our previous study  <cit> .
here, we conducted genome-wide transcriptome profiling of these two strains by rna-seq and compared the results to the previously published microarray data, to assess the performance of these two methods.
further, to avoid technical variation associated with rna isolation, we used the aliquots from the same total rna samples used for microarray experiments also for rna-seq.
we obtained  <dig> , <dig>   <dig> , <dig>   <dig> , <dig> sequence reads for the wild-type and  <dig> , <dig>   <dig> , <dig>  and  <dig> , <dig> for the hrpx mutant strain with a median sequence length of 74-base pairs  .
raw reads often have high sequencing errors, especially in the 3′ end where there is a high chance of sequencing errors to occur  <cit> .
we therefore filtered the reads for high quality ones by trimming off the base pairs with low quality score assigned to them during down-line processing of rna-seq.
more than 90% of the reads passed the quality filter, as a result, the median sequence length of quality filtered reads subsequently dropped to 68-bp .
we then mapped these high quality trimmed reads on to the xcc genome.
approximately more than 90% of the reads could be mapped on to the reference genome, indicating good sequence coverage .
overall ~97% of the annotated genes had more than one read mapped, while merely ~3% of the annotated genes had no reads mapped, indicating good sequencing depth.
further, we also observed a difference in the sequence coverage between the chromosome and the two endogenous plasmids of xcc.
annotated coding genes from the chromosome with a size of  <dig>  mega base pairs  had 98% sequence coverage, whereas, it was 78% for plasmid pxac <dig> with a size of  <dig>  mb, and relatively lower with only 62% sequence coverage for plasmid pxac <dig> with a size of  <dig>  mb .
comparison at absolute levels of expression
rna-seq had coverage for  <dig> genes with one or more reads mapped, while by microarray  <dig> genes were assigned the fluorescence intensity values after the background correction.
among these  <dig> genes  were common to both methods, while merely  <dig>  and  <dig> genes  were uniquely called by microarray and rna-seq respectively .
we compared the absolute levels of gene expression in terms of rna-seq counts and microarray fluorescence intensities for all the listed genes called by both the methods.
these two independent measures of transcript abundance associated with each gene for all the biological replicates from the wild-type and the hrpx mutant strains were compared separately.
the resulting correlation was mapped as a scatter plot, with an average number of counts from illumina sequencing against the normalized fluorescence intensities from agilent arrays for each gene in the wild-type  as well as in the hrpx mutant .
absolute levels of gene expression correlated well, when estimated in terms of spearman’s correlation coefficient  with  <dig>   for the wild-type and  <dig>   for the hrpx mutant strain.
this is in agreement with the previous reports that expression levels measured by microarray and rna-seq had correlations ranging between  <dig>  and  <dig>  for prokaryotic and eukaryotic datasets  <cit> .
however, there seems to be little or no correlation for the genes with low level of expression.
we further estimated the correlation for the subset of genes with fluorescence intensity values ≤ <dig> assigned by microarray  with the corresponding expression levels determined by rna-seq.
this subset of genes revealed a very poor rs of  <dig>   and  <dig>   for the wild-type and the hrpx mutant strains respectively.
although the expression levels of these genes did not change much according to microarray, rna-seq reported them to have different expression levels.
this may be attributed to the high sensitivity of rna-seq method.
we further estimated the correlation between all the combinations of biological replicates for the wild-type and the hrpx mutant strains independently.
the resulting rs values of these comparisons are represented in the form of heat maps, for the wild-type  and the hrpx mutant strains , which provide a global view of these correlations.
overall, on an average the wild-type with rs =  <dig>   and the hrpx mutant with rs =  <dig>   were observed for the biological replicates from all the correlation combinations.
this level of comparison strongly suggested that not only the absolute level of gene expressions determined by rna-seq and microarray highly correlated, but were also highly reproducible, in spite of the technical as well as the biological variability associated with the quantifications.
comparison at relative levels of expression
we also compared the performance of these two methods at relative level of gene expression.
for this purpose, we first computed the relative expression level of genes in terms of fold-change  for the hrpx mutant in relation to the wild-type strain, along with p-values to denote the statistical significance and false discovery rate , for having a good control over the false positives rate.
we compared the relative expression levels for  <dig> consensus genes both qualitatively and quantitatively, after transforming the fc values to logarithm base  <dig>  scale without any statistical cut-off thresholds .
for the  <dig>  genes, the expression levels agreed qualitatively, while  <dig>  genes disagreed between the two methods .
at this point, our comparison was exclusively focused on whether the gene of interest is up- or down-regulated based on the sign of the log <dig> transformed fc values, but not necessarily on the fc magnitude.
we further illustrated the quantitative relationship of log2fc between rna-seq and microarray in the form of a scatter plot as shown in figure 2b.
genes with no change in expression levels in the wild-type and the hrpx mutant strains  clustered around log2fc of zero  in the scatter plot .
the rs between the log2fcs determined by rna-seq and microarray was found to be  <dig>   .
this lower correlation value indicated that the magnitude of fcs between the two methods differed largely that might be due to the background noise resulting from the many imperfections, which are inherent to the high-throughput technologies  <cit> .
the correlation coefficient provides an overall estimate of correlation between the expression levels determined by rna-seq and microarray methods.
however, this does not zoom into the data in a detailed manner.
for instance, no information is provided about how much of fc magnitude that actually differs between the two methods for a given gene.
in order to get an insight into this aspect, we computed the fraction of genes deviating in their fc magnitude values by dividing the fc magnitude value determined by rna-seq with that of microarray .
here, the fold difference of one represents the fraction of genes that are determined to have a fc magnitude of ±  <dig>   by both rna-seq and microarray methods.
when we plotted this frequency as a histogram for the whole  <dig> consensus genes, more than 75% of genes were found to have fc magnitude values ±  <dig>  by rna-seq and microarray methods.
since it is a relative expression comparison, genes whose expression values did not change much in the wild-type and the hrpx mutant strains, tend to have fc values =  <dig>  subsequently, it is more sensible to consider only differentially expressed set of genes for further comparisons.
we therefore applied fdr ≤  <dig>   in conjunction with fc  to filter the whole data set.
in total,  <dig>  genes from rna-seq and  <dig>  from microarray qualified at this cut-off threshold from the  <dig> consensus genes .
together,  <dig> genes satisfied our selection criterion from both the methods .
among them  <dig>  genes were up-regulated, while  <dig>  genes were found to be down-regulated.
further,  <dig>  genes were common between both the methods, whereas,  <dig>  and  <dig>  genes were uniquely detected by rna-seq and microarray respectively .
we further compared the fc values of the  <dig> consensus genes both qualitatively and quantitatively.
these genes qualitatively agreed 100% by having the same trend of log <dig> transformed fc values by both rna-seq and microarray .
likewise the quantitative comparison was performed by estimating the correlation between the magnitude of log2fc determined by rna-seq and microarray for the  <dig> consensus genes as shown in figure 2e.
the magnitude of fc values between the two methods were found to be well correlated , indicating that the same trend of variation was observed in fc values between the two methods without any dispersion.
thereby, the magnitude of fc values determined by rna-seq and microarray agreed to a large extent for the  <dig> consensus genes.
in order to further pinpoint the deviation in the fc magnitude quantified by the two methods, we plotted the differences in the fc values determined by rna-seq with respect to microarray, and the percentage of genes with that difference for the  <dig> consensus genes .
majority of the genes  were found to have a magnitude of fc within the range of ≤  <dig> , while for the remaining 2% of the genes, it was  <dig> -times higher in rna-seq than the microarray based quantification.
based on these comparisons, we concluded that the relative gene expression levels quantified by rna-seq and microarray were consistent to a large extent for the statistically differentially expressed set of consensus genes.
comparison with qrt-pcr
traditionally, quantitative reverse transcription pcr  is used to validate the gene expression levels quantified by high-throughput technologies like rna-seq and microarray  <cit> .
therefore, we compared the relative expression levels quantified by rna-seq and microarray by qrt-pcr for a subset of  <dig>  genes  that were randomly selected from the  <dig> significantly differentially expressed genes.
among them,  <dig> genes were found to be common between both the methods,  <dig> genes were unique to rna-seq, while remaining  <dig> genes were found to be unique to microarray .
the expression levels were found to be highly reliable for genes that are determined to be significantly differentially expressed by rna-seq  as well as microarray .
for the consensus genes, microarray had a slightly higher correlation with qrt-pcr than rna-seq .
we further plotted the percentage of genes that deviated in the magnitude of fc quantified by rna-seq and microarray with respect to qrt-pcr .
for most of the genes, the magnitude of fc quantified by rna-seq and microarray were relatively higher, when compared to qrt-pcr .
overall, the magnitude of fc quantified by rna-seq was in consistence with qrt-pcr based quantification .
for microarray, the magnitude of fc was observed to be consistent with qrt-pcr for a majority of genes, however, we also noticed outlier genes with a 9-times higher fc magnitude .
for the subset of  <dig> genes that were found to be uniquely determined by rna-seq, the magnitude of fc quantified by rna-seq correlated moderately with qrt-pcr  .
the  <dig> genes found to be uniquely detected by microarray had a correlation of rs =  <dig>   with qrt-pcr .
these correlations are slightly lower when compared to the consensus genes .
this indicated that the expression levels are more reliable for the genes that are determined to be significantly differentially expressed by both rna-seq and microarray rather than by any one method.
moreover, it also indicated that there is a lot of variation in the magnitude of fc quantified by rna-seq and qrt-pcr.
we further evaluated this variation i.e.
deviation from the magnitude of fc, by plotting the frequency histogram for the  <dig> genes unique to rna-seq  and microarray .
for the genes unique to rna-seq, we observed that none of them had the same magnitude of fc, with 50% genes having  <dig> to  <dig> -time lower and for the remaining 50% of the genes the magnitude of fc was observed to be  <dig> to 3-time higher, when compared to qrt-pcr .
because of this inconsistence in the magnitude of fc, the expression levels are moderately correlated.
for the genes unique to microarray, we observed a good consistence in the magnitude of fc with qrt-pcr .
comparison in terms of detection of genes encoding t3ss and effectors
extensive and detailed studies have been carried out since past three decades in cataloguing the target genes of hrpx in the genus xanthomonas using various genetic and biochemical methods  <cit> .
hrpx is known to regulate hrp gene cluster that encodes the type iii secretion system  and effectors  <cit> .
t3ss are specialized macromolecular machinery that act as a nano-injector to translocate the effector proteins into the cytoplasm of host plant cells  <cit> .
these translocated effectors manipulate the host cellular processes by altering signal transduction, transcriptional activities like suppression of basal plant defense responses, and protein turnover in host cells for the benefit of the pathogen  <cit> .
the t3ss machineries are evolutionarily conserved across many gram-negative animal- and plant-pathogenic bacteria  <cit> .
xcc is comprised of  <dig> hrp genes, including  <dig> hrp-conserved  and  <dig> hrp-associated  genes that encode the t3ss  <cit> .
these genes are clustered in a ~ <dig> kb region spanning from  <dig> to  <dig> bp of the genome  <cit> .
we applied statistically significant differentially expressed gene list that were derived from rna-seq and microarray methods to this cluster.
we counted for the number of known hrp cluster genes, which passed the fc and fdr cut-off thresholds from rna-seq and microarray methods .
among the  <dig> hrp cluster genes,  <dig>  were detected by both rna-seq and microarray methods.
six genes were found to be uniquely detected by microarray, whereas, none uniquely detected by rna-seq .
three genes namely, hrcc, hpa <dig>  and hpaa could not pass our statistical cut-off criteria by any of the methods, although they followed the same qualitative expression pattern.
we further quantified the deviation in the magnitude of fc for the  <dig> known hrp genes, found in consensus between rna-seq and microarray .
the magnitude of fc for 5% genes found to be same, while for the remaining 95% genes it was found to be between  <dig>  to  <dig> -time higher in rna-seq than in microarray.
even though, microarray overall detected more genes from hrp cluster, rna-seq reported higher magnitude of fc .
hrp
cluster genes detected by rna-seq and microarray
2
2
† consensus between rna-seq and microarray <dig> genes .
ξ only rna-seq  <dig> gene .
ψ only microarray  <dig> genes .
$ undetected  <dig> genes .
the list of known t3ss hrp cluster genes along with the information about the log2fc, p-value and fdr value from rna-seq and microarray experiments.
"detected by" column assigns by which method the known hrp cluster gene is found to be significantly differentially expressed.
xcc also encodes  <dig> putative effector genes regulated by hrpx, which meditate the interaction with the host plant, hence determine the host specificity  <cit> .
since xac <dig>  xac <dig> and xac <dig> were considered as pseudo or inactive genes, they were excluded from our analysis.
we tabulated how many of these genes were detected by rna-seq and microarray methods with their corresponding log2fc values along with p-value and fdr from the respective methods .
in total,  <dig>  genes were detected by both the methods.
rna-seq and microarray uniquely detected one and three genes respectively.
the remaining  <dig> genes  were neither detected by rna-seq nor by microarray, since they could not pass both the fc and fdr cut-offs .
for the  <dig> consensus genes, we calculated the fold differences in the magnitude of fc quantified by rna-seq with respect to microarray.
none of the genes had the same magnitude of fc between the two methods.
microarray estimated higher magnitude of fc for ~64% genes than rna-seq, while rna-seq estimated  <dig>  to  <dig> -time higher magnitude of fc for the remaining ~36% genes .
in contrast to hrp gene cluster, where microarray qualitatively outperformed rna-seq in its ability to detect more genes, here rna-seq complemented quantitatively with higher confidence by reporting higher magnitude of fcs.
thereby, for the effector gene data set, rna-seq and microarray complemented each other both qualitatively as well as quantitatively.
2
2
† consensus between rna-seq and microarray  <dig> genes .
ψ only microarray  <dig> genes .
ξ only rna-seq  <dig> genes .
$ undetected  <dig> genes .
the list of known t3ss effector genes along with the information about the log2fc, p-value and fdr value derived from rna-seq and microarray experiments.
"detected by" column assigns by which method the following effector genes are found to be significantly differentially expressed.
overall, considering t3ss and effector genes, in total there are  <dig> genes, from which,  <dig> genes  were detected by both rna-seq and microarray .
rna-seq uniquely detected  <dig> gene , whereas, microarray detected  <dig> genes .
remaining  <dig> genes  were not detected by either one of the methods by failing to pass the cut-off threshold .
further, considering only the genes that are detected by at least one method, 72% of the known were detected by both methods, while remaining 28% were detected by either one of the methods.
genes uniquely detected by rna-seq and microarray
among the  <dig> statistically significant differentially expressed genes from rna-seq,  <dig>  genes were found to be uniquely detected by this method .
of these  <dig> genes,  <dig> were found to be down-regulated, while  <dig> were up-regulated .
nearly 98% of these genes  could not pass the fc cut-off threshold by microarray.
the only exception is the gene flio  that encodes a flagellar protein for flagellum apparatus, which passed the fc cut-off, but failed with fdr threshold.
the gene xac <dig> encoding kdpf, a component of an integral membrane potassium-transporting system  <cit> , is down-regulated by a factor of  <dig>  according to rna-seq, but, microarray could not capture this, as the probes for this gene were missing on the chip.
this shows the limitation of microarray, where probes for all the genes need to be defined while designing the chip.
furthermore, four genes uniquely found by rna-seq are involved in signal transduction and gene regulation, i.e.
xac <dig> encoding a serine/threonine kinase, xac <dig> encoding a tryptophan-rich sensory protein, and two regulatory genes xac <dig>  and xac <dig>  whose function in citrus canker disease development remain to be explored.
furthermore,  <dig> genes  are currently annotated as hypothetical proteins .
among them, four hypothetical proteins xac <dig>  xac <dig>  xac <dig>  and xacb <dig> were predicted to be t3ss secreted while  <dig> hypothetical proteins, xac <dig>  xac <dig>  xac <dig>  xac <dig>  xac <dig>  xac <dig>  and xac <dig> were predicted to be type  <dig> secretion system  substrates  by effective database  <cit> .
gram-negative bacteria employ t2ss to transport proteins to the extracellular milieu, where the t2ss exo-proteins containing n-terminal signal peptides are used for inner-membrane translocation through either the sec translocon or the tat complex  <cit> .
genes encoding proteins secreted by t3ss and t2ss have been experimentally proved to be regulated by hrpx  <cit> .
among the  <dig> statistically significant differentially expressed genes from microarray,  <dig>  genes were found to be uniquely detected by this method .
<dig> were found to be down-regulated, while one gene was up-regulated .
unlike that of rna-seq, nearly 63% genes  could pass the fc cut-off threshold, but failed to pass the fdr threshold by rna-seq.
the remaining 37% genes  could not pass both fc and fdr cut-off threshold.
furthermore, six genes were found to be hypothetical.
among them xac <dig>  xac <dig>  and xac <dig> were predicted as t2ss substrates.
xac <dig> predicted as a t2ss substrate, shared 73% identity with a putative secreted protein from x. campestris pv.
vesicatoria strain 85– <dig>  another t2ss candidate xac <dig> shared 95% identity with a secreted protein from x. fuscans subsp.
aurantifolii str.
icpb  <dig>  xac <dig> shared 100% identity with mekhla domain protein from x. axonopodis pv.
punicae str.
lmg  <dig>  <cit> .
this domain is found in bacteria associated with plants.
it further shares similarity with the pas domain and might be involved in light, oxygen, and redox potential sensation  <cit> .
comparison at the level of functional annotations of genes
for comparison based on the biological function for the differentially expressed genes from rna-seq and microarray, we utilized the cluego to integrate the gene ontology   <cit>  terms and kegg  <cit>  pathway terms and create a functionally organised go/kegg network.
functional annotation with biological processes category resulted in  <dig>  genes found from cluster for rna-seq, while for microarray it was  <dig> .
the cluego overview pie chart highlighted that significant proportion of the genes differentially regulated are involved in “protein secretion by the t3ss” by both rna-seq and microarray .
additionally, rna-seq also identified genes involved in “secretion activity by cell” as well as “single organism catabolic process” .
on the other hand, microarray highlighted the genes involved in “protein transmembrane transport”, “polycyclic aromatic hydrocarbon degradation” and “establishment of localization in cell” .
majority of the genes are involved in “bacterial secretion system”, as shown by both rna-seq and microarray.
also the differentially expressed genes are found to be significantly involved in the “transport of monovalent inorganic cation”  and “protein transport” .
genes have also been found uniquely by microarray as significantly involved in “polycyclic aromatic hydrocarbon degradation” .
genes from rna-seq have been found to be involved in “riboflavin metabolism” as well as “single organism catabolic process” .
further, visualization of the functionally grouped annotation network for the differentially regulated genes derived from rna-seq  and microarray  methods highlighted the relationships between the terms.
rna-seq highlighted “protein secretion by the t3ss” along with the “small molecule catabolic process”, while microarray reflected “polycyclic aromatic hydrocarbon degradation” and “establishment of localization in cell”, as the most significant terms of the group.
this analysis also showed that rna-seq and microarray together provide more comprehensive functional information than the individual methods.
pip box detection
hrpx is known to regulate the target gene expression by specifically binding to pip box motif present in the cis-regulatory regions.
pip box consists of direct repeats of “ttcgc” with a spacer of  <dig> to 26-bps in between the repeats, even though ideally 8-bps and 15-bps are considered as the canonical pip box  <cit> .
we exploited this feature and looked for pip boxes in the promoter regions of the  <dig> significantly differentially expressed genes .
all the  <dig> differentially expressed genes could be assigned to  <dig> transcriptional units based on metacyc database  <cit>  .
however for simplicity, genes under the control of the same cis-regulatory regions were counted separately.
among the consensus  <dig> genes,  <dig>  were shown to have canonical pip boxes .
of the  <dig> genes that are uniquely determined by rna-seq,  <dig>  genes were confirmed to have pip boxes; whereas, among the  <dig> genes that are uniquely determined by microarray  <dig>  genes were confirmed to have pip boxes .
in this study, we identified newly pip box motif in  <dig>  genes among consensus,  <dig>  genes unique to rna-seq and  <dig>  gene unique to microarray .
overall,  <dig> of the  <dig>  significantly differentially expressed genes were confirmed to have pip boxes in their cis-regulatory regions .
the presence of pip box confirmed that these genes may be directly regulated by hrpx, while the remaining  <dig> that do not have pip boxes may be indirectly regulated by hrpx via the other transcription factors.
in this regard, we looked for genes with sequence specific dna binding activity in the  <dig> differentially expressed genes.
six genes namely hrpg, pcaq, blal, xac <dig>  xac <dig>  and xac <dig> were known to have sequence specific dna binding activity according to go annotation.
among them, xac <dig>  xac <dig>  and blai have been newly identified in this study containing pip box motif .
thereby these  <dig> transcription regulators are directly regulated by hrpx, which in turn we assume regulate the  <dig> genes, which do not contain the pip box motif and hence indirectly regulated by hrpx.
the dna binding signatures for many of these transcription factors are unknown; hence, obscure the further confirmation of regulation by these transcription factors.
nevertheless, the fact that many of the genes that were uniquely determined by each method showed a clear pip box in their cis-regulatory regions reiterates that rna-seq and microarray complement each other.
discussion
currently, rna-seq is becoming the preferable choice for gene expression profiling in place of microarrays.
although, all the parameters that influence the various aspects of this method are yet to be understood completely, rna-seq undoubtedly is playing a very important role in deciphering the complexity of the transcriptome by giving a new direction to isoforms, allelic expression, untranslated regions, splice junctions, antisense regulation and intragenic expression  <cit> .
several studies have begun to investigate on the parameters like sequencing depth, precision, gc bias, length bias, lane effects, and processing artifacts  <cit> .
on the other hand, microarrays are in usage for more than two decades.
therefore, most of the biases inherent to this method have become more apparent  <cit> .
for instance, biases in the hybridization of the samples labeled with cyanine <dig>  and cyanine <dig>  are sufficiently explored, and currently several approaches are practiced to minimize such effects  <cit> .
further, systematic variability like influence of the image scanner settings on the dye intensity measurements have now been robustly handled by applying various normalization techniques  <cit> .
despite these developments, some inherent genes–specific biases like differential hybridization efficiencies of the labeled target transcript to the same probe are still found to be inevitable in microarrays.
in rna-seq as well as microarray, all these known and unknown parameters influence the final outcome.
therefore, in this study, we focused on the assessment of rna-seq and microarray based on the final outcome .i.e.
statistically significant differentially expressed genes.
in comparison with previous rna-seq studies, with a sequence coverage of 97% we observed for our data set, is in consistence with the reported  <dig> % to 95% coverage observed in other bacterial rna-seq studies  <cit> .
in our study, rna-seq has identified more significantly differentially expressed genes , when compared to microarray  as in previous studies  <cit> .
the overall correlation  in the magnitudes of fc for the consensus genes between the two methods was found to be similar or higher than previous studies  <cit> .
furthermore, our comparison analysis with qrt-pcr suggested that the expression levels were highly reliable for those genes that were determined to be differentially expressed by both rna-seq and microarray.
hence, confirming the differential expression of genes by multiple methods reduces false positives thereby enhances the biological discovery.
even though microarray overall outperformed rna-seq by detecting more known hrpx target genes from the t3ss in hrp cluster by satisfying both fc and fdr cut-off threshold, in principle rna-seq also detected genes hrpb <dig>  hrcs, hpap, xac <dig>  hrpb <dig>  and hrct, in terms of fc, but failed to pass fdr threshold.
this parameter is more directly influenced by error model considered in the statistical method that is used to infer the differential expression rather than rna-seq itself.
for the same read counts, one can get slightly different fdr values depending on the statistical method  <cit> .
but the implementation of all the statistical methods is not feasible for every dataset.
from the t3ss in hrp cluster, three genes namely, hrcc, hpa <dig>  and hpaa were not found to be detected by both rna-seq and microarray, mainly because they fail to pass fdr threshold.
interestingly, our previous microarray analysis confirmed that all these three genes are regulated by hrpx, but only at a later stage of the growth phase by satisfying both fc and fdr cut-off thresholds  <cit> .
this consolidates the regulation of some of the genes at later stages of the growth phase.
further, in case of type iii effector genes,  <dig> genes  were not detected by both rna-seq and microarray within considered cut-off threshold limit.
however, among them xopl, avrbs <dig>  xopak and xopz were found to be regulated by hrpx only at the later stage of the growth phase , according to our previous microarray analysis  <cit> .
further, four genes namely, ptha <dig>  ptha <dig>  ptha <dig>  ptha <dig> were regulated by another transcription regulator hrpg at early stage of growth phase  as observed in our previous study, while another undetected gene xope was found to be also regulated by hrpg, but only at od <dig> =  <dig>  time point of growth phase  <cit> .
thereby this study further validated our previous results.
subsequently, both methods detected 100% of the genes known to be regulated by hrpx  without any false positives.
among them, 72% were detected by both the methods while interestingly 28% of the known target genes were detected by either one of the methods.
hence, both the methods together could complement each other.
in addition  <dig> genes  were newly identified as differentially expressed by applying both microarray as well as rna-seq methods, thereby adding up to the already existing repertoire of hrpx regulated genes.
furthermore,  <dig>  genes among them were uniquely identified by either one of the methods.
overall,  <dig> newly identified genes were found to have pip box in their promoter regions, wherein  <dig>  genes were uniquely identified by either rna-seq or microarray.
the presence of the pip box in the promoter regions of the hrpx-regulated genes uniquely identified by rna-seq and microarray further not only confirmed that these genes are directly regulated by hrpx, but also that these candidates are not false positives.
consequently, 100% of the known hrpx regulated genes could only be detected together by both the methods, since each method missed out on some of the known genes; hence both the methods together enhance the understanding of hrpx regulome by providing a more comprehensive picture.
CONCLUSIONS
this study has significantly advanced our understanding of the regulome of the critical transcriptional factor hrpx and demonstrates that rna-seq and microarray complement each other in transcriptome profiling.
consequently, our study demonstrates the advantage of applying multiple transcriptome profiling methods to reveal a more comprehensive picture of a transcriptome, rather than relying solely on one method.

 transcriptome of an organism represents the entire repertoire of transcripts encoded by the genes as a phenotypic response to the condition in which they exist. the sheer ability to simultaneously quantify the expression levels for a vast number of genes has revolutionized the biomedical research, facilitating the analysis of global gene expression patterns at the genome-wide scale. in the past decade, there has been a tremendous progress in the development of methods to deduce and quantify the gene expression levels at the whole transcriptome level. among the several transcriptome profiling methods, rna-seq and dna microarray stand out as the two widely used genome-wide gene expression quantification methods. rna-seq method involves the conversion of isolated transcripts into the complementary dna, which is then directly sequenced in a massively parallel deep-sequencing-based approach. by mapping the resulting short sequencing reads onto the reference genome, the expression levels of genes relative to the condition of interest or absolute levels can be quantified. this method has been implemented in different platforms like illumina’s genome analyzer, roche genome sequence, and applied biosystems’ solid. on the other hand, microarray is based on the hybridization of specimen target strands onto the immobilized complementary probe strands. for example, in a two-color microarray, transcripts extracted from different conditions are labeled with distinct fluorescent dyes while being converted to cdna. these labeled samples are then hybridized to the immobilized complementary probe strands in an array representing the genes. by measuring the light intensity of the distinct fluorescent dyes, the relative abundance of each transcript in the two different conditions can be measured. affymetrix and agilent are the two prevalent platforms in microarray technology. even though, initially microarray has been instrumental in whole transcriptome analysis, currently rna-seq is becoming a preferred method of choice, since it is considered to effectively surmount the limitations of microarray. rna-seq technology, unlike microarray, does not depend on the prerequisite knowledge of the reference transcriptome. further, rna-seq data contains very low background signal, a higher dynamic range of expression levels, and also relatively small amount of total rna required for quantification, when compared to microarray. despite these advantages, the efficiency of rna-seq is marred with the problem of overwhelming amount of ribosomal rna in the data, short reads, less base accuracy, and variation of read density along the length of the transcript, posing a challenge for this high-throughput method. however, in spite of their strengths and limitations, rna-seq and microarray have become the default popular methods of choices for genome-wide transcriptome studies. currently several studies have been conducted to compare the performance of rna-seq and microarray in quantifying the expression level of genes, by focusing on various aspects like reproducibility, accuracy, statistical issues, technical and biological variabilities. the main conclusion from these studies has been that the expression levels quantified by these two methods correlated to a large extent, and overall favored the rna-seq because of high reproducibility, accuracy, and dynamic range. however, none of these comparison studies have addressed if these two methods complement each other in transcriptome profiling given the strengths and limitations associated with them. in order to address this question, we require an already well characterized dataset. the hrpx regulome from xanthomonas citri subsp. citri serves as an ideal data model in this regard. xcc is a causal agent of citrus canker, one of the serious and destructive diseases in citrus that is resulting in significant losses to citrus industry worldwide, while hrpx is a key global transcription factor that regulates the expression of hrp cluster of genes, which are considered as the major pathogenicity factors. hrpx contains arac-type of dna binding domain, which specifically recognizes the plant-inducible promoter box and imperfect pip box present in the cis-regulatory regions of hrp gene cluster. since hrpx has a key role in pathogenicity, tremendous progress has been made in cataloguing the target genes of hrpx. we therefore assessed the performance of rna-seq and microarray in their ability to detect known hrpx target genes. we chose illumina and agilent as the corresponding platforms for rna-seq and microarray, as they are the most popular platforms for these technologies.  in order to uncover the regulome of hrpx transcription regulator by profiling the wild-type and the hrpx mutant strains transcriptome, we had designed a microarray chip covering the whole genome under agilent platform in our previous study. here, we conducted genome-wide transcriptome profiling of these two strains by rna-seq and compared the results to the previously published microarray data, to assess the performance of these two methods. further, to avoid technical variation associated with rna isolation, we used the aliquots from the same total rna samples used for microarray experiments also for rna-seq. we obtained,,, sequence reads for the wild-type and,, and, for the hrpx mutant strain with a median sequence length of 74-base pairs. raw reads often have high sequencing errors, especially in the 3′ end where there is a high chance of sequencing errors to occur. we therefore filtered the reads for high quality ones by trimming off the base pairs with low quality score assigned to them during down-line processing of rna-seq. more than 90% of the reads passed the quality filter, as a result, the median sequence length of quality filtered reads subsequently dropped to 68-bp. we then mapped these high quality trimmed reads on to the xcc genome. approximately more than 90% of the reads could be mapped on to the reference genome, indicating good sequence coverage. overall ~97% of the annotated genes had more than one read mapped, while merely ~3% of the annotated genes had no reads mapped, indicating good sequencing depth. further, we also observed a difference in the sequence coverage between the chromosome and the two endogenous plasmids of xcc. annotated coding genes from the chromosome with a size of mega base pairs had 98% sequence coverage, whereas, it was 78% for plasmid pxac with a size of mb, and relatively lower with only 62% sequence coverage for plasmid pxac with a size of mb. comparison at absolute levels of expression rna-seq had coverage for genes with one or more reads mapped, while by microarray genes were assigned the fluorescence intensity values after the background correction. among these genes were common to both methods, while merely and genes were uniquely called by microarray and rna-seq respectively. we compared the absolute levels of gene expression in terms of rna-seq counts and microarray fluorescence intensities for all the listed genes called by both the methods. these two independent measures of transcript abundance associated with each gene for all the biological replicates from the wild-type and the hrpx mutant strains were compared separately. the resulting correlation was mapped as a scatter plot, with an average number of counts from illumina sequencing against the normalized fluorescence intensities from agilent arrays for each gene in the wild-type as well as in the hrpx mutant. absolute levels of gene expression correlated well, when estimated in terms of spearman’s correlation coefficient with for the wild-type and for the hrpx mutant strain. this is in agreement with the previous reports that expression levels measured by microarray and rna-seq had correlations ranging between and for prokaryotic and eukaryotic datasets. however, there seems to be little or no correlation for the genes with low level of expression. we further estimated the correlation for the subset of genes with fluorescence intensity values ≤ assigned by microarray with the corresponding expression levels determined by rna-seq. this subset of genes revealed a very poor rs of and for the wild-type and the hrpx mutant strains respectively. although the expression levels of these genes did not change much according to microarray, rna-seq reported them to have different expression levels. this may be attributed to the high sensitivity of rna-seq method. we further estimated the correlation between all the combinations of biological replicates for the wild-type and the hrpx mutant strains independently. the resulting rs values of these comparisons are represented in the form of heat maps, for the wild-type and the hrpx mutant strains, which provide a global view of these correlations. overall, on an average the wild-type with rs = and the hrpx mutant with rs = were observed for the biological replicates from all the correlation combinations. this level of comparison strongly suggested that not only the absolute level of gene expressions determined by rna-seq and microarray highly correlated, but were also highly reproducible, in spite of the technical as well as the biological variability associated with the quantifications. comparison at relative levels of expression we also compared the performance of these two methods at relative level of gene expression. for this purpose, we first computed the relative expression level of genes in terms of fold-change for the hrpx mutant in relation to the wild-type strain, along with p-values to denote the statistical significance and false discovery rate, for having a good control over the false positives rate. we compared the relative expression levels for consensus genes both qualitatively and quantitatively, after transforming the fc values to logarithm base scale without any statistical cut-off thresholds. for the genes, the expression levels agreed qualitatively, while genes disagreed between the two methods. at this point, our comparison was exclusively focused on whether the gene of interest is up- or down-regulated based on the sign of the log transformed fc values, but not necessarily on the fc magnitude. we further illustrated the quantitative relationship of log2fc between rna-seq and microarray in the form of a scatter plot as shown in figure 2b. genes with no change in expression levels in the wild-type and the hrpx mutant strains clustered around log2fc of zero in the scatter plot. the rs between the log2fcs determined by rna-seq and microarray was found to be. this lower correlation value indicated that the magnitude of fcs between the two methods differed largely that might be due to the background noise resulting from the many imperfections, which are inherent to the high-throughput technologies. the correlation coefficient provides an overall estimate of correlation between the expression levels determined by rna-seq and microarray methods. however, this does not zoom into the data in a detailed manner. for instance, no information is provided about how much of fc magnitude that actually differs between the two methods for a given gene. in order to get an insight into this aspect, we computed the fraction of genes deviating in their fc magnitude values by dividing the fc magnitude value determined by rna-seq with that of microarray. here, the fold difference of one represents the fraction of genes that are determined to have a fc magnitude of ± by both rna-seq and microarray methods. when we plotted this frequency as a histogram for the whole consensus genes, more than 75% of genes were found to have fc magnitude values ± by rna-seq and microarray methods. since it is a relative expression comparison, genes whose expression values did not change much in the wild-type and the hrpx mutant strains, tend to have fc values = subsequently, it is more sensible to consider only differentially expressed set of genes for further comparisons. we therefore applied fdr ≤ in conjunction with fc to filter the whole data set. in total, genes from rna-seq and from microarray qualified at this cut-off threshold from the consensus genes. together, genes satisfied our selection criterion from both the methods. among them genes were up-regulated, while genes were found to be down-regulated. further, genes were common between both the methods, whereas, and genes were uniquely detected by rna-seq and microarray respectively. we further compared the fc values of the consensus genes both qualitatively and quantitatively. these genes qualitatively agreed 100% by having the same trend of log transformed fc values by both rna-seq and microarray. likewise the quantitative comparison was performed by estimating the correlation between the magnitude of log2fc determined by rna-seq and microarray for the consensus genes as shown in figure 2e. the magnitude of fc values between the two methods were found to be well correlated, indicating that the same trend of variation was observed in fc values between the two methods without any dispersion. thereby, the magnitude of fc values determined by rna-seq and microarray agreed to a large extent for the consensus genes. in order to further pinpoint the deviation in the fc magnitude quantified by the two methods, we plotted the differences in the fc values determined by rna-seq with respect to microarray, and the percentage of genes with that difference for the consensus genes. majority of the genes were found to have a magnitude of fc within the range of ≤, while for the remaining 2% of the genes, it was -times higher in rna-seq than the microarray based quantification. based on these comparisons, we concluded that the relative gene expression levels quantified by rna-seq and microarray were consistent to a large extent for the statistically differentially expressed set of consensus genes. comparison with qrt-pcr traditionally, quantitative reverse transcription pcr is used to validate the gene expression levels quantified by high-throughput technologies like rna-seq and microarray. therefore, we compared the relative expression levels quantified by rna-seq and microarray by qrt-pcr for a subset of genes that were randomly selected from the significantly differentially expressed genes. among them, genes were found to be common between both the methods, genes were unique to rna-seq, while remaining genes were found to be unique to microarray. the expression levels were found to be highly reliable for genes that are determined to be significantly differentially expressed by rna-seq as well as microarray. for the consensus genes, microarray had a slightly higher correlation with qrt-pcr than rna-seq. we further plotted the percentage of genes that deviated in the magnitude of fc quantified by rna-seq and microarray with respect to qrt-pcr. for most of the genes, the magnitude of fc quantified by rna-seq and microarray were relatively higher, when compared to qrt-pcr. overall, the magnitude of fc quantified by rna-seq was in consistence with qrt-pcr based quantification. for microarray, the magnitude of fc was observed to be consistent with qrt-pcr for a majority of genes, however, we also noticed outlier genes with a 9-times higher fc magnitude. for the subset of genes that were found to be uniquely determined by rna-seq, the magnitude of fc quantified by rna-seq correlated moderately with qrt-pcr. the genes found to be uniquely detected by microarray had a correlation of rs = with qrt-pcr. these correlations are slightly lower when compared to the consensus genes. this indicated that the expression levels are more reliable for the genes that are determined to be significantly differentially expressed by both rna-seq and microarray rather than by any one method. moreover, it also indicated that there is a lot of variation in the magnitude of fc quantified by rna-seq and qrt-pcr. we further evaluated this variation i.e. deviation from the magnitude of fc, by plotting the frequency histogram for the genes unique to rna-seq and microarray. for the genes unique to rna-seq, we observed that none of them had the same magnitude of fc, with 50% genes having to -time lower and for the remaining 50% of the genes the magnitude of fc was observed to be to 3-time higher, when compared to qrt-pcr. because of this inconsistence in the magnitude of fc, the expression levels are moderately correlated. for the genes unique to microarray, we observed a good consistence in the magnitude of fc with qrt-pcr. comparison in terms of detection of genes encoding t3ss and effectors extensive and detailed studies have been carried out since past three decades in cataloguing the target genes of hrpx in the genus xanthomonas using various genetic and biochemical methods. hrpx is known to regulate hrp gene cluster that encodes the type iii secretion system and effectors. t3ss are specialized macromolecular machinery that act as a nano-injector to translocate the effector proteins into the cytoplasm of host plant cells. these translocated effectors manipulate the host cellular processes by altering signal transduction, transcriptional activities like suppression of basal plant defense responses, and protein turnover in host cells for the benefit of the pathogen. the t3ss machineries are evolutionarily conserved across many gram-negative animal- and plant-pathogenic bacteria. xcc is comprised of hrp genes, including hrp-conserved and hrp-associated genes that encode the t3ss. these genes are clustered in a ~ kb region spanning from to bp of the genome. we applied statistically significant differentially expressed gene list that were derived from rna-seq and microarray methods to this cluster. we counted for the number of known hrp cluster genes, which passed the fc and fdr cut-off thresholds from rna-seq and microarray methods. among the hrp cluster genes, were detected by both rna-seq and microarray methods. six genes were found to be uniquely detected by microarray, whereas, none uniquely detected by rna-seq. three genes namely, hrcc, hpa and hpaa could not pass our statistical cut-off criteria by any of the methods, although they followed the same qualitative expression pattern. we further quantified the deviation in the magnitude of fc for the known hrp genes, found in consensus between rna-seq and microarray. the magnitude of fc for 5% genes found to be same, while for the remaining 95% genes it was found to be between to -time higher in rna-seq than in microarray. even though, microarray overall detected more genes from hrp cluster, rna-seq reported higher magnitude of fc. hrp cluster genes detected by rna-seq and microarray 2 2 † consensus between rna-seq and microarray genes. ξ only rna-seq gene. ψ only microarray genes. $ undetected genes. the list of known t3ss hrp cluster genes along with the information about the log2fc, p-value and fdr value from rna-seq and microarray experiments. "detected by" column assigns by which method the known hrp cluster gene is found to be significantly differentially expressed. xcc also encodes putative effector genes regulated by hrpx, which meditate the interaction with the host plant, hence determine the host specificity. since xac xac and xac were considered as pseudo or inactive genes, they were excluded from our analysis. we tabulated how many of these genes were detected by rna-seq and microarray methods with their corresponding log2fc values along with p-value and fdr from the respective methods. in total, genes were detected by both the methods. rna-seq and microarray uniquely detected one and three genes respectively. the remaining genes were neither detected by rna-seq nor by microarray, since they could not pass both the fc and fdr cut-offs. for the consensus genes, we calculated the fold differences in the magnitude of fc quantified by rna-seq with respect to microarray. none of the genes had the same magnitude of fc between the two methods. microarray estimated higher magnitude of fc for ~64% genes than rna-seq, while rna-seq estimated to -time higher magnitude of fc for the remaining ~36% genes. in contrast to hrp gene cluster, where microarray qualitatively outperformed rna-seq in its ability to detect more genes, here rna-seq complemented quantitatively with higher confidence by reporting higher magnitude of fcs. thereby, for the effector gene data set, rna-seq and microarray complemented each other both qualitatively as well as quantitatively. 2 2 † consensus between rna-seq and microarray genes. ψ only microarray genes. ξ only rna-seq genes. $ undetected genes. the list of known t3ss effector genes along with the information about the log2fc, p-value and fdr value derived from rna-seq and microarray experiments. "detected by" column assigns by which method the following effector genes are found to be significantly differentially expressed. overall, considering t3ss and effector genes, in total there are genes, from which, genes were detected by both rna-seq and microarray. rna-seq uniquely detected gene, whereas, microarray detected genes. remaining genes were not detected by either one of the methods by failing to pass the cut-off threshold. further, considering only the genes that are detected by at least one method, 72% of the known were detected by both methods, while remaining 28% were detected by either one of the methods. genes uniquely detected by rna-seq and microarray among the statistically significant differentially expressed genes from rna-seq, genes were found to be uniquely detected by this method. of these genes, were found to be down-regulated, while were up-regulated. nearly 98% of these genes could not pass the fc cut-off threshold by microarray. the only exception is the gene flio that encodes a flagellar protein for flagellum apparatus, which passed the fc cut-off, but failed with fdr threshold. the gene xac encoding kdpf, a component of an integral membrane potassium-transporting system, is down-regulated by a factor of according to rna-seq, but, microarray could not capture this, as the probes for this gene were missing on the chip. this shows the limitation of microarray, where probes for all the genes need to be defined while designing the chip. furthermore, four genes uniquely found by rna-seq are involved in signal transduction and gene regulation, i.e. xac encoding a serine/threonine kinase, xac encoding a tryptophan-rich sensory protein, and two regulatory genes xac and xac whose function in citrus canker disease development remain to be explored. furthermore, genes are currently annotated as hypothetical proteins. among them, four hypothetical proteins xac xac xac and xacb were predicted to be t3ss secreted while hypothetical proteins, xac xac xac xac xac xac and xac were predicted to be type secretion system substrates by effective database. gram-negative bacteria employ t2ss to transport proteins to the extracellular milieu, where the t2ss exo-proteins containing n-terminal signal peptides are used for inner-membrane translocation through either the sec translocon or the tat complex. genes encoding proteins secreted by t3ss and t2ss have been experimentally proved to be regulated by hrpx. among the statistically significant differentially expressed genes from microarray, genes were found to be uniquely detected by this method. were found to be down-regulated, while one gene was up-regulated. unlike that of rna-seq, nearly 63% genes could pass the fc cut-off threshold, but failed to pass the fdr threshold by rna-seq. the remaining 37% genes could not pass both fc and fdr cut-off threshold. furthermore, six genes were found to be hypothetical. among them xac xac and xac were predicted as t2ss substrates. xac predicted as a t2ss substrate, shared 73% identity with a putative secreted protein from x. campestris pv. vesicatoria strain 85– another t2ss candidate xac shared 95% identity with a secreted protein from x. fuscans subsp. aurantifolii str. icpb xac shared 100% identity with mekhla domain protein from x. axonopodis pv. punicae str. lmg. this domain is found in bacteria associated with plants. it further shares similarity with the pas domain and might be involved in light, oxygen, and redox potential sensation. comparison at the level of functional annotations of genes for comparison based on the biological function for the differentially expressed genes from rna-seq and microarray, we utilized the cluego to integrate the gene ontology terms and kegg pathway terms and create a functionally organised go/kegg network. functional annotation with biological processes category resulted in genes found from cluster for rna-seq, while for microarray it was. the cluego overview pie chart highlighted that significant proportion of the genes differentially regulated are involved in “protein secretion by the t3ss” by both rna-seq and microarray. additionally, rna-seq also identified genes involved in “secretion activity by cell” as well as “single organism catabolic process”. on the other hand, microarray highlighted the genes involved in “protein transmembrane transport”, “polycyclic aromatic hydrocarbon degradation” and “establishment of localization in cell”. majority of the genes are involved in “bacterial secretion system”, as shown by both rna-seq and microarray. also the differentially expressed genes are found to be significantly involved in the “transport of monovalent inorganic cation” and “protein transport”. genes have also been found uniquely by microarray as significantly involved in “polycyclic aromatic hydrocarbon degradation”. genes from rna-seq have been found to be involved in “riboflavin metabolism” as well as “single organism catabolic process”. further, visualization of the functionally grouped annotation network for the differentially regulated genes derived from rna-seq and microarray methods highlighted the relationships between the terms. rna-seq highlighted “protein secretion by the t3ss” along with the “small molecule catabolic process”, while microarray reflected “polycyclic aromatic hydrocarbon degradation” and “establishment of localization in cell”, as the most significant terms of the group. this analysis also showed that rna-seq and microarray together provide more comprehensive functional information than the individual methods. pip box detection hrpx is known to regulate the target gene expression by specifically binding to pip box motif present in the cis-regulatory regions. pip box consists of direct repeats of “ttcgc” with a spacer of to 26-bps in between the repeats, even though ideally 8-bps and 15-bps are considered as the canonical pip box. we exploited this feature and looked for pip boxes in the promoter regions of the significantly differentially expressed genes. all the differentially expressed genes could be assigned to transcriptional units based on metacyc database. however for simplicity, genes under the control of the same cis-regulatory regions were counted separately. among the consensus genes, were shown to have canonical pip boxes. of the genes that are uniquely determined by rna-seq, genes were confirmed to have pip boxes; whereas, among the genes that are uniquely determined by microarray genes were confirmed to have pip boxes. in this study, we identified newly pip box motif in genes among consensus, genes unique to rna-seq and gene unique to microarray. overall, of the significantly differentially expressed genes were confirmed to have pip boxes in their cis-regulatory regions. the presence of pip box confirmed that these genes may be directly regulated by hrpx, while the remaining that do not have pip boxes may be indirectly regulated by hrpx via the other transcription factors. in this regard, we looked for genes with sequence specific dna binding activity in the differentially expressed genes. six genes namely hrpg, pcaq, blal, xac xac and xac were known to have sequence specific dna binding activity according to go annotation. among them, xac xac and blai have been newly identified in this study containing pip box motif. thereby these transcription regulators are directly regulated by hrpx, which in turn we assume regulate the genes, which do not contain the pip box motif and hence indirectly regulated by hrpx. the dna binding signatures for many of these transcription factors are unknown; hence, obscure the further confirmation of regulation by these transcription factors. nevertheless, the fact that many of the genes that were uniquely determined by each method showed a clear pip box in their cis-regulatory regions reiterates that rna-seq and microarray complement each other. discussion currently, rna-seq is becoming the preferable choice for gene expression profiling in place of microarrays. although, all the parameters that influence the various aspects of this method are yet to be understood completely, rna-seq undoubtedly is playing a very important role in deciphering the complexity of the transcriptome by giving a new direction to isoforms, allelic expression, untranslated regions, splice junctions, antisense regulation and intragenic expression. several studies have begun to investigate on the parameters like sequencing depth, precision, gc bias, length bias, lane effects, and processing artifacts. on the other hand, microarrays are in usage for more than two decades. therefore, most of the biases inherent to this method have become more apparent. for instance, biases in the hybridization of the samples labeled with cyanine and cyanine are sufficiently explored, and currently several approaches are practiced to minimize such effects. further, systematic variability like influence of the image scanner settings on the dye intensity measurements have now been robustly handled by applying various normalization techniques. despite these developments, some inherent genes–specific biases like differential hybridization efficiencies of the labeled target transcript to the same probe are still found to be inevitable in microarrays. in rna-seq as well as microarray, all these known and unknown parameters influence the final outcome. therefore, in this study, we focused on the assessment of rna-seq and microarray based on the final outcome.i.e. statistically significant differentially expressed genes. in comparison with previous rna-seq studies, with a sequence coverage of 97% we observed for our data set, is in consistence with the reported % to 95% coverage observed in other bacterial rna-seq studies. in our study, rna-seq has identified more significantly differentially expressed genes, when compared to microarray as in previous studies. the overall correlation in the magnitudes of fc for the consensus genes between the two methods was found to be similar or higher than previous studies. furthermore, our comparison analysis with qrt-pcr suggested that the expression levels were highly reliable for those genes that were determined to be differentially expressed by both rna-seq and microarray. hence, confirming the differential expression of genes by multiple methods reduces false positives thereby enhances the biological discovery. even though microarray overall outperformed rna-seq by detecting more known hrpx target genes from the t3ss in hrp cluster by satisfying both fc and fdr cut-off threshold, in principle rna-seq also detected genes hrpb hrcs, hpap, xac hrpb and hrct, in terms of fc, but failed to pass fdr threshold. this parameter is more directly influenced by error model considered in the statistical method that is used to infer the differential expression rather than rna-seq itself. for the same read counts, one can get slightly different fdr values depending on the statistical method. but the implementation of all the statistical methods is not feasible for every dataset. from the t3ss in hrp cluster, three genes namely, hrcc, hpa and hpaa were not found to be detected by both rna-seq and microarray, mainly because they fail to pass fdr threshold. interestingly, our previous microarray analysis confirmed that all these three genes are regulated by hrpx, but only at a later stage of the growth phase by satisfying both fc and fdr cut-off thresholds. this consolidates the regulation of some of the genes at later stages of the growth phase. further, in case of type iii effector genes, genes were not detected by both rna-seq and microarray within considered cut-off threshold limit. however, among them xopl, avrbs xopak and xopz were found to be regulated by hrpx only at the later stage of the growth phase, according to our previous microarray analysis. further, four genes namely, ptha ptha ptha ptha were regulated by another transcription regulator hrpg at early stage of growth phase as observed in our previous study, while another undetected gene xope was found to be also regulated by hrpg, but only at od = time point of growth phase. thereby this study further validated our previous results. subsequently, both methods detected 100% of the genes known to be regulated by hrpx without any false positives. among them, 72% were detected by both the methods while interestingly 28% of the known target genes were detected by either one of the methods. hence, both the methods together could complement each other. in addition genes were newly identified as differentially expressed by applying both microarray as well as rna-seq methods, thereby adding up to the already existing repertoire of hrpx regulated genes. furthermore, genes among them were uniquely identified by either one of the methods. overall, newly identified genes were found to have pip box in their promoter regions, wherein genes were uniquely identified by either rna-seq or microarray. the presence of the pip box in the promoter regions of the hrpx-regulated genes uniquely identified by rna-seq and microarray further not only confirmed that these genes are directly regulated by hrpx, but also that these candidates are not false positives. consequently, 100% of the known hrpx regulated genes could only be detected together by both the methods, since each method missed out on some of the known genes; hence both the methods together enhance the understanding of hrpx regulome by providing a more comprehensive picture.  this study has significantly advanced our understanding of the regulome of the critical transcriptional factor hrpx and demonstrates that rna-seq and microarray complement each other in transcriptome profiling. consequently, our study demonstrates the advantage of applying multiple transcriptome profiling methods to reveal a more comprehensive picture of a transcriptome, rather than relying solely on one method. 
rna-seq and microarray are the two popular methods employed for genome-wide transcriptome profiling. our comparative analysis indicated that gene expression levels quantified by rna-seq and microarray well-correlated both at absolute as well as relative levels. further, the expression levels quantified by rna-seq and microarray for the significantly differentially expressed genes also well-correlated with qrt-pcr based quantification. we compared the performance of rna-seq and microarray in their ability to detect known hrpx target genes by profiling the transcriptome from the wild-type and the hrpx mutant strains of γ-proteobacterium xanthomonas citri subsp. finally, in addition to the newly identified degs, 72% of the already known hrpx target genes were detected by both rna-seq and microarray, while, the remaining 28% could only be detected by either one of the methods. our study demonstrated that rna-seq and microarray complement each other in transcriptome profiling. in this regard, hrpx regulome from pathogenic bacteria serves as an ideal choice as the target genes of hrpx transcription factor are well studied due to their central role in pathogenicity. rna-seq and microarray together provide a more comprehensive picture of hrpx regulome by uniquely identifying new degs. this study has significantly advanced our understanding of the regulome of the critical transcriptional factor hrpx.
 rna-seq and microarray are the two popular methods employed for genome-wide transcriptome profiling. current comparison studies have shown that transcriptome quantified by these two methods correlated well. however, none of them have addressed if they complement each other, considering the strengths and the limitations inherent with them. the pivotal requirement to address this question is the knowledge of a well known data set. in this regard, hrpx regulome from pathogenic bacteria serves as an ideal choice as the target genes of hrpx transcription factor are well studied due to their central role in pathogenicity.  we compared the performance of rna-seq and microarray in their ability to detect known hrpx target genes by profiling the transcriptome from the wild-type and the hrpx mutant strains of γ-proteobacterium xanthomonas citri subsp. citri. our comparative analysis indicated that gene expression levels quantified by rna-seq and microarray well-correlated both at absolute as well as relative levels. further, the expression levels quantified by rna-seq and microarray for the significantly differentially expressed genes also well-correlated with qrt-pcr based quantification. finally, in addition to the newly identified degs, 72% of the already known hrpx target genes were detected by both rna-seq and microarray, while, the remaining 28% could only be detected by either one of the methods.  this study has significantly advanced our understanding of the regulome of the critical transcriptional factor hrpx. rna-seq and microarray together provide a more comprehensive picture of hrpx regulome by uniquely identifying new degs. our study demonstrated that rna-seq and microarray complement each other in transcriptome profiling. rna-seqmicroarraytranscriptome profilingpathogenic bacteriavirulencetype secretion systemeffectorshrpxxanthomonascitrus canker disease 