RNA-seq and microarray complement each other in transcriptome profiling

<p>Abstract</p> <p>Background</p> <p>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...

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Main Authors: Kogenaru Sunitha, Qing Yan, Guo Yinping, Wang Nian
Format: Article
Language:English
Published: BMC 2012-11-01
Series:BMC Genomics
Subjects:
Online Access:http://www.biomedcentral.com/1471-2164/13/629
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spelling doaj-832e44d3d687400ca76d0a62fbae6aac2020-11-24T21:53:01ZengBMCBMC Genomics1471-21642012-11-0113162910.1186/1471-2164-13-629RNA-seq and microarray complement each other in transcriptome profilingKogenaru SunithaQing YanGuo YinpingWang Nian<p>Abstract</p> <p>Background</p> <p>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.</p> <p>Results</p> <p>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 <it>hrpX</it> mutant strains of γ-Proteobacterium <it>Xanthomonas citri</it> subsp. <it>citri</it>. Our comparative analysis indicated that gene expression levels quantified by RNA-seq and microarray well-correlated both at absolute as well as relative levels (Spearman correlation-coefficient, r<sub>s</sub> > 0.76). Further, the expression levels quantified by RNA-seq and microarray for the significantly differentially expressed genes (DEGs) also well-correlated with qRT-PCR based quantification (r<sub>s</sub> = 0.58 to 0.94). Finally, in addition to the 55 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.</p> <p>Conclusions</p> <p>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.</p> http://www.biomedcentral.com/1471-2164/13/629RNA-seqMicroarrayTranscriptome profilingPathogenic bacteriaVirulenceType 3 secretion systemEffectorsHrpXXanthomonasCitrus canker disease
collection DOAJ
language English
format Article
sources DOAJ
author Kogenaru Sunitha
Qing Yan
Guo Yinping
Wang Nian
spellingShingle Kogenaru Sunitha
Qing Yan
Guo Yinping
Wang Nian
RNA-seq and microarray complement each other in transcriptome profiling
BMC Genomics
RNA-seq
Microarray
Transcriptome profiling
Pathogenic bacteria
Virulence
Type 3 secretion system
Effectors
HrpX
Xanthomonas
Citrus canker disease
author_facet Kogenaru Sunitha
Qing Yan
Guo Yinping
Wang Nian
author_sort Kogenaru Sunitha
title RNA-seq and microarray complement each other in transcriptome profiling
title_short RNA-seq and microarray complement each other in transcriptome profiling
title_full RNA-seq and microarray complement each other in transcriptome profiling
title_fullStr RNA-seq and microarray complement each other in transcriptome profiling
title_full_unstemmed RNA-seq and microarray complement each other in transcriptome profiling
title_sort rna-seq and microarray complement each other in transcriptome profiling
publisher BMC
series BMC Genomics
issn 1471-2164
publishDate 2012-11-01
description <p>Abstract</p> <p>Background</p> <p>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.</p> <p>Results</p> <p>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 <it>hrpX</it> mutant strains of γ-Proteobacterium <it>Xanthomonas citri</it> subsp. <it>citri</it>. Our comparative analysis indicated that gene expression levels quantified by RNA-seq and microarray well-correlated both at absolute as well as relative levels (Spearman correlation-coefficient, r<sub>s</sub> > 0.76). Further, the expression levels quantified by RNA-seq and microarray for the significantly differentially expressed genes (DEGs) also well-correlated with qRT-PCR based quantification (r<sub>s</sub> = 0.58 to 0.94). Finally, in addition to the 55 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.</p> <p>Conclusions</p> <p>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.</p>
topic RNA-seq
Microarray
Transcriptome profiling
Pathogenic bacteria
Virulence
Type 3 secretion system
Effectors
HrpX
Xanthomonas
Citrus canker disease
url http://www.biomedcentral.com/1471-2164/13/629
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