RNA-seq and microarray complement each other in transcriptome profiling
Background 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 th...
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| Autor*in: |
Kogenaru, Sunitha [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2012 |
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| Anmerkung: |
© Kogenaru et al.; licensee BioMed Central Ltd. 2012. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( |
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| Übergeordnetes Werk: |
Enthalten in: BMC genomics - London : BioMed Central, 2000, 13(2012), 1 vom: 15. Nov. |
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| Übergeordnetes Werk: |
volume:13 ; year:2012 ; number:1 ; day:15 ; month:11 |
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| DOI / URN: |
10.1186/1471-2164-13-629 |
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| Katalog-ID: |
SPR027072304 |
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| 520 | |a Background 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. Results 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 (Spearman correlation-coefficient, $ r_{s} $ > 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_{s} $ = 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. Conclusions 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. | ||
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| 700 | 1 | |a Guo, Yinping |4 aut | |
| 700 | 1 | |a Wang, Nian |4 aut | |
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10.1186/1471-2164-13-629 doi (DE-627)SPR027072304 (SPR)1471-2164-13-629-e DE-627 ger DE-627 rakwb eng Kogenaru, Sunitha verfasserin aut RNA-seq and microarray complement each other in transcriptome profiling 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Kogenaru et al.; licensee BioMed Central Ltd. 2012. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( Background 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. Results 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 (Spearman correlation-coefficient, $ r_{s} $ > 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_{s} $ = 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. Conclusions 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-seq (dpeaa)DE-He213 Microarray (dpeaa)DE-He213 Transcriptome profiling (dpeaa)DE-He213 Pathogenic bacteria (dpeaa)DE-He213 Virulence (dpeaa)DE-He213 Type 3 secretion system (dpeaa)DE-He213 Effectors (dpeaa)DE-He213 HrpX (dpeaa)DE-He213 Xanthomonas (dpeaa)DE-He213 Citrus canker disease (dpeaa)DE-He213 Yan, Qing aut Guo, Yinping aut Wang, Nian aut Enthalten in BMC genomics London : BioMed Central, 2000 13(2012), 1 vom: 15. Nov. (DE-627)326644954 (DE-600)2041499-7 1471-2164 nnns volume:13 year:2012 number:1 day:15 month:11 https://dx.doi.org/10.1186/1471-2164-13-629 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2012 1 15 11 |
| spelling |
10.1186/1471-2164-13-629 doi (DE-627)SPR027072304 (SPR)1471-2164-13-629-e DE-627 ger DE-627 rakwb eng Kogenaru, Sunitha verfasserin aut RNA-seq and microarray complement each other in transcriptome profiling 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Kogenaru et al.; licensee BioMed Central Ltd. 2012. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( Background 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. Results 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 (Spearman correlation-coefficient, $ r_{s} $ > 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_{s} $ = 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. Conclusions 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-seq (dpeaa)DE-He213 Microarray (dpeaa)DE-He213 Transcriptome profiling (dpeaa)DE-He213 Pathogenic bacteria (dpeaa)DE-He213 Virulence (dpeaa)DE-He213 Type 3 secretion system (dpeaa)DE-He213 Effectors (dpeaa)DE-He213 HrpX (dpeaa)DE-He213 Xanthomonas (dpeaa)DE-He213 Citrus canker disease (dpeaa)DE-He213 Yan, Qing aut Guo, Yinping aut Wang, Nian aut Enthalten in BMC genomics London : BioMed Central, 2000 13(2012), 1 vom: 15. Nov. (DE-627)326644954 (DE-600)2041499-7 1471-2164 nnns volume:13 year:2012 number:1 day:15 month:11 https://dx.doi.org/10.1186/1471-2164-13-629 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2012 1 15 11 |
| allfields_unstemmed |
10.1186/1471-2164-13-629 doi (DE-627)SPR027072304 (SPR)1471-2164-13-629-e DE-627 ger DE-627 rakwb eng Kogenaru, Sunitha verfasserin aut RNA-seq and microarray complement each other in transcriptome profiling 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Kogenaru et al.; licensee BioMed Central Ltd. 2012. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( Background 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. Results 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 (Spearman correlation-coefficient, $ r_{s} $ > 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_{s} $ = 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. Conclusions 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-seq (dpeaa)DE-He213 Microarray (dpeaa)DE-He213 Transcriptome profiling (dpeaa)DE-He213 Pathogenic bacteria (dpeaa)DE-He213 Virulence (dpeaa)DE-He213 Type 3 secretion system (dpeaa)DE-He213 Effectors (dpeaa)DE-He213 HrpX (dpeaa)DE-He213 Xanthomonas (dpeaa)DE-He213 Citrus canker disease (dpeaa)DE-He213 Yan, Qing aut Guo, Yinping aut Wang, Nian aut Enthalten in BMC genomics London : BioMed Central, 2000 13(2012), 1 vom: 15. Nov. (DE-627)326644954 (DE-600)2041499-7 1471-2164 nnns volume:13 year:2012 number:1 day:15 month:11 https://dx.doi.org/10.1186/1471-2164-13-629 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2012 1 15 11 |
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10.1186/1471-2164-13-629 doi (DE-627)SPR027072304 (SPR)1471-2164-13-629-e DE-627 ger DE-627 rakwb eng Kogenaru, Sunitha verfasserin aut RNA-seq and microarray complement each other in transcriptome profiling 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Kogenaru et al.; licensee BioMed Central Ltd. 2012. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( Background 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. Results 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 (Spearman correlation-coefficient, $ r_{s} $ > 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_{s} $ = 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. Conclusions 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-seq (dpeaa)DE-He213 Microarray (dpeaa)DE-He213 Transcriptome profiling (dpeaa)DE-He213 Pathogenic bacteria (dpeaa)DE-He213 Virulence (dpeaa)DE-He213 Type 3 secretion system (dpeaa)DE-He213 Effectors (dpeaa)DE-He213 HrpX (dpeaa)DE-He213 Xanthomonas (dpeaa)DE-He213 Citrus canker disease (dpeaa)DE-He213 Yan, Qing aut Guo, Yinping aut Wang, Nian aut Enthalten in BMC genomics London : BioMed Central, 2000 13(2012), 1 vom: 15. Nov. (DE-627)326644954 (DE-600)2041499-7 1471-2164 nnns volume:13 year:2012 number:1 day:15 month:11 https://dx.doi.org/10.1186/1471-2164-13-629 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2012 1 15 11 |
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10.1186/1471-2164-13-629 doi (DE-627)SPR027072304 (SPR)1471-2164-13-629-e DE-627 ger DE-627 rakwb eng Kogenaru, Sunitha verfasserin aut RNA-seq and microarray complement each other in transcriptome profiling 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Kogenaru et al.; licensee BioMed Central Ltd. 2012. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( Background 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. Results 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 (Spearman correlation-coefficient, $ r_{s} $ > 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_{s} $ = 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. Conclusions 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-seq (dpeaa)DE-He213 Microarray (dpeaa)DE-He213 Transcriptome profiling (dpeaa)DE-He213 Pathogenic bacteria (dpeaa)DE-He213 Virulence (dpeaa)DE-He213 Type 3 secretion system (dpeaa)DE-He213 Effectors (dpeaa)DE-He213 HrpX (dpeaa)DE-He213 Xanthomonas (dpeaa)DE-He213 Citrus canker disease (dpeaa)DE-He213 Yan, Qing aut Guo, Yinping aut Wang, Nian aut Enthalten in BMC genomics London : BioMed Central, 2000 13(2012), 1 vom: 15. Nov. (DE-627)326644954 (DE-600)2041499-7 1471-2164 nnns volume:13 year:2012 number:1 day:15 month:11 https://dx.doi.org/10.1186/1471-2164-13-629 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2012 1 15 11 |
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Kogenaru, Sunitha misc RNA-seq misc Microarray misc Transcriptome profiling misc Pathogenic bacteria misc Virulence misc Type 3 secretion system misc Effectors misc HrpX misc Xanthomonas misc Citrus canker disease RNA-seq and microarray complement each other in transcriptome profiling |
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RNA-seq and microarray complement each other in transcriptome profiling RNA-seq (dpeaa)DE-He213 Microarray (dpeaa)DE-He213 Transcriptome profiling (dpeaa)DE-He213 Pathogenic bacteria (dpeaa)DE-He213 Virulence (dpeaa)DE-He213 Type 3 secretion system (dpeaa)DE-He213 Effectors (dpeaa)DE-He213 HrpX (dpeaa)DE-He213 Xanthomonas (dpeaa)DE-He213 Citrus canker disease (dpeaa)DE-He213 |
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rna-seq and microarray complement each other in transcriptome profiling |
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RNA-seq and microarray complement each other in transcriptome profiling |
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Background 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. Results 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 (Spearman correlation-coefficient, $ r_{s} $ > 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_{s} $ = 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. Conclusions 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. © Kogenaru et al.; licensee BioMed Central Ltd. 2012. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( |
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Background 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. Results 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 (Spearman correlation-coefficient, $ r_{s} $ > 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_{s} $ = 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. Conclusions 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. © Kogenaru et al.; licensee BioMed Central Ltd. 2012. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( |
| abstract_unstemmed |
Background 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. Results 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 (Spearman correlation-coefficient, $ r_{s} $ > 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_{s} $ = 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. Conclusions 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. © Kogenaru et al.; licensee BioMed Central Ltd. 2012. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( |
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| score |
7.398591 |