A comparison of global gene expression measurement technologies in <i>Arabidopsis thaliana</i>

Coughlan, Sean; Agrawal, Vikas

doi:10.1002/cfg.397

Cited by 22 publications

(20 citation statements)

References 21 publications

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“…Previous evaluations of expression analysis platforms all rely on measurements from biological samples compared with data obtained by another expression measurement technology (Coughlan et al 2004;Chen et al 2007;Liu et al 2007;Arikawa et al 2008;Marioni et al 2008;9t Hoen et al 2008). In contrast to this, our experimental approach, using synthetic RNAs with known sequences and concentrations, produces a wellcharacterized input as the basis for estimates of each platform's performance (summarized in Table 2).…”

Section: Discussionmentioning

confidence: 99%

“…Such studies compare microarray data from biological samples to Illumina sequencing data (Marioni et al 2008;9t Hoen et al 2008) or to massively parallel signature sequencing (MPSS) data (Coughlan et al 2004;Chen et al 2007;Liu et al 2007). However, most high-throughput sequencing methods rely on a polymerase chain reaction (PCR) based sample amplification step, in which bias may be introduced.…”

Section: Introductionmentioning

confidence: 99%

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Quantitative miRNA expression analysis: Comparing microarrays with next-generation sequencing

Willenbrock¹,

Salomon²,

Søkilde³

et al. 2009

RNA

145

121

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Recently, next-generation sequencing has been introduced as a promising, new platform for assessing the copy number of transcripts, while the existing microarray technology is considered less reliable for absolute, quantitative expression measurements. Nonetheless, so far, results from the two technologies have only been compared based on biological data, leading to the conclusion that, although they are somewhat correlated, expression values differ significantly. Here, we use synthetic RNA samples, resembling human microRNA samples, to find that microarray expression measures actually correlate better with sample RNA content than expression measures obtained from sequencing data. In addition, microarrays appear highly sensitive and perform equivalently to next-generation sequencing in terms of reproducibility and relative ratio quantification.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Quantitative miRNA expression analysis: Comparing microarrays with next-generation sequencing

Willenbrock¹,

Salomon²,

Søkilde³

et al. 2009

RNA

145

121

View full text Add to dashboard Cite

show abstract

“…Also, Dabrowski et al (2003) found a high level of agreement between data obtained from cDNA arrays and a similar but independent study using mouse Affymetrix GeneChips. Moreover, microarray results have been found to agree well with direct measurements of gene expression such as serial analysis of gene expression (SAGE; Ishii et al, 2000) or massively parallel signature sequencing (MPSS; Coughlan et al, 2004).…”

Section: Introductionmentioning

confidence: 91%

Comparison of Transcript Profiling on Arabidopsis Microarray Platform Technologies

Pylatuik

Fobert

2005

Plant Mol Biol

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To date there have been few systematic studies comparing the results of transcript profiling from different microarray platform technologies. We evaluated in detail two different Arabidopsis thaliana microarray platforms: our own Genomic Amplicon arrays and the Qiagen long oligonucleotide arrays designed by Operon; furthermore, we cross-validated these arrays against the Affymetrix AG and ATH1 GeneChips. Data were obtained from all three platforms in each of two separate experiments; (1) at 2 h and (2) 8 h following a salicylic acid treatment applied to both wild-type and npr1-3 mutant plants. A total of 20 hybridizations were performed, analyzing the expression of 26,814 unique locus IDs. We demonstrate that intensity rank is a key variable that affects both inter-platform and cross-platform reproducibility. Although general agreement between platform technologies is low, data derived from high signal intensities (90th percentile) can correlate as well between differing platforms as replicates within the same platform (r=0.4-0.7). We also show that the identification of differentially expressed genes by significance analysis of microarrays is influenced by signal intensity and that overlap between significant gene lists from different platform technologies was as high as 67% when low intensity values were removed. Validation of 41 genes by Northern blot hybridization showed that all platform technologies performed well, qualitatively confirming 83-100% of differential gene expression. Our results suggest that the potential for the broad integration of microarray data from different platforms and laboratories is promising.

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“…Coughlan et al (2004) compared the proWling results from both microarray and massively parallel signature sequencing (MPSS, Meyers et al 2004) using the same RNA samples of Arabidopsis. A moderate and low correlation among the two data types was observed when the abundance of the MPSS tags and microarray signal intensity were compared.…”

Section: Discussionmentioning

confidence: 99%

RL-SAGE and microarray analysis of the rice transcriptome after Rhizoctonia solani infection

et al. 2007

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Sheath blight caused by the fungal pathogen Rhizoctonia solani is an emerging problem in rice production worldwide. To elucidate the molecular basis of rice defense to the pathogen, RNA isolated from R. solani-infected leaves of Jasmine 85 was used for both RL-SAGE library construction and microarray hybridization. RL-SAGE sequence analysis identified 20,233 and 24,049 distinct tags from the control and inoculated libraries, respectively. Nearly half of the significant tags (> or =2 copies) from both libraries matched TIGR annotated genes and KOME full-length cDNAs. Among them, 42% represented sense and 7% antisense transcripts, respectively. Interestingly, 60% of the library-specific (> or =10 copies) and differentially expressed (>4.0-fold change) tags were novel transcripts matching genomic sequence but not annotated genes. About 70% of the genes identified in the SAGE libraries showed similar expression patterns (up or down-regulated) in the microarray data obtained from three biological replications. Some candidate RL-SAGE tags and microarray genes were located in known sheath blight QTL regions. The expression of ten differentially expressed RL-SAGE tags was confirmed with RT-PCR. The defense genes associated with resistance to R. solani identified in this study are useful genomic materials for further elucidation of the molecular basis of the defense response to R. solani and fine mapping of target sheath blight QTLs.

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A comparison of global gene expression measurement technologies in Arabidopsis thaliana

Cited by 22 publications

References 21 publications

Quantitative miRNA expression analysis: Comparing microarrays with next-generation sequencing

Quantitative miRNA expression analysis: Comparing microarrays with next-generation sequencing

Comparison of Transcript Profiling on Arabidopsis Microarray Platform Technologies

RL-SAGE and microarray analysis of the rice transcriptome after Rhizoctonia solani infection

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