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RNA-seq and microarray complement each other in transcriptome profiling.

ABSTRACT: 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 ?-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, rs > 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 (rs?=?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.

SUBMITTER: Kogenaru S

PROVIDER: S-EPMC3534599 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature

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RNA-seq and microarray complement each other in transcriptome profiling.

Kogenaru Sunitha S Qing Yan Y Guo Yinping Y Wang Nian N

BMC genomics 20121115

<h4>Background</h4>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 ba ...[more]

PMID: 23153100

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Project description:BackgroundNowadays, many public repositories containing large microarray gene expression datasets are available. However, the problem lies in the fact that microarray technology are less powerful and accurate than more recent Next Generation Sequencing technologies, such as RNA-Seq. In any case, information from microarrays is truthful and robust, thus it can be exploited through the integration of microarray data with RNA-Seq data. Additionally, information extraction and acquisition of large number of samples in RNA-Seq still entails very high costs in terms of time and computational resources.This paper proposes a new model to find the gene signature of breast cancer cell lines through the integration of heterogeneous data from different breast cancer datasets, obtained from microarray and RNA-Seq technologies. Consequently, data integration is expected to provide a more robust statistical significance to the results obtained. Finally, a classification method is proposed in order to test the robustness of the Differentially Expressed Genes when unseen data is presented for diagnosis.ResultsThe proposed data integration allows analyzing gene expression samples coming from different technologies. The most significant genes of the whole integrated data were obtained through the intersection of the three gene sets, corresponding to the identified expressed genes within the microarray data itself, within the RNA-Seq data itself, and within the integrated data from both technologies. This intersection reveals 98 possible technology-independent biomarkers. Two different heterogeneous datasets were distinguished for the classification tasks: a training dataset for gene expression identification and classifier validation, and a test dataset with unseen data for testing the classifier. Both of them achieved great classification accuracies, therefore confirming the validity of the obtained set of genes as possible biomarkers for breast cancer. Through a feature selection process, a final small subset made up by six genes was considered for breast cancer diagnosis.ConclusionsThis work proposes a novel data integration stage in the traditional gene expression analysis pipeline through the combination of heterogeneous data from microarrays and RNA-Seq technologies. Available samples have been successfully classified using a subset of six genes obtained by a feature selection method. Consequently, a new classification and diagnosis tool was built and its performance was validated using previously unseen samples.

Project description:The molecular mechanisms underlying neuronal death are poorly understood. One of the most widely used models to study neuronal death are cultured cerebellar granule neurons (CGNs) which undergo apoptosis when switched from a medium containing depolarizing levels of potassium (HK) to a medium with low non-depolarizing levels of potassium (LK). Previously, other labs have used DNA microarray analysis to characterize gene expression changes in LK-treated CGNs. However, microarray analysis is only capable of measuring the status of known transcripts, and expression of low-abundance mRNAs is often not detected by the hybridization-based approach. We have used RNA-sequencing to conduct a more detailed and comprehensive analysis of gene expression changes in CGNs induced to die by LK treatment. RNA-seq investigates the status of both known transcripts as well as exploring new ones and is substantially more sensitive than the microarray approach. We have found that the expression of 4334 genes is significantly altered in LK-treated CGNs with 2199 being up-regulated while 2135 are down-regulated. Genes functioning in cell death and survival regulation, cell growth and proliferation and molecular transport were most affected by LK treatment. Further, a large number of genes involved in nervous system development and function were also deregulated. Analysis of signaling pathways that were affected in LK-induced death included but were not limited to mitochondrial dysfunction and oxidative phosphorylation, consistent with a number of studies showing perturbations of these pathways in neurodegenerative disorders. Thus, our study identifies a large number of new genes that are affected during the process of neuronal death. While a majority of these changes may reflect consequences of the induction of neuronal death, many of the genes that we have identified are likely to be critical and potentially novel mediators of neuronal death, including death associated with neurodegenerative disease.

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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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