Dataset Information

Quantitative miRNA expression analysis: comparing microarrays with next-generation sequencing.

ABSTRACT: 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.

SUBMITTER: Willenbrock H

PROVIDER: S-EPMC2764476 | biostudies-literature | 2009 Nov

REPOSITORIES: biostudies-literature

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Quantitative miRNA expression analysis: comparing microarrays with next-generation sequencing.

Willenbrock Hanni H Salomon Jesper J Søkilde Rolf R Barken Kim Bundvig KB Hansen Thomas Nøhr TN Nielsen Finn Cilius FC Møller Søren S Litman Thomas T

RNA (New York, N.Y.) 20090910 11

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 samp ...[more]

PMID: 19745027

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Project description:Emerging evidence has demonstrated that miRNA sequences can regulate skeletal myogenesis by controlling the process of myoblast proliferation and differentiation. However, at present a deep analysis of miRNA expression in control and FSHD myoblasts during differentiation has not yet been derived. To close this gap, we used a next-generation sequencing (NGS) approach applied to in vitro myogenesis. Furthermore, to minimize sample genetic heterogeneity and muscle-type specific patterns of gene expression, miRNA profiling from NGS data was filtered with FC ≥ 4 (log(2)FC ≥ 2) and p-value<0.05, and its validation was derived by qRT-PCR on myoblasts from seven muscle districts. In particular, control myogenesis showed the modulation of 38 miRNAs, the majority of which (34 out 38) were up-regulated, including myomiRs (miR-1, -133a, -133b and -206). Approximately one third of the modulated miRNAs were not previously reported to be involved in muscle differentiation, and interestingly some of these (i.e. miR-874, -1290, -95 and -146a) were previously shown to regulate cell proliferation and differentiation. FSHD myogenesis evidenced a reduced number of modulated miRNAs than healthy muscle cells. The two processes shared nine miRNAs, including myomiRs, although with FC values lower in FSHD than in control cells. In addition, FSHD cells showed the modulation of six miRNAs (miR-1268, -1268b, -1908, 4258, -4508- and -4516) not evidenced in control cells and that therefore could be considered FSHD-specific, likewise three novel miRNAs that seem to be specifically expressed in FSHD myotubes. These data further clarify the impact of miRNA regulation during control myogenesis and strongly suggest that a complex dysregulation of miRNA expression characterizes FSHD, impairing two important features of myogenesis: cell cycle and muscle development. The derived miRNA profiling could represent a novel molecular signature for FSHD that includes diagnostic biomarkers and possibly therapeutic targets.

Project description:BackgroundReproducibility of results can have a significant impact on the acceptance of new technologies in gene expression analysis. With the recent introduction of the so-called next-generation sequencing (NGS) technology and established microarrays, one is able to choose between two completely different platforms for gene expression measurements. This study introduces a novel methodology for gene-ranking stability analysis that is applied to the evaluation of gene-ranking reproducibility on NGS and microarray data.ResultsThe same data used in a well-known MicroArray Quality Control (MAQC) study was also used in this study to compare ranked lists of genes from MAQC samples A and B, obtained from Affymetrix HG-U133 Plus 2.0 and Roche 454 Genome Sequencer FLX platforms. An initial evaluation, where the percentage of overlapping genes was observed, demonstrates higher reproducibility on microarray data in 10 out of 11 gene-ranking methods. A gene set enrichment analysis shows similar enrichment of top gene sets when NGS is compared with microarrays on a pathway level. Our novel approach demonstrates high accuracy of decision trees when used for knowledge extraction from multiple bootstrapped gene set enrichment analysis runs. A comparison of the two approaches in sample preparation for high-throughput sequencing shows that alternating decision trees represent the optimal knowledge representation method in comparison with classical decision trees.ConclusionsUsual reproducibility measurements are mostly based on statistical techniques that offer very limited biological insights into the studied gene expression data sets. This paper introduces the meta-learning-based gene set enrichment analysis that can be used to complement the analysis of gene-ranking stability estimation techniques such as percentage of overlapping genes or classic gene set enrichment analysis. It is useful and practical when reproducibility of gene ranking results or different gene selection techniques is observed. The proposed method reveals very accurate descriptive models that capture the co-enrichment of gene sets which are differently enriched in the compared data sets.

Project description:Kaposi's sarcoma associated herpesvirus (KSHV) causes several tumors, including primary effusion lymphoma (PEL) and Kaposi's sarcoma (KS). Cellular and viral microRNAs (miRNAs) have been shown to play important roles in regulating gene expression. A better knowledge of the miRNA-mediated pathways affected by KSHV infection is therefore important for understanding viral infection and tumor pathogenesis. In this study, we used deep sequencing to analyze miRNA and cellular mRNA expression in a cell line with latent KSHV infection (SLKK) as compared to the uninfected SLK line. This approach revealed 153 differentially expressed human miRNAs, eight of which were independently confirmed by qRT-PCR. KSHV infection led to the dysregulation of ~15% of the human miRNA pool and most of these cellular miRNAs were down-regulated, including nearly all members of the 14q32 miRNA cluster, a genomic locus linked to cancer and that is deleted in a number of PEL cell lines. Furthermore, we identified 48 miRNAs that were associated with a total of 1,117 predicted or experimentally validated target mRNAs; of these mRNAs, a majority (73%) were inversely correlated to expression changes of their respective miRNAs, suggesting miRNA-mediated silencing mechanisms were involved in a number of these alterations. Several dysregulated miRNA-mRNA pairs may facilitate KSHV infection or tumor formation, such as up-regulated miR-708-5p, associated with a decrease in pro-apoptotic caspase-2 and leukemia inhibitory factor LIF, or down-regulated miR-409-5p, associated with an increase in the p53-inhibitor MDM2. Transfection of miRNA mimics provided further evidence that changes in miRNAs are driving some observed mRNA changes. Using filtered datasets, we also identified several canonical pathways that were significantly enriched in differentially expressed miRNA-mRNA pairs, such as the epithelial-to-mesenchymal transition and the interleukin-8 signaling pathways. Overall, our data provide a more detailed understanding of KSHV latency and guide further studies of the biological significance of these changes.

Dataset Information

Quantitative miRNA expression analysis: comparing microarrays with next-generation sequencing.

Publications

Quantitative miRNA expression analysis: comparing microarrays with next-generation sequencing.

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