Comparing Bioinformatic Gene Expression Profiling Methods: Microarray and RNA-Seq

Mantione, Kirk J.; Kream, Richard M.; Kuželová, H.; Ptáček, Radek; Raboch, J; Samuel, Joshua; Stefano, George B.

doi:10.12659/msmbr.892101

Cited by 209 publications

(110 citation statements)

References 24 publications

(27 reference statements)

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“…There was a highly significant overlap between this meta-analysis and the results of de Magalhães, et al (2009) (Figure 1) for both over-and underexpressed genes, which was an expected result as this analysis includes almost all the datasets used in the previous study. This overlap, although significant, is perhaps not as extensive as would have been expected, potentially due to the differing biases in microarray and RNA-Seq results (Mantione et al, 2014), which, given the presence of a large number of RNA-Seq datasets in this new analysis could result in different but similar genes being identified. Almost all the overexpressed genes from the 2009 study that were not detected by this study are immune or stress response genes.…”

Section: Discussionmentioning

confidence: 86%

Ageing Transcriptome Meta-Analysis Reveals Similarities Between Key Mammalian Tissues

Palmer

Fabris

Doherty

et al. 2019

Preprint

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Understanding the expression changes that come with age is an important step in understanding the ageing process as a whole. By combining such transcriptomic data with other sources of information, for instance protein-protein interaction (PPI) data, it is possible to make inferences about the functional changes that occur with age. To address this, we conducted a meta-analysis on 127 publicly available microarray and RNA-Seq datasets from mice, rats and humans, to identify genes that are commonly differentially expressed with age in mammals. We also conducted analyses on subsets of these datasets, to produce transcriptomic signatures for brain, heart and muscle tissues, all of which are important tissues in the pathophysiology of ageing. This approach identified the transcriptomic signatures of the ageing system, as well as brain, heart and muscle tissues. We then applied enrichment analysis and machine learning to functionally describe those signatures. This revealed a typical ageing signature including the overexpression of immune and stress response genes and the underexpression of metabolic and developmental genes. Further analysis of the ageing expression signatures revealed that genes differentially expressed with age tend to be broadly expressed across tissues, rather than be tissue-specific, and that the ageing expression signatures (particularly the overexpressed signatures) of the whole system, brain and muscle tend to include genes that are central in PPI networks. We also show that genes underexpressed in the brain are highly central in a co-expression map, suggesting that underexpression of these genes may play a part in cognitive ageing. In sum, we show numerous functional similarities between the ageing

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Section: Discussionmentioning

confidence: 86%

Ageing Transcriptome Meta-Analysis Reveals Similarities Between Key Mammalian Tissues

Palmer

Fabris

Doherty

et al. 2019

Preprint

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“…In order to help better identify more specific tendon markers, gene expression profiling techniques such as microarray analysis have been used, and identified TNMD and thrombospondin 4 ( THBS4 ) as possible tendon-selective gene markers [23]. Recent developments in next generation sequencing now offer the possibility to profile the entire transcriptome in a very high-throughput, accurate and quantitative manner [24]. This technique has been used in equine musculoskeletal tissue to determine transcriptomic signatures associated with ageing in cartilage [25].…”

Section: Introductionmentioning

confidence: 99%

Identification of Novel Equine (Equus caballus) Tendon Markers Using RNA Sequencing

Kuemmerle

Theiss

Okoniewski

et al. 2016

Genes

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Although several tendon-selective genes exist, they are also expressed in other musculoskeletal tissues. As cell and tissue engineering is reliant on specific molecular markers to discriminate between cell types, tendon-specific genes need to be identified. In order to accomplish this, we have used RNA sequencing (RNA-seq) to compare gene expression between tendon, bone, cartilage and ligament from horses. We identified several tendon-selective gene markers, and established eyes absent homolog 2 (EYA2) and a G-protein regulated inducer of neurite outgrowth 3 (GPRIN3) as specific tendon markers using RT-qPCR. Equine tendon cells cultured as three-dimensional spheroids expressed significantly greater levels of EYA2 than GPRIN3, and stained positively for EYA2 using immunohistochemistry. EYA2 was also found in fibroblast-like cells within the tendon tissue matrix and in cells localized to the vascular endothelium. In summary, we have identified EYA2 and GPRIN3 as specific molecular markers of equine tendon as compared to bone, cartilage and ligament, and provide evidence for the use of EYA2 as an additional marker for tendon cells in vitro.

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“…We are aware that newer, increasingly recognized methods are now commonly used instead of RNA microarray, such as RNA sequencing. RNA sequencing has a higher signal-to-noise ratio than RNA microarray because it allows for the DNA to bind to specific regions of the genome, and does not have problems with cross-hybridization or non-ideal hybridization kinetics that are seen with RNA microarray [75, 76]. As described in details in the Materials and methods section, to accommodate for this disadvantage, the data were subjected to a procedure to minimize the background noise, as described by Irizarry et al .…”

Section: Discussionmentioning

confidence: 99%

Gene expression profiling of the dorsolateral and medial orbitofrontal cortex in schizophrenia

Mladinov

Sedmak

Fuller

et al. 2016

Translational Neuroscience

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Schizophrenia is a complex polygenic disorder of unknown etiology. Over 3,000 candidate genes associated with schizophrenia have been reported, most of which being mentioned only once. Alterations in cognitive processing - working memory, metacognition and mentalization - represent a core feature of schizophrenia, which indicates the involvement of the prefrontal cortex in the pathophysiology of this disorder. Hence we compared the gene expression in postmortem tissue from the left and right dorsolateral prefrontal cortex (DLPFC, Brodmann's area 46), and the medial part of the orbitofrontal cortex (MOFC, Brodmann's area 11/12), in six patients with schizophrenia and six control brains. Although in the past decade several studies performed transcriptome profiling in schizophrenia, this is the first study to investigate both hemispheres, providing new knowledge about possible brain asymmetry at the level of gene expression and its relation to schizophrenia. We found that in the left hemisphere, twelve genes from the DLPFC and eight genes from the MOFC were differentially expressed in patients with schizophrenia compared to controls. In the right hemisphere there was only one gene differentially expressed in the MOFC. We reproduce the involvement of previously reported genes TARDBP and HNRNPC in the pathogenesis of schizophrenia, and report seven novel genes: SART1, KAT7, C1D, NPM1, EVI2A, XGY2, and TTTY15. As the differentially expressed genes only partially overlap with previous studies that analyzed other brain regions, our findings indicate the importance of considering prefrontal cortical regions, especially those in the left hemisphere, for obtaining disease-relevant insights.

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Comparing Bioinformatic Gene Expression Profiling Methods: Microarray and RNA-Seq

Cited by 209 publications

References 24 publications

Ageing Transcriptome Meta-Analysis Reveals Similarities Between Key Mammalian Tissues

Ageing Transcriptome Meta-Analysis Reveals Similarities Between Key Mammalian Tissues

Identification of Novel Equine (Equus caballus) Tendon Markers Using RNA Sequencing

Gene expression profiling of the dorsolateral and medial orbitofrontal cortex in schizophrenia

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