ArticlePloS one2012
RNA-Seq vs dual- and single-channel microarray data: sensitivity analysis for differential expression and clustering.
Article in PloS one, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers, 1 of them a synthesis that pooled it.
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Who cites it
44 citing papers in PubMed, 1 synthesis or guideline pooled it.
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- Cis and trans acting factors involved in human cytomegalovirus experimental and natural latent infection of CD14 (+) monocytes and CD34 (+) cells.PLoS pathogens · 2013Trial
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- Chronology of transcriptome and proteome expression during early Arabidopsis flower development.Journal of experimental botany · 2025Article
- BADDADAN: Mechanistic modelling of time-series gene module expression.Quantitative plant biology · 2025Article
- Transcriptome analysis reveals molecular targets of erythrocyte invasion phenotype diversity in naturalFrontiers in parasitology · 2024Article
- Can biomarkers identified from the uterine fluid transcriptome be used to establish a noninvasive endometrial receptivity prediction tool? A proof-of-concept study.Reproductive biology and endocrinology : RB&E · 2023Article
- Facilitating systems-level analyses of all-cause and Covid-mediated sepsis through SeptiSearch, a manually-curated compendium of dysregulated gene sets.Frontiers in immunology · 2023Article
- Status of diagnosis and treatment of esophageal cancer and non-coding RNA correlation research: a narrative review.Translational cancer research · 2021Review
- Advanced Genomics-Based Approaches for Defining Allograft Rejection With Single Cell Resolution.Frontiers in immunology · 2021Review
- RNA-seq analysis reveals divergent adaptive response to hyper- and hypo-salinity in cobia, Rachycentron canadum.Fish physiology and biochemistry · 2020Article
- Temporal changes in the spinal cord transcriptome after peripheral nerve injury.Neural regeneration research · 2020Article
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- Semantic Multi-Classifier Systems Identify Predictive Processes in Heart Failure Models across Species.Biomolecules · 2018Article
- Population genomics of parallel evolution in gene expression and gene sequence during ecological adaptation.Scientific reports · 2018Article
- DART: a fast and accurate RNA-seq mapper with a partitioning strategy.Bioinformatics (Oxford, England) · 2018Article
- Article
- Advantages of RNA-seq compared to RNA microarrays for transcriptome profiling of anterior cruciate ligament tears.Journal of orthopaedic research : official publication of the Orthopaedic Research Society · 2018Article
- The Whole Transcriptome Involved in Denervated Muscle Atrophy Following Peripheral Nerve Injury.Frontiers in molecular neuroscience · 2018Article
- Distinct transcriptional responses of lymphatic endothelial cells to VEGFR-3 and VEGFR-2 stimulation.Scientific data · 2017Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
With the fast development of high-throughput sequencing technologies, a new generation of genome-wide gene expression measurements is under way. This is based on mRNA sequencing (RNA-seq), which complements the already mature technology of microarrays, and is expected to overcome some of the latter's disadvantages. These RNA-seq data pose new challenges, however, as strengths and weaknesses have yet to be fully identified. Ideally, Next (or Second) Generation Sequencing measures can be integrated for more comprehensive gene expression investigation to facilitate analysis of whole regulatory networks. At present, however, the nature of these data is not very well understood. In this paper we study three alternative gene expression time series datasets for the Drosophila melanogaster embryo development, in order to compare three measurement techniques: RNA-seq, single-channel and dual-channel microarrays. The aim is to study the state of the art for the three technologies, with a view of assessing overlapping features, data compatibility and integration potential, in the context of time series measurements. This involves using established tools for each of the three different technologies, and technical and biological replicates (for RNA-seq and microarrays, respectively), due to the limited availability of biological RNA-seq replicates for time series data. The approach consists of a sensitivity analysis for differential expression and clustering. In general, the RNA-seq dataset displayed highest sensitivity to differential expression. The single-channel data performed similarly for the differentially expressed genes common to gene sets considered. Cluster analysis was used to identify different features of the gene space for the three datasets, with higher similarities found for the RNA-seq and single-channel microarray dataset.
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