Transcriptome Analysis
Transcriptome Analysis - Related Videos
07:27
Transcriptome Analysis of Single Cells
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Cited by 72 •
2011
In this article we describe a simple method for the harvesting of single cells from rat primary neuronal cultures and subsequent transcriptome analysis using aRNA amplification. This approach is generalizable to any cell type.
10:32
Transcriptomic Analysis of Human Retinal Surgical Specimens Using jouRNAl
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Cited by 6 •
2013
We used retinal samples from retinectomy for a transcriptomic analysis of retinal detachment. We developed a procedure that allows RNA conservation between the surgical blocks and the laboratory. We standardized a protocol to purify RNA by cesium chloride ultracentrifugation to assure that the purified RNAs are suitable for microarray analysis.
02:52
Inducing the Prophage Lytic Replication for Transcriptomic Analysis
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2026
Source: Krishnamurthi, R., et al. Understanding the Impact of Temperate Bacteriophages on Their Lysogens Through Transcriptomics. J. Vis. Exp. (2024)This video demonstrates the induction of lytic replication in a prophage integrated into the bacterial genome and the capture of gene expression changes through timed sampling for transcriptomic analysis.
18:30
RNA-seq Analysis of Transcriptomes in Thrombin-treated and Control Human Pulmonary Microvascular Endothelial Cells
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Cited by 12 •
2013
This protocol presents a complete and detailed procedure to apply RNA-seq, a powerful next-generation DNA sequencing technology, to profile transcriptomes in human pulmonary microvascular endothelial cells with or without thrombin treatment. This protocol is generalizable to various cells or tissues affected by different reagents or disease states.
10:19
Transcriptomic Analysis of C. elegans RNA Sequencing Data Through the Tuxedo Suite on the Galaxy Project
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Cited by 8 •
2017
Galaxy and DAVID have emerged as popular tools that allow investigators without bioinformatics training to analyze and interpret RNA-Seq data. We describe a protocol for C. elegans researchers to perform RNA-Seq experiments, access and process the dataset using Galaxy and obtain meaningful biological information from the gene lists using DAVID.
FAQs
By comparing transcript abundance across samples, transcriptome analysis identifies genes whose activity differs under the tested conditions. Differential expression analysis organizes these changes into measurable patterns rather than treating every detected RNA molecule as equally informative. In genetics, these expression shifts can indicate how cells respond to developmental, physiological, or disease-associated contexts.
It shows whether the RNA output associated with a gene appears in more than one transcript form. Because the workflow can identify these alternative splice forms alongside transcript abundance, researchers can compare not only how much RNA is present, but also which transcript versions occur in different samples. This adds detail to genetic interpretation.
The transcriptome provides an intermediate view between DNA and observable traits because expression patterns can reflect the effects of DNA variation and regulatory mechanisms on cellular activity. Comparing those patterns across relevant samples helps relate genetic differences to traits, developmental states, or disease-associated changes. This connection can also help prioritize genes for later functional experiments.
Pathway analysis places genes showing expression changes into broader biological groupings, helping researchers interpret a list of differential signals. It can support characterization of cellular states and prioritize candidate genes instead of examining each transcript in isolation. The resulting interpretation can guide functional experiments designed to test which genes or processes contribute to the observed pattern.
A typical workflow begins with RNA extraction from the selected cell, tissue, or organismal sample. Researchers then convert the RNA into complementary DNA before applying high-throughput sequencing. The resulting sequence data support transcript quantification, alternative splice-form identification, and comparisons of expression patterns across samples. Keeping the sample conditions defined is essential to interpreting those comparisons.
Defined experimental conditions establish the context in which RNA production is measured, while comparisons across samples reveal whether expression patterns differ. This design allows researchers to associate transcript changes with a particular cellular, developmental, or disease-related context rather than viewing them as universal properties. Clear comparisons therefore strengthen the biological meaning of differential expression results.
In genetics, results can be used to prioritize candidate genes that may underlie observable traits or disease-associated changes, characterize cellular states, and select targets for functional experiments. The approach therefore serves as a bridge from expression patterns to testable genetic hypotheses, especially when researchers need to connect regulatory activity with development or disease.