Rna Analysis
Rna Analysis - Related Videos
12:04
RNA Analysis of Environmental Samples Using RT-PCR
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2023
Source: Laboratories of Dr. Ian Pepper and Dr. Charles Gerba - The University of Arizona Demonstrating Author: Bradley Schmitz Reverse transcription-polymerase chain reaction (RT-PCR) involves the same process as conventional PCR — cycling temperature to amplify nucleic acids. However, while conventional PCR only amplifies deoxyribonucleic acids (DNA), RT-PCR enables the amplification of ribonucleic acids (RNA) through the formation of complementary DNA (cDNA). This enables RNA-based organisms...
14:53
Analysis of Global RNA Synthesis at the Single Cell Level following Hypoxia
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Cited by 2 •
2014
We describe a technique for analysis of global RNA synthesis in hypoxia using imaging. Click-chemistry labeling of RNA has not previously been performed under hypoxia and allows visualization of global RNA changes at the single cell level. This approach complements the existing averaged RNA techniques, allowing direct visualization of cell-to-cell changes in global RNA synthesis.
07:42
Laser-capture Microdissection of Human Prostatic Epithelium for RNA Analysis
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Cited by 15 •
2015
The goal of this protocol is to use laser-capture micro-dissection as an effective method to isolate pure populations of cell types from heterogeneous prostate tissues for downstream RNA analysis.
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.
09:56
A Stepwise Guide to the Isolation and Analysis of Leaf Surface and Apoplastic RNA Using Arabidopsis Rosettes
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2025
We describe a simple yet highly reproducible method to isolate extracellular RNA from the leaf surface and apoplast of Arabidopsis plants.
FAQs
Reverse transcription converts RNA into complementary DNA, creating material that can then be quantified or sequenced. This step connects the original RNA population to downstream measurements, but it also makes the result dependent on how efficiently the conversion represents the starting sample. Researchers therefore interpret measurements alongside controls that help identify amplification bias and differences in sample input.
Controls help distinguish biological differences from technical variation. In particular, they can reveal RNA degradation, unequal sample input, and bias introduced during amplification. Accounting for these factors makes comparisons between samples more meaningful and reduces the risk of attributing a measurement difference to gene regulation when it may instead reflect handling or processing effects.
The desired information determines the analytical focus. Measurements of abundance address how much of an RNA is present, whereas sequencing examines sequence information and can help identify alternative transcripts. Structural or distributional questions require different measurements again. Matching the readout to the question prevents a study from drawing conclusions that its chosen analysis cannot support.
A typical workflow begins with RNA isolation, followed by an assessment of the information needed from the sample. Scientists may convert the RNA to complementary DNA through reverse transcription, then quantify or sequence the resulting material. Controls are incorporated to account for degradation, amplification bias, and unequal input before researchers interpret the measurements in their biological context.
RNA analysis can reveal whether cells produce different transcript forms or activate noncoding RNA molecules, extending interpretation beyond a single gene-expression value. Sequence-focused measurements help characterize transcript differences, while abundance measurements indicate how strongly particular RNA populations are represented. Together, these data support functional studies of how RNA contributes to cellular regulation.
Comparing RNA patterns across developmental states, disease conditions, or experimental treatments can show which transcriptional programs change with biological state. These results support transcriptomics, biomarker development, and functional investigations. Their value depends on controlling technical variation and interpreting altered RNA abundance or transcript identity as evidence about cellular regulation rather than as an isolated measurement.