Rna Sequencing
Rna Sequencing - Related Videos
09:36
Unbiased Deep Sequencing of RNA Viruses from Clinical Samples
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Cited by 50 •
2016
This protocol describes a rapid and broadly applicable method for unbiased RNA-sequencing of viral samples from human clinical isolates.
07:30
Optimization for Sequencing and Analysis of Degraded FFPE-RNA Samples
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Cited by 19 •
2020
This method describes the steps to improve the quality and quantity of sequence data that can be obtained from formalin-fixed paraffin-embedded (FFPE) RNA samples. We describe the methodology to more accurately assess the quality of FFPE-RNA samples, prepare sequencing libraries, and analyze the data from FFPE-RNA samples.
10:24
Next-generation Sequencing of 16S Ribosomal RNA Gene Amplicons
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Cited by 119 •
2014
Characterizing microbial community has been a longstanding goal in environmental microbiology. Next-generation sequencing methods now allow for the characterization of microbial communities at an unprecedented depth with minimal cost and labor. We detail here our approach to sequence bacterial 16S ribosomal RNA genes using a benchtop sequencer.
11:52
Targeted RNA Sequencing Assay to Characterize Gene Expression and Genomic Alterations
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Cited by 15 •
2016
We describe a targeted RNA sequencing-based method that includes preparation of indexed cDNA libraries, hybridization and capture with custom probes and data analysis to interrogate selected transcripts for gene expression, mutations, and gene fusions. Targeted RNAseq permits cost-effective, rapid evaluation of selected transcripts on a desktop sequencer.
03:26
Probe Hybridization and Signal Amplification in RNA In Situ Hybridization: A Technique for Detecting Specific RNA Sequences in Tissue Sections
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2023
This video describes a sequential hybridization strategy that involves hybridizing mRNA probes to specific target mRNA. Following probe hybridization, signal amplification is performed to enhance resolution via reduction of signal-to-noise ratio during RNA-CISH of histological samples.
FAQs
Converting RNA into complementary DNA, or cDNA, creates a form that can be prepared as a sequencing library and read by high-throughput sequencing. This step connects the original RNA population to downstream measurement of transcript abundance, splice variants, and expressed mutations. The resulting data therefore reflect the RNA molecules present while using the sequencing workflow described for cDNA.
Fragmentation or other library-preparation steps organize the cDNA-derived material into a form suitable for sequencing. The resulting collection of library fragments provides the reads used for computational analysis. Those reads can support transcript quantification and transcript reconstruction, allowing the experiment to examine both overall expression patterns and differences in RNA processing, such as splice variants.
Alignment places sequencing reads in relation to known sequence information, whereas assembly reconstructs transcript information from the reads. Together, these computational approaches support quantification of transcripts and detection of splice variants. Their outputs help distinguish which RNA molecules are represented in a sample and provide the basis for comparing expression or transcript structures across experimental conditions.
A useful comparison examines how transcript abundance changes between the conditions being studied. After sequencing and computational analysis, researchers can quantify transcripts in each group and evaluate differences in expression. This approach connects molecular measurements to experimental contrasts, helping identify genes or transcripts whose activity changes during development, disease-related processes, or other investigated conditions.
In genetics, RNA sequencing can investigate how gene regulation shapes RNA output, whether alternative splicing changes transcript structure, and which mutations are expressed in transcripts. These perspectives extend analysis beyond a simple expression difference. They can help connect regulatory mechanisms and transcript variation with biological states, while also highlighting candidate genes for further study.
The workflow begins with RNA isolation, followed by conversion to cDNA. The material is then fragmented or otherwise prepared as a sequencing library before high-throughput sequencing. Computational analysis subsequently aligns or assembles the reads, quantifies transcripts, and identifies splice variants. Each stage converts the sample's RNA content into interpretable measurements of transcript representation.
RNA sequencing is useful when researchers need to compare gene activity across experimental groups, characterize cell-type-specific programs, or study development and disease. It can also support discovery of candidate biomarkers and regulatory mechanisms. By combining transcript measurements with information about splicing and expressed mutations, the method provides several complementary views of genetic activity in a sample.