Rna-seq Analysis
Rna-seq Analysis - Related Videos
09:54
RNA-Seq
0 Views •
2023
Among different methods to evaluate gene expression, the high-throughput sequencing of RNA, or RNA-seq. is particularly attractive, as it can be performed and analyzed without relying on prior available genomic information. During RNA-seq, RNA isolated from samples of interest is used to generate a DNA library, which is then amplified and sequenced. Ultimately, RNA-seq can determine which genes are expressed, the levels of their expression, and the presence of any previously unknown transcripts.
03:21
RNA-seq
0 Views •
2021
RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while microarray-based...
05:07
Rup (RNA-seq Usability Assessment Pipeline) - Quality Control for Bulk RNA-seq Experiments in Eukaryotes
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2025
This protocol allows initial quality control for RNA-seq experiments for wet-lab biologists with limited bioinformatics experience.
06:21
Depletion of Ribosomal RNA for Mosquito Gut Metagenomic RNA-seq
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Cited by 20 •
2013
A ribosomal RNA (rRNA) depletion protocol was developed to enrich messenger RNA (mRNA) for RNA-seq of the mosquito gut metatranscriptome. Sample specific rRNA probes, which were used to remove rRNA via subtraction, were created from the mosquito and its gut microbes. Performance of the protocol can result in the removal of approximately 90-99% of rRNA.
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.
FAQs
Converting RNA into complementary DNA creates a sequenceable representation of the RNA molecules present in a sample. This step connects the original transcript population to the later computational stages, including read processing, alignment or assembly, and abundance estimation. The resulting data can therefore be used to compare gene activity across biological samples and examine changes associated with cellular or developmental conditions.
Alignment places sequencing reads against an available reference sequence, whereas transcript assembly reconstructs transcript structures from the reads. These alternatives support different analytical situations: alignment enables measurement relative to known genomic or transcript features, while assembly can help characterize transcript structures and support novel transcript discovery. The selected route influences which aspects of gene and transcript activity can be examined.
After transcript or gene abundance has been quantified, statistical models compare measurements across biological samples or experimental conditions. They distinguish systematic differences in RNA levels from variation among the observed data, producing evidence for differential expression. These results help researchers prioritize genes or transcripts whose altered activity may relate to a biological response, disease process, developmental stage, or other phenotype.
A typical workflow begins with RNA conversion to complementary DNA and sequencing, followed by quality control of the resulting data. Researchers then align reads to a reference or assemble transcripts, quantify gene or transcript abundance, and apply statistical analysis. Organizing the process in this sequence allows measurements to be checked before they support comparisons of gene activity or transcript behavior.
Quality control provides an assessment of the sequencing data before alignment or transcript assembly and abundance measurement. This checkpoint helps determine whether the data are suitable for downstream comparisons rather than treating every generated read as equally informative. Reliable quality assessment strengthens the interpretation of differential expression results and the biological conclusions drawn from comparisons among samples.
Researchers apply RNA-seq analysis to investigate genome-wide changes in gene activity during development, disease, or cellular responses. It can also support examination of alternative splicing and discovery of previously unrecognized transcripts. These applications connect molecular changes with phenotypes, helping investigators identify candidate mechanisms and formulate testable hypotheses about biological function.
Comparing samples can reveal coordinated changes in gene or transcript abundance associated with different biological states. Such patterns may indicate how cells respond, how developmental programs change, or how disease-related processes alter molecular activity. Because the results span many genes and transcripts, they provide a broad starting point for linking molecular mechanisms to observed phenotypes and selecting questions for further testing.