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Related Concept Videos

RNA-seq03:21

RNA-seq

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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...
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Ribosome Profiling02:24

Ribosome Profiling

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Ribosome profiling or ribo-sequencing is a deep sequencing technique that produces a snapshot of active translation in a cell. It selectively sequences the mRNAs protected by ribosomes to get an insight into a cell’s translation landscape at any given point in time.
Applications of ribosome profiling
Ribosome profiling has many applications, including in vivo monitoring of translation inside a particular organ or tissue type and quantifying new protein synthesis levels.
The technique...
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Related Experiment Video

Updated: Aug 15, 2025

Targeted RNA Sequencing Assay to Characterize Gene Expression and Genomic Alterations
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Targeted RNA Sequencing Assay to Characterize Gene Expression and Genomic Alterations

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The hitchhikers' guide to RNA sequencing and functional analysis.

Jiung-Wen Chen1, Lisa Shrestha2, George Green1

  • 1Department of Biology, University of Alabama at Birmingham, Birmingham, AL, USA.

Briefings in Bioinformatics
|January 8, 2023
PubMed
Summary

RNA sequencing (RNA-Seq) analysis transforms raw data into biological insights. This guide details RNA-Seq steps, from quality checks to functional enrichment, aiding researchers in data interpretation and knowledge discovery.

Keywords:
RNA sequencingdifferential expressionfunctional analysismachine learningmulti-omics

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Area of Science:

  • Genomics and Molecular Biology
  • Bioinformatics and Computational Biology

Background:

  • High-throughput sequencing technologies like RNA sequencing (RNA-Seq) are crucial for transcript identification and quantification.
  • Researchers often face challenges in analyzing raw RNA-Seq data, including differential expression, pathway analysis, and interpretation.
  • Existing pipelines offer various approaches, but comprehensive guidance on deriving actionable knowledge is limited.

Purpose of the Study:

  • To provide a detailed explanation of RNA sequencing analysis steps.
  • To compare popular RNA-Seq analysis options, highlighting their advantages and disadvantages.
  • To illustrate the impact of algorithmic decisions on RNA-Seq results and interpretation.

Main Methods:

  • Detailed explanation of standard RNA-Seq analysis workflow: quality control, read alignment, summarization, differential expression analysis, and gene set analysis.
  • Discussion of advanced topics including non-coding RNA, multi-omics, meta-transcriptomics, and artificial intelligence in RNA-Seq.
  • Practical demonstration of a complete RNA-Seq analysis from raw reads to functional enrichment.

Main Results:

  • RNA-Seq analysis involves sequential decision-making points, with diverse options impacting outcomes.
  • Algorithmic choices significantly influence the final results and their biological interpretation.
  • Results from RNA-Seq analyses are not absolute and require careful consideration of analytical methods.

Conclusions:

  • This paper offers a comprehensive guide to RNA sequencing data analysis, empowering researchers to navigate complex workflows.
  • Understanding the nuances of different analytical choices is critical for accurate interpretation of transcriptomic data.
  • The study emphasizes the importance of critical evaluation of RNA-Seq results, considering the influence of analytical methodologies.