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RNA-seq03:21

RNA-seq

10.2K
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 6, 2025

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues
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Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

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Analysis of Single-Cell RNA-seq Data.

Xiaoru Dong1, Rhonda Bacher2

  • 1Department of Biostatistics, University of Florida, Gainesville, Florida, USA.

Methods in Molecular Biology (Clifton, N.J.)
|March 17, 2023
PubMed
Summary
This summary is machine-generated.

This chapter details a standard workflow and analysis tools for single-cell RNA sequencing (scRNA-seq) data. It offers guidance on method selection and interpretation for scRNA-seq experiments.

Keywords:
Gene expressionHigh-throughput sequencingNormalizationQuality controlSingle-cell RNA-seq

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Area of Science:

  • Genomics
  • Bioinformatics
  • Molecular Biology

Background:

  • Single-cell RNA sequencing (scRNA-seq) is a powerful technology driving biological discoveries.
  • The proliferation of scRNA-seq necessitates robust and standardized analytical approaches.
  • Numerous computational tools and workflows have emerged for scRNA-seq data analysis.

Purpose of the Study:

  • To present a comprehensive, standard workflow for analyzing scRNA-seq data.
  • To elaborate on essential data analysis tools applicable to scRNA-seq.
  • To provide practical recommendations for method selection and interpretation in scRNA-seq analysis.

Main Methods:

  • Description of a generalized scRNA-seq analysis pipeline.
  • Review and categorization of commonly used bioinformatics tools.
  • Inclusion of code examples for practical implementation.
  • Guidance on interpreting analysis results.

Main Results:

  • A structured workflow for scRNA-seq data processing and analysis.
  • Recommendations for selecting appropriate analytical methods based on experimental goals.
  • Illustrative code snippets demonstrating tool application.
  • Explanations of how to interpret key analytical outputs.

Conclusions:

  • Standardized workflows enhance the reproducibility and reliability of scRNA-seq studies.
  • Appropriate tool selection is crucial for accurate biological insights from scRNA-seq data.
  • This chapter serves as a practical guide for researchers utilizing scRNA-seq analysis tools.