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Related Concept Videos

RNA-seq

RNA-seq

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 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.
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Related Experiment Video

Updated: Aug 4, 2025

Characterization of In Vitro Differentiation of Human Primary Keratinocytes by RNA-Seq Analysis

Characterization of In Vitro Differentiation of Human Primary Keratinocytes by RNA-Seq Analysis

Published on: May 16, 2020

RNA-seq data science: From raw data to effective interpretation.

Dhrithi Deshpande¹, Karishma Chhugani¹, Yutong Chang¹

¹Department of Pharmacology and Pharmaceutical Sciences, USC Alfred E. Mann School of Pharmacy and Pharmaceutical Sciences, Los Angeles, CA, United States.

Frontiers in Genetics

|March 31, 2023

Summary

This summary is machine-generated.

RNA sequencing (RNA-seq) analysis uses bioinformatics tools to interpret vast transcriptomic data. This review explains RNA-seq computational methods and clarifies jargon for researchers.

Keywords:

RNA sequencing bioinformatics differential gene expression high throughput sequencing read alignment transcriptome quantification

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Related Experiment Videos

Last Updated: Aug 4, 2025

Characterization of In Vitro Differentiation of Human Primary Keratinocytes by RNA-Seq Analysis

Characterization of In Vitro Differentiation of Human Primary Keratinocytes by RNA-Seq Analysis

Published on: May 16, 2020

Identification of Alternative Splicing and Polyadenylation in RNA-seq Data

Identification of Alternative Splicing and Polyadenylation in RNA-seq Data

Published on: June 24, 2021

Improving Small RNA-seq: Less Bias and Better Detection of 2'-O-Methyl RNAs

Improving Small RNA-seq: Less Bias and Better Detection of 2'-O-Methyl RNAs

Published on: September 16, 2019

Area of Science:

Bioinformatics and Computational Biology
Genomics and Transcriptomics

Background:

RNA sequencing (RNA-seq) is a key technology in biology and clinical science.
Bioinformatics tools are crucial for analyzing large-scale transcriptomic data generated by RNA-seq.
Challenges include data scale and technical biases (e.g., amplification, library preparation).

Purpose of the Study:

To explain fundamental concepts in computational RNA-seq data analysis.
To define essential discipline-specific terminology for researchers.
To guide the interpretation of complex transcriptomic data.

Main Methods:

Review of computational tools and algorithms for RNA-seq data analysis.
Explanation of common RNA-seq analysis workflows.
Discussion of methods to address technical biases in sequencing data.

Main Results:

RNA-seq analysis allows for gene and transcript expression assessment, novel transcript detection, and alternative splicing studies.
Advancements in computational tools have paralleled technological progress in RNA-seq.
A diverse toolkit now exists to unlock RNA-seq's full potential.

Conclusions:

Computational analysis is vital for extracting biological insights from RNA-seq data.
Understanding basic concepts and terminology enhances the effective use of RNA-seq.
The integration of advanced tools and researcher expertise maximizes the value of transcriptomic studies.