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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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DNA sequencing is a fundamental technique that is routinely used in the biological sciences. This method can be applied to a range of questions at different scales - from the sequencing of a cloned DNA fragment or the study of a mutation in a gene up to whole-genome sequencing. However, despite the widespread use of sequencing today, it was not until 1977 that Fredrick Sanger and his collaborators developed the chain-termination method to decode DNA sequences. It relies on the separation of a...

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Related Experiment Video

Updated: Jan 5, 2026

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Published on: August 3, 2018

RNA Sequencing Best Practices: Experimental Protocol and Data Analysis.

Andrew R Hesketh^1,2

¹Cambridge Systems Biology Centre, University of Cambridge, Cambridge, UK. A.Hesketh@brighton.ac.uk.

Methods in Molecular Biology (Clifton, N.J.)

|October 12, 2019

Summary

This summary is machine-generated.

RNA sequencing (RNA-seq) is the top method for studying yeast gene transcription. This guide offers best practices and tool recommendations for yeast RNA-seq data analysis, addressing common challenges.

Keywords:

RNA sequencing Transcriptomics Yeast

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Last Updated: Jan 5, 2026

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

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Area of Science:

Molecular Biology
Genomics
Bioinformatics

Background:

RNA sequencing (RNA-seq) is the preferred method for genome-wide gene transcription analysis in yeasts.
RNA-seq offers superior accuracy and flexibility over older microarray techniques for transcript identification and quantification.
Advancements in RNA-seq introduce unique challenges in data collection and analysis, particularly in establishing consensus processing methods.

Purpose of the Study:

To provide general guidelines for best practices in yeast RNA-seq studies.
To address the complexities and overwhelming array of software tools available for RNA-seq data analysis.
To recommend high-performing software tools for yeast transcriptome analysis.

Main Methods:

Review of current RNA sequencing (RNA-seq) methodologies for yeast.
Comparative analysis of different bioinformatics tools for transcriptome data processing.
Development of best practice guidelines based on tool performance and experimental considerations.

Main Results:

RNA-seq has become the standard for yeast transcriptional studies due to its accuracy and flexibility.
Significant challenges remain in standardizing RNA-seq data processing and analysis pipelines.
A curated selection of effective software tools for yeast RNA-seq analysis is identified.

Conclusions:

Implementing standardized best practices is crucial for reliable yeast RNA-seq studies.
Careful selection of bioinformatics tools is essential for accurate transcriptome analysis.
Further consensus on data analysis protocols will enhance the utility of RNA-seq in yeast research.