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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...

Ribosome Profiling

Ribosome Profiling

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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Updated: May 6, 2026

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

Biases in small RNA deep sequencing data.

Carsten A Raabe¹, Thean-Hock Tang, Juergen Brosius

¹Institute of Experimental Pathology (ZMBE), University of Muenster, Von-Esmarch-Strasse 56, 48149 Muenster, Germany and Advanced Medical and Dental Institute (AMDI), Universiti Sains Malaysia, 13200 Penang, Malaysia.

Nucleic Acids Research

|November 8, 2013

Summary

This summary is machine-generated.

Small RNA sequencing (sRNA-seq) faces significant bias, affecting gene expression accuracy. This review highlights challenges and proposes solutions to ensure reliable sRNA-seq data for molecular biology and medicine.

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Unbiased Deep Sequencing of RNA Viruses from Clinical Samples

Unbiased Deep Sequencing of RNA Viruses from Clinical Samples

Published on: July 2, 2016

Area of Science:

Molecular Biology
Genomics
Bioinformatics

Background:

High-throughput RNA sequencing (RNA-seq) is crucial for gene discovery and expression profiling.
RNA-seq offers digital data for meta-analysis and reduced noise compared to hybridization methods.
Multiplex sequencing enhances efficiency and cost-effectiveness for parallel gene expression analysis.

Purpose of the Study:

To review recent findings challenging the accuracy of small non-protein coding RNA sequencing (sRNA-seq) data.
To identify sources of bias in sRNA-seq, including adapter ligation, RNA structure, modifications, and PCR amplification.
To suggest strategies and precautions for minimizing or overcoming bias in sRNA-seq.

Main Methods:

Literature review of recent findings on sRNA-seq bias.
Analysis of factors influencing cDNA synthesis and amplification efficiencies.
Identification of adapter, barcode, RNA structure, modification, and GC-content impacts.

Main Results:

sRNA-seq data can exhibit severe bias, leading to artificially enhanced or diminished RNA expression levels.
Bias originates from variations in adapter/barcode ligation, RNA structures, modifications, and PCR amplification.
Unequal polymerase chain reaction amplification efficiencies are linked to RNA GC-content.

Conclusions:

The accuracy of sRNA-seq data is compromised by various technical biases.
Understanding these biases is critical for reliable gene expression analysis.
Implementing suggested precautions is essential for robust molecular biology and personalized medicine applications.