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Updated: Nov 5, 2025

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

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Published on: September 16, 2019

Bias in RNA-seq Library Preparation: Current Challenges and Solutions.

Huajuan Shi¹, Ying Zhou¹, Erteng Jia¹

¹State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing 210096, China.

Biomed Research International

|May 14, 2021

Summary

Understanding RNA sequencing (RNA-seq) biases is crucial for accurate transcriptome analysis. This review details experimental bias sources and improvement methods for better RNA-seq data quality and interpretation.

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Area of Science:

Molecular Biology
Bioinformatics
Genomics

Background:

RNA sequencing (RNA-seq) is a powerful tool for transcriptome analysis.
RNA-seq workflows are complex and prone to experimental biases.
These biases can compromise data quality and lead to inaccurate results.

Purpose of the Study:

To identify and discuss the sources of experimental bias in RNA sequencing.
To provide methods for improving RNA sequencing experimental quality.
To offer suggestions for researchers working with RNA-seq data.

Main Methods:

Review and discussion of common RNA-seq experimental biases.
Analysis of bias origins and their impact on data.
Exploration of strategies to mitigate identified biases.

Main Results:

Experimental biases are inherent in RNA sequencing workflows.
Understanding bias sources is key to improving data integrity.
Specific methods can be applied to enhance RNA-seq quality.

Conclusions:

Addressing experimental biases is essential for reliable RNA-seq interpretation.
Implementing improvement strategies can lead to higher quality transcriptome data.
This work provides practical guidance for RNA-seq researchers.