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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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Related Experiment Video

Updated: Jun 23, 2025

Optimization for Sequencing and Analysis of Degraded FFPE-RNA Samples

Optimization for Sequencing and Analysis of Degraded FFPE-RNA Samples

Published on: June 8, 2020

A comprehensive workflow for optimizing RNA-seq data analysis.

Gao Jiang¹, Juan-Yu Zheng¹, Shu-Ning Ren²

¹School of Information Science and Technology, School of Artificial Intelligence, Beijing Forestry University, Beijing, 100083, People's Republic of China.

|June 24, 2024

Summary

This summary is machine-generated.

RNA-seq analysis requires species-specific parameters for accuracy. Tailoring analysis pipelines, especially for fungal pathogens, improves biological insights and research efficiency.

Keywords:

Differential gene analysis RNA-seq data Software comparison

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Last Updated: Jun 23, 2025

Optimization for Sequencing and Analysis of Degraded FFPE-RNA Samples

Optimization for Sequencing and Analysis of Degraded FFPE-RNA Samples

Published on: June 8, 2020

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

Targeted RNA Sequencing Assay to Characterize Gene Expression and Genomic Alterations

Targeted RNA Sequencing Assay to Characterize Gene Expression and Genomic Alterations

Published on: August 4, 2016

Area of Science:

Bioinformatics
Genomics
Computational Biology

Background:

RNA-sequencing (RNA-seq) analysis software often uses generic parameters across diverse species.
This approach overlooks species-specific variations, potentially impacting data accuracy in humans, animals, plants, fungi, and bacteria.
Researchers often struggle to build effective RNA-seq analysis workflows due to tool complexity.

Purpose of the Study:

To evaluate the performance of RNA-seq analysis tools across different species.
To develop an optimized RNA-seq analysis pipeline for plant pathogenic fungi.
To establish standards for selecting RNA-seq analysis tools.

Main Methods:

Performance evaluation of 288 pipelines using various tools on five fungal RNA-seq datasets.
Utilized simulation-based evaluation for differential gene expression analysis.
Compared tools for alternative splicing analysis, including rMATS and SpliceWiz.

Main Results:

Observed performance variations of analytical tools across different species (plants, animals, fungi).
Established a superior and relatively universal fungal RNA-seq analysis pipeline.
Identified rMATS as optimal for alternative splicing analysis, with SpliceWiz as a potential supplement.

Conclusions:

Tuned analysis parameters yield more accurate biological insights than default settings.
Selecting appropriate RNA-seq analysis software based on specific data is crucial for efficiency and quality.
Developed standards for tool selection in fungal RNA-seq analysis.