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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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Updated: Jan 15, 2026

Isolation and Transcriptome Analysis of Plant Cell Types

Isolation and Transcriptome Analysis of Plant Cell Types

Published on: April 7, 2023

RNA-Seq Analysis in Plant Stress Memory: A Bioinformatics Protocol.

Shivanti Negi¹, Shagun Sharma¹, Pankaj Kumar²

¹Department of Biotechnology, Dr. Y.S. Parmar University of Horticulture and Forestry, Solan, Himachal Pradesh, India.

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

|October 6, 2025

Summary

This summary is machine-generated.

This study outlines a bioinformatics protocol for analyzing plant RNA sequencing (RNA-Seq) data to understand stress responses and memory. Researchers can identify key genes for enhancing crop resilience to environmental challenges.

Keywords:

Bioinformatics Differential gene expression Stress resilience Stress response Transcriptomics functional enrichment

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Last Updated: Jan 15, 2026

Isolation and Transcriptome Analysis of Plant Cell Types

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A Rat Methyl-Seq Platform to Identify Epigenetic Changes Associated with Stress Exposure

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Published on: October 24, 2018

Area of Science:

Plant Science
Bioinformatics
Genomics

Background:

Plant resilience to climate change is critical.
Understanding plant stress responses requires advanced analytical methods.
RNA sequencing (RNA-Seq) offers comprehensive transcriptomic analysis.

Purpose of the Study:

To present a step-by-step bioinformatics protocol for plant RNA-Seq data analysis under stress.
To guide researchers in understanding plant stress memory and response mechanisms.
To facilitate the identification of genes contributing to plant stress resilience.

Main Methods:

Quality control and read trimming of RNA-Seq data.
Sequence alignment and gene expression quantification.
Differential expression analysis, functional enrichment, and result interpretation.

Main Results:

The protocol details essential bioinformatics steps for RNA-Seq data analysis.
It emphasizes the use of specific tools and databases for reproducibility.
The methods enable the characterization of genes involved in plant stress memory.

Conclusions:

This protocol provides a clear framework for analyzing plant stress response data.
It aids in identifying genetic factors for improved crop resilience.
The findings support future research in plant stress adaptation and memory.