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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: Oct 4, 2025

A Bioinformatics Pipeline to Accurately and Efficiently Analyze the MicroRNA Transcriptomes in Plants

A Bioinformatics Pipeline to Accurately and Efficiently Analyze the MicroRNA Transcriptomes in Plants

Published on: January 21, 2020

Modern Approaches for Transcriptome Analyses in Plants.

Diego Mauricio Riaño-Pachón¹, Hector Fabio Espitia-Navarro², John Jaime Riascos³

¹Laboratory of Computational, Evolutionary and Systems Biology, Center for Nuclear Energy in Agriculture, University of São Paulo, Piracicaba, Brazil. diego.riano@cena.usp.br.

Advances in Experimental Medicine and Biology

|February 3, 2022

Summary

This summary is machine-generated.

This study reviews technologies for analyzing plant transcriptomes, enabling detailed studies of gene expression and function across various species and conditions. These methods aid in identifying genes related to stress and understanding plant physiology.

Keywords:

Assembly Crops Gene expression Long reads Next-generation sequencing Polyploidy RNA-Seq Short reads Transcription

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Area of Science:

Plant biology
Genomics
Bioinformatics

Background:

The transcriptome represents all RNA transcripts in a biological system.
Meta-transcriptomics analyzes microbial communities.
Advancements in sequencing technologies enable comprehensive transcriptome analysis.

Purpose of the Study:

To present the state-of-the-art technologies for plant transcriptome analysis.
To highlight diverse applications of these technologies in plant science.
To discuss future directions in plant transcriptomics.

Main Methods:

Review of current high-throughput sequencing technologies.
Analysis of differential gene expression and coexpression.
Application of machine learning for alternative splicing and ncRNA identification.
Gene cataloging and phylogenomic approaches.

Main Results:

Technologies allow detailed assessment of transcriptome composition and abundance.
Applications include gene/transcript atlases, population mapping, and stress-related gene identification.
Machine learning aids in identifying complex RNA features.

Conclusions:

Plant transcriptome analysis technologies are rapidly advancing.
These tools are crucial for understanding plant physiology, adaptation, and evolution.
The field holds significant potential for future discoveries in plant science.