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Related Experiment Video

Updated: Sep 22, 2025

An Experimental and Bioinformatics Protocol for RNA-seq Analyses of Photoperiodic Diapause in the Asian Tiger Mosquito, Aedes albopictus

An Experimental and Bioinformatics Protocol for RNA-seq Analyses of Photoperiodic Diapause in the Asian Tiger Mosquito, Aedes albopictus

Published on: November 30, 2014

Exploiting plant transcriptomic databases: Resources, tools, and approaches.

Peng Ken Lim¹, Xinghai Zheng¹, Jong Ching Goh¹

¹School of Biological Sciences, Nanyang Technological University, 60 Nanyang Drive, Singapore 637551, Singapore.

Plant Communications

|May 23, 2022

Summary

This summary is machine-generated.

Vast plant RNA sequencing data offers powerful tools for gene function prediction and evolutionary studies. Re-analyzing expression profiles, networks, and differential expression reveals new biological insights and aids plant research.

Keywords:

co-expression comparative transcriptomics databases differential expression gene expression gene function

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An Experimental and Bioinformatics Protocol for RNA-seq Analyses of Photoperiodic Diapause in the Asian Tiger Mosquito, Aedes albopictus

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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

Area of Science:

Plant biology
Genomics
Bioinformatics

Background:

Over 300,000 plant RNA sequencing samples are publicly available.
These datasets originate from thousands of experiments across diverse plant species, conditions, and tissues.

Purpose of the Study:

To review methods for extracting novel biological knowledge from existing plant gene expression data.
To highlight the utility of expression profiles, co-expression networks, and differential expression analyses.
To introduce databases facilitating user-friendly access to these analytical tools.

Main Methods:

Analysis of gene expression profiles.
Construction and interpretation of co-expression networks.
Differential gene expression analysis.
Experiment correlation studies.

Main Results:

Gene expression data provide crucial clues for predicting gene function.
Comparative analyses of expression data enable new perspectives on plant evolution.
User-friendly databases offer accessible tools for data exploration.

Conclusions:

Re-analysis of public RNA sequencing data is a powerful approach to generate new biological insights.
Gene expression data are invaluable for understanding gene function and plant evolution.
Accessible databases enhance the utility of large-scale gene expression datasets for the research community.