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Related Experiment Video

Updated: May 29, 2026

A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq

A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq

Published on: May 28, 2021

Seed bioinformatics.

George W Bassel¹, Michael J Holdsworth, Nicholas J Provart

¹Department of Horticulture, Oregon State University, Corvallis, OR, USA.

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

|September 8, 2011

Summary

This summary is machine-generated.

Bioinformatic data mining of gene expression datasets accelerates the discovery of gene function in plants like Arabidopsis thaliana. This approach aids in understanding plant biology, particularly seed development, by analyzing publicly available data.

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Published on: May 9, 2017

Area of Science:

Plant biology
Bioinformatics
Genomics

Background:

Gene expression data analysis is crucial for predicting gene function and generating hypotheses in plants.
Vast amounts of Arabidopsis thaliana transcriptome data are publicly available for mining.
Many Arabidopsis genes lack assigned functions, necessitating advanced analytical methods.

Purpose of the Study:

To explore the utility of bioinformatic data mining tools for hypothesis generation.
To focus on the application of these tools within the domain of seed biology.
To leverage publicly available resources for accelerating gene function discovery.

Main Methods:

Utilizing publicly accessible gene expression datasets.
Employing data mining techniques such as "electronic northerns" and co-expression analysis.
Accessing tools from resources like the Bio-Array Resource (BAR).

Main Results:

Bioinformatic analysis can significantly speed up the identification of potential gene functions.
This approach complements traditional homology searches and experimental lab work.
Facilitates hypothesis generation for uncharacterized genes.

Conclusions:

Bioinformatic data mining is a powerful and efficient method for plant gene function prediction.
The analysis of public datasets aids in understanding complex biological processes like seed development.
Accelerated discovery of gene function is achievable through accessible web-based tools.