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Related Experiment Video

Updated: Jan 23, 2026

Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons

Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons

Published on: June 9, 2023

Comparing time series transcriptome data between plants using a network module finding algorithm.

Jiyoung Lee^1,2, Lenwood S Heath³, Ruth Grene²

¹1Genetics, Bioinformatics and Computational Biology, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061 USA.

|June 6, 2019

Summary

This summary is machine-generated.

This study presents a novel pipeline for comparative transcriptome analysis, enabling the mapping of developmental stages between plant species. The method uses co-expression networks to identify conserved and divergent gene functions across species.

Keywords:

Arabidopsis Comparative transcriptome analysis Embryo development Network Sequence homology Soybean

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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:

Comparative transcriptome analysis aids functional gene annotation in diverse plant species.
Conserved biological processes like embryo development pose challenges in cross-species stage mapping.
Most plant molecular studies focus on model species (e.g., Arabidopsis, rice).

Purpose of the Study:

To develop a method for establishing one-to-one mapping of developmental stages between species.
To enable functional annotation of genes in non-model plant species.
To reveal functional conservation and divergence of genes across species.

Main Methods:

Converting gene expression patterns into co-expression networks.
Applying network module finding algorithms to cross-species co-expression networks.
Utilizing bash scripts for data processing and R programming with simulated annealing for module finding.

Main Results:

A comprehensive pipeline for cross-species co-expression network analysis was developed.
The method allows for the visualization of co-expression networks across species.
Functional conservation and divergence of genes were demonstrated using this approach.

Conclusions:

The pipeline provides a complete workflow from data acquisition to orthologous network identification.
The method facilitates the understanding of gene function conservation and divergence.
This approach enhances functional gene annotation in diverse plant species.