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Updated: Feb 7, 2026

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

Published on: October 19, 2021

MCENet: A database for maize conditional co-expression network and network characterization collaborated with

Tian Tian¹, Qi You¹, Hengyu Yan¹

¹State Key Laboratory of Plant Physiology and Biochemistry, College of Biological Sciences, China Agricultural University, Beijing 100193, China.

Journal of Genetics and Genomics = Yi Chuan Xue Bao

|July 31, 2018

Summary

We developed MCENet, a searchable platform integrating maize transcriptomic and epigenomic data. This tool aids researchers in identifying gene functions and modules related to important agronomic traits in maize (Zea mays).

Keywords:

Conditional co-expression network Epigenomic datasets Maize Module finder Transcriptomic datasets

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Area of Science:

Plant genomics and bioinformatics
Functional genomics in maize (Zea mays)

Background:

Maize is a globally significant crop, yet its gene functions are largely uncharacterized.
Transcriptome data is crucial for understanding gene expression, predicting functions, and identifying trait-related modules in plants.

Purpose of the Study:

To integrate diverse maize omics data for a comprehensive understanding of gene regulation.
To develop a user-friendly platform for exploring maize gene co-expression networks and identifying functional genes.

Main Methods:

Integrated 701 transcriptomic and 108 epigenomic datasets.
Constructed conditional co-expression networks using multi-dimensional omics data.
Developed the Maize Conditional Co-expression Network (MCENet) platform with analysis toolkits.

Main Results:

Created MCENet, a searchable online platform offering 10 global/conditional co-expression networks.
Included 5 network analysis toolkits (Network Search, Network Remodel, Module Finder, Network Comparison, Dynamic Expression View).
Provided functional support toolkits for motif and module enrichment analysis.

Conclusions:

MCENet facilitates the identification of maize functional genes and modules associated with agronomic traits.
The platform supports plant research communities in advancing maize functional genomics.
MCENet is publicly accessible for broad utilization in maize research.