MCENet: A database for maize conditional co-expression network and network characterization collaborated with multi-dimensional omics levels

Tian Tian; Qi You; Hengyu Yan; Wenying Xu; Zhen Su

doi:10.1016/j.jgg.2018.05.007

Volume 45 Issue 7

Jul. 2018

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Article Navigation > Journal of Genetics and Genomics > 2018 > 45(7): 351-360

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MCENet: A database for maize conditional co-expression network and network characterization collaborated with multi-dimensional omics levels

doi: 10.1016/j.jgg.2018.05.007

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

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Corresponding author: E-mail address: x_wenying@yahoo.com (Wenying Xu); E-mail address: zhensu@cau.edu.cn (Zhen Su)
Received Date: 2018-01-08
Accepted Date: 2018-05-25
Rev Recd Date: 2018-04-30

Available Online: 2018-07-18

Publish Date: 2018-07-20

Abstract

Abstract

Maize (Zea mays) is the most widely grown grain crop in the world, playing important roles in agriculture and industry. However, the functions of maize genes remain largely unknown. High-quality genome-wide transcriptome datasets provide important biological knowledge which has been widely and successfully used in plants not only by measuring gene expression levels but also by enabling co-expression analysis for predicting gene functions and modules related to agronomic traits. Recently, thousands of maize transcriptomic data are available across different inbred lines, development stages, tissues, and treatments, or even across different tissue sections and cell lines. Here, we integrated 701 transcriptomic and 108 epigenomic data and studied the different conditional networks with multi-dimensional omics levels. We constructed a searchable, integrative, one-stop online platform, the maize conditional co-expression network (MCENet) platform. MCENet provides 10 global/conditional co-expression networks, 5 network accessional analysis toolkits (i.e., Network Search, Network Remodel, Module Finder, Network Comparison, and Dynamic Expression View) and multiple network functional support toolkits (e.g., motif and module enrichment analysis). We hope that our database might help plant research communities to identify maize functional genes or modules that regulate important agronomic traits. MCENet is publicly accessible at http://bioinformatics.cau.edu.cn/MCENet/.
- Conditional co-expression network,
- Module finder,
- Transcriptomic datasets,
- Epigenomic datasets,
- Maize

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