摘要
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 suc- cessfully 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 net- works, 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.
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 suc- cessfully 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 net- works, 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.
基金
supported by the National Natural Science Foundation of China (Nos. 31771467, 31571360 and 31371291)
