本文主要研究内容
作者（2019）在《Discovery of leaf region and time point related modules and genes in maize(Zea mays L.)leaves by Weighted Gene Co-expression Network analysis(WGCNA)of gene expression profiles of carbon metabolism》一文中研究指出：Maize(Zea mays L.) yield depends not only on the conversion and accumulation of carbohydrates in kernels, but also on the supply of carbohydrates by leaves. However, the carbon metabolism process in leaves can vary across different leaf regions and during the day and night. Hence, we used Weighted Gene Co-expression Network analysis(WGCNA) with the gene expression profiles of carbon metabolism to identify the modules and genes that may associate with particular regions in a leaf and time of day. There were a total of 45 samples of maize leaves that were taken from three different regions of a growing maize leaf at five time points. Robust Multi-array Average analysis was used to pre-process the raw data of GSE85963(accession number), and quality control of data was based on Pearson correlation coefficients. We obtained eight co-expression network modules. The modules with the highest significance of association with LeafRegion and TimePoint were selected. Functional enrichment and gene-gene interaction analyses were conducted to acquire the hub genes and pathways in these significant modules. These results can support the findings of similar studies by providing evidence of potential module genes and enriched pathways associated with leaf development in maize.
Abstract
Maize(Zea mays L.) yield depends not only on the conversion and accumulation of carbohydrates in kernels, but also on the supply of carbohydrates by leaves. However, the carbon metabolism process in leaves can vary across different leaf regions and during the day and night. Hence, we used Weighted Gene Co-expression Network analysis(WGCNA) with the gene expression profiles of carbon metabolism to identify the modules and genes that may associate with particular regions in a leaf and time of day. There were a total of 45 samples of maize leaves that were taken from three different regions of a growing maize leaf at five time points. Robust Multi-array Average analysis was used to pre-process the raw data of GSE85963(accession number), and quality control of data was based on Pearson correlation coefficients. We obtained eight co-expression network modules. The modules with the highest significance of association with LeafRegion and TimePoint were selected. Functional enrichment and gene-gene interaction analyses were conducted to acquire the hub genes and pathways in these significant modules. These results can support the findings of similar studies by providing evidence of potential module genes and enriched pathways associated with leaf development in maize.
论文参考文献
论文详细介绍
论文作者分别是来自Journal of Integrative Agriculture的，发表于刊物Journal of Integrative Agriculture2019年02期论文，是一篇关于,Journal of Integrative Agriculture2019年02期论文的文章。本文可供学术参考使用，各位学者可以免费参考阅读下载，文章观点不代表本站观点，资料来自Journal of Integrative Agriculture2019年02期论文网站，若本站收录的文献无意侵犯了您的著作版权，请联系我们删除。