High Impact Gene Discovery: Simple Strand-Specific mRNA Library Construction and Differential Regulatory Analysis Based on Gene Co-Expression Network
High Impact Gene Discovery: Simple Strand-Specific mRNA Library Construction and Differential Regulatory Analysis Based on Gene Co-Expression Network
复制标题
DOI：
10.1007/978-1-4939-8657-6_11
复制
发表时间：
2018-01-01
期刊：
影响因子：
--
通讯作者：
Shirasu, Ken
中科院分区：
文献类型：
--
作者：
Ichihashi, Yasunori;Fukushima, Atsushi;Shirasu, Ken
植物转录因子有潜力通过改变许多下游基因的表达而在基因调控网络中发挥枢纽作用，并且这种枢纽转录因子的鉴定极大地增强了我们对生物过程的理解。转录组分析已成为基因表达分析的主要内容。除了下一代测序（NGS）技术的最新进展外，各种mRNA文库构建和下游数据分析方法也得到了热烈的发展。在这里，我们描述了呼吸适配器定向测序（bradseq），这是一种用于Illumina平台的简单的链特异性mRNA文库制备，可以以低试剂和人工成本轻松扩展转录组实验。本协议包括我们最近的修改和详细的实用程序的布拉德-seq。由于从大规模转录组数据中提取生物学意义提出了重大挑战，我们还描述了一种超越差异表达的新分析方法。差异调控分析（DRA）是基于基因共表达网络来确定哪些调控因子或因子有能力预测两组或两种情况下差异表达基因的丰度。该协议为植物转录组数据集提供了从原始序列数据到DRA的现成信息学管道。
Plant transcription factors have potential to behave as hubs in gene regulatory networks through altering the expression of many downstream genes, and identification of such hub transcription factors strongly enhances our understating of biological processes. Transcriptome analysis has become a staple of gene expression analyses. In addition to current advances in Next Generation Sequencing (NGS) technology, various methods for mRNA library construction and downstream data analyses have been enthusiastically developed. Here, we describe Breath Adapter Directional sequencing (BrAD-seq), a simple strand-specific mRNA library preparation for the Illumina platform, allowing easy scaling of transcriptome experiments with low reagent and labor costs. This protocol includes our recent modifications and the detailed practical procedure for BrAD-seq. Because extracting biological meanings from large-scale transcriptome data presents a significant challenge, we also describe a new analytical method that goes beyond differential expression. Differential regulatory analysis (DRA) is based on a gene co-expression network to address which regulatory factor or factors have the ability to predict the abundance of differentially expressed genes between two groups or conditions. This protocol provides a ready-to-use informatics pipeline from raw sequence data to DRA for plant transcriptome datasets.