Triticeae Genomics and Genetics, 2025, Vol.16, No.6, 262-268 http://cropscipublisher.com/index.php/tgg 264 activity level of genes in the spike. External environmental changes will also be taken into consideration, thereby jointly affecting the differentiation and development of floral organs (Lin et al., 2024; Liu et al., 2025; Yang et al., 2025). 2.3 Stage-specific gene expression and functional annotation Not all genes are equally "busy" at all stages. Transcriptome data show that some genes are only highly expressed at specific stages, especially those related to hormones, metabolism and stress responses. The "main force" regulating these processes - transcription factor families such as bHLH, bZIP, MADS-box, MYB, NAC, and WRKY - are frequently mentioned in many studies (Zhu and Wang, 2025). They are responsible for maintaining the state of meristem, controlling the flowering time, and guiding the development of floral organs. It should be noted that, in addition to protein-coding genes, some long non-coding Rnas are also involved in the morphological regulation of the spike, and the regulatory process is much more complex than we imagined (Cao et al., 2021; Tan et al., 2025; Xu et al., 2025). 3 Methods and Strategies for Constructing Gene Co-expression Networks 3.1 Acquisition and preprocessing of transcriptome data (e.g., RNA-seq) To figure out whether genes are "working together", the first step usually cannot do without transcriptome data. RNA-seq is now the most widely used, and it can simultaneously measure the expression levels of tens of thousands of genes. But once the data is in hand, it cannot be used directly. Quality checks must be conducted first to remove those "unclear" passages. Normalization cannot be omitted either; otherwise, the sequencing depth differences among different samples will be too great to compare. There are still some low-expression genes that make little contribution and need to be screened out first. Although these preprocesses may sound cumbersome, if they are not done or not done well, the network built later will be unreliable (Dam et al., 2017; Hou et al., 2022). 3.2 Principles of constructing weighted gene co-expression network analysis (WGCNA) The WGCNA method, in the final analysis, is about looking at the "synchronization rate" of gene expression. The commonly used indicator is the Pearson correlation coefficient, but now some people also use more flexible ones, such as distance or Hellinger coefficient. These values are first transformed into a matrix of "how familiar who is with whom", and then a thing called topological overlap is calculated, which can reflect the degree of overlap in the "circle of friends" between one gene and other genes. Ultimately, we can draw out some closely connected small groups, which are called modules. These modules are often associated with certain biological functions and are the focus of the analysis (Zhang and Wong, 2022). 3.3 Module identification, sample clustering, and module specificity analysis The step of dividing the modules is actually quite similar to seeing who is most familiar with whom on a social network. By using the algorithm of hierarchical clustering combined with dynamic tree cutting, hundreds or even thousands of genes can be separated into "circles of friends". Sometimes we also cluster the samples to see which ones have similar expression patterns and can correspond to the same developmental stage or a certain phenotype. The meaning of module-specific analysis is to match these "friend circles" with actual traits, such as whether they are particularly active during panicle development? In this way, we can step by step identify the regulatory modules most relevant to the target trait and also discover potential key genes, laying the foundation for the subsequent functional verification (Ruan et al., 2010; Gysi et al., 2020; Morabito et al., 2023). 4 Functional Annotation and Module–Trait Association Analysis 4.1 Correlation between modules and agronomic traits such as spike length, branch number, and floret number When looking at the relationship between modules and agronomic traits, there is a problem that is often overlooked: the connection between expression patterns and traits is not always immediately apparent. However, once you substitute the yield-related traits such as panicle length, branching number, and flower number into the analysis, it will be found that some co-expression modules are indeed closely linked to these indicators (Wei et al., 2022; Yang et al., 2023). Which "promising" genes are there in these modules exactly? Through module-trait pairing, researchers can identify the cluster that may be closely related to the spike structure as the focus for subsequent functional verification.
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