Bioscience Evidence 2025, Vol.15, No.5, 219-227 http://bioscipublisher.com/index.php/be 222 3.3 Transcriptomics and gene expression profiling Transcriptomic methods (such as RNA-Seq process, DESeq2 and co-expression network analysis) are widely used in the expression study of cotton. The research subjects include gene expression under different tissues, developmental stages and stress conditions. CottonFGD and CottonMD integrate a large amount of expression data and provide visualization tools, which can help study expression patterns and functional annotations (Zhu et al., 2017; Yang et al., 2020; 2022; Khalilisamani et al., 2024). 3.4 Variant detection and genotyping The detection and typing of variations such as SNPS and Indels are the focus of molecular breeding and population genetics research. CottonGVD and CottonMD integrate large-scale resequencing data and are equipped with built-in tools such as GWAS, eGWAS, and SNPmatch. Researchers can use them for variant screening, trait association and population structure analysis (Yu et al., 2015; Peng et al., 2021; Yang et al., 2022). 3.5 Epigenomics and regulatory genomics Epigenomic methods (such as DNA methylation, histone modification and chromatin accessibility analysis) have gradually been applied to cotton research. They help reveal the mechanisms of gene regulation and adaptation to adversity. CottonMD includes various epigenome data and provides visualization and correlation tools (Yang et al., 2020; 2022). 3.6 Systems biology and network analysis Systems biology methods (such as co-expression networks, metabolic pathways, and functional enrichment analysis) can integrate multi-omics data to study the molecular mechanisms of complex traits. CottonFGD and CottonMD provide network analyses at the levels of gene families, pathways, QTL and GWAS (Zhu et al., 2017; Yang et al., 2020; 2022; Khalilisamani et al., 2024). 3.7 Machine learning and AI in cotton genomics In recent years, machine learning and AI methods have begun to be applied in cotton genomics. They can be used for gene function prediction, phenotype prediction and precision breeding. For example, gene network clustering or Bayesian models based on transcriptome and SNP data can improve the prediction accuracy of complex traits and also provide new ideas for molecular design breeding (Yang et al., 2020; Khalilisamani et al., 2024). 4 Case Study: Application of Bioinformatics Tools in Cotton Research 4.1 Topic example: genome-wide association study (GWAS) for fiber quality traits Genome-wide association analysis (GWAS) is an important method for studying the complex traits of cotton. These properties include fiber length and strength, etc. CottonMD is a commonly used platform that has collected genotype, phenotype, transcriptome and epigenome data of 4,180 cotton materials. The platform is equipped with a variety of statistical tools and can conduct analyses such as GWAS and eGWAS. Researchers can use these tools to quickly identify variations related to fiber quality and locate candidate genes. Meanwhile, more in-depth research can be conducted by combining expression data and functional annotations, thereby accelerating molecular breeding of superior fiber traits (Yang et al., 2022). Platforms such as CottonGen also provide result visualization, QTL localization and genome browsing functions, which are very convenient to use (Yu et al., 2013; 2015). 4.2 Alternative case study: Integrating transcriptomics and epigenomics to understand cotton stress response The study of how cotton responds to adverse conditions such as drought, salinity and low temperatures is increasingly relying on multi-omics integration. CottonMD integrated transcriptome data from 76 tissues, epigenome data (DNA methylation, histone modification, chromatin accessibility) from 5 species, and metabolome data. These resources can support the combined analysis of gene expression, regulatory elements and phenotypes. Researchers can leverage this platform to analyze the changes in gene expression and epigenetic modifications under stress conditions, and identify key regulatory networks and responding genes. These results provide a basis for the breeding of cotton varieties with high stress resistance (Yang et al., 2022). In addition,
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