About this Research Topic
High-throughput sequencing provides important insights for genomics research in biology. In recent years, sequencing technology has achieved rapid development, from next-generation sequencing (NGS) to third-generation sequencing (TGS), which effectively improves the length of sequencing reads and saves the cost of sequencing. A large amount of omics data is generated from high-throughput sequencing, including genomics, transcriptomics, translatomics, and epigenomics, which has greatly facilitated some interesting scientific discoveries from big data.
Recent plant biology research has examined how to effectively integrate the increasingly available multi-omics data to infer biologically realistic and statistically robust regulatory networks and link multi-omics findings to physiological, pathological, and resistance characteristics. In this Research Topic, we would like to focus on the recent progress of statistical and bioinformatics methods and applications aimed at integrating multi-omics data in plants. We welcome the submission of Original Research and Review articles on, but not limited to, the following subjects:
• New algorithms and computational tool development for integrative analysis of multi-omics data.
• Multilevel dynamic analysis of the related genetic mechanisms of biological complex traits from multi-omics data information in plants.
• Network inference methods for multi-omics in complex trait dissection in plants.
Keywords: Integration analysis, multi-omics, statistical methods, bioinformatics methods, method development and application, Network inference
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