The advent of multi-omics technologies is producing massive amount of high-throughput data and network biology, and artificial intelligence (AI) is revolutionizing the data integration in plant research. Researchers are now able to analyze massive datasets of plant-pathogen interactions to identify patterns and trends that were previously invisible Systems biology and AI-based machine learning models on big data can serve as a “gold-mine”.  To this end, CSBLab have acquired extensive training and research expertise to handle multi-collaborative, outcome-focused, rigorous, and reproducible research at a huge scale.

Through this aim, we will provide a multiscale view of data interpretation for a

wide array of -omics utilizing biostatistics models to establish a holistic understanding of gene-trait and pathway associations.

The single-cell transcriptomics can identify the role of different cells and regulators driving disease pathogenesis. CSBLab will assess the scRNA-Seq multi-ome driving the cellular heterogeneity and regulatory landscape in different conditions. Furthermore, we will employ systems biology and machine learning approaches to accelerate the identification of master regulators. 

Overall, the single-cell technologies, GRNs, and computational biology methods will allow us to understand how cellular processes operate in each organ to keep homeostasis and how cells adapt to different challenges during diseases.

Next-generation sequencing (NGS) data analysis is a complex process that transforms raw sequencing data into meaningful biological insights. It involves a series of computational steps to process the massive amounts of data generated by NGS technologies. The CSBLab follows FAIR data analysis, reproducible, and reporting practices including version control, containerization, open-source computational tools usages, and performed on university-wide shared computation clusters.

This area of research support will provide the crucial expertise in reproducible data analysis, extracting valuable information from NGS experiments, enabling advancements in genomics, medicine, and various biological research fields.

Performing cross-species integration in single-cell and bulk RNA sequencing involves several key steps and considerations to effectively compare and analyze gene expression data across different species. This process is crucial for understanding evolutionary relationships, gene function, and the conservation of cell types across species. This process can involve different strategies for mapping genes by homology, including using one-to-one orthologs or including one-to-many or many-to-many orthologs based on criteria such as high average expression level or strong homology confidence. Currently, CSBlab is developing a modular platform by integrating existing and novel computational tools and algorithms that can predict shared gene profiles. 

This area of research will simplify the translational research from model to non-model systems and help us understand shared genetic and phenotypic responses.