TomAP: A multi-omics data analysis platform for advancing functional genomics research in tomatoes

Abstract

Tomato (Solanum lycopersicum), belonging to the Solanaceae family, holds the distinction of being the second most important vegetable crop on a global scale. As a model plant renowned for its insights into fruit ripening and disease resistance, the collaborative analysis of multi-omics data takes on an indispensable role in advancing the flavor and genetic traits of this vital crop. In our endeavor, we have seamlessly integrated a staggering 343 transcriptome datasets to create a co-expression network, including global network and conditional network, offering a expression view for multi-dimensional insight into gene expression patterns. Simultaneously, we harnessed the power of 136 epigenomic datasets to define 35 distinct chromatin states, employing the sophisticated ChromHMM algorithm. Our pursuit of holistic understanding culminated in the fusion of multi-omics data, encompassing the genome, transcriptome, and epigenome. This comprehensive approach extends to functional identification, offering invaluable insights into the intricate web of biological interactions. Our offering goes beyond mere data analysis; it presents a platform for comparative network exploration, enabling users to draw meaningful comparisons between two networks. Additionally, we have thoughtfully included extensive annotation for gene sets, encompassing GO terms, KEGG pathways, plantCyc, gene families, literature references, miRNA targets, and functional modules. The culmination of our efforts is the Tomato multi-omics data Analysis Platform (TomAP, http://bioinformatics.cau.edu.cn/TomAP/). The co-expression network and the defined chromatin states open up a realm of possibilities, not only for investigating the commonalities and variations among co-expressed genes in the context of chromatin states but also for comparative functional assessments of orthologs across species. Our aspiration is that TomAP will become avaluable resource for the research community, enabling the identification of functional genes or modules that underpin critical tomato agronomic traits.