CARMO: a comprehensive annotation platform for functional exploration of rice multi-omics data

Area of Science:

• Agricultural Science
• Bioinformatics
• Genomics

Background:

• High-throughput technologies generate vast amounts of data in rice research.
• Interpreting biological significance from omics data is challenging, especially for rice, where annotations often rely on sequence similarity.
• Existing methods struggle with comprehensive analysis of diverse rice omics datasets.

Purpose of the Study:

• To introduce CARMO, a comprehensive annotation platform for rice multi-omics data.
• To provide web-based tools for in-depth data mining and visualization of rice omics information.
• To facilitate the biological interpretation of large-scale omics datasets and generate hypotheses.

Main Methods:

• Systematic integration of 1819 omics samples and 15,401 functional evidence terms.
• Organization of integrated data into gene sets and higher-level gene modules.
• Development of web-based tools for interactive visualization and data mining.

Main Results:

• CARMO enables comparison of high-throughput data across studies and platforms.
• Integration of multiple evidence types allows high-confidence interpretation of gene functional modules.
• Functions and pathways for uncharacterized genes can be deduced from co-expression networks and gene modules.
• Comprehensive annotations are provided for transcriptomic, epigenomic, and genomic variation data.

Conclusions:

• CARMO is a valuable resource for intensive data mining and hypothesis generation in rice research.
• The platform facilitates biological insights through user-friendly visualization and analysis tools.
• CARMO supports the interpretation of complex rice multi-omics data, advancing functional genomics.