CARMO

CARMO annotates rice multi-omics data by integrating transcriptomic datasets, epigenomic modification sites, and SNPs from genome re-sequencing to enable functional interpretation and pathway inference.

Key Features:

• Integration of Diverse Data Sources: Integrates 1819 samples from multiple omics studies and incorporates 15,401 functional terms organized into gene sets and higher-level gene modules to enable cross-study and cross-platform comparisons.
• Comprehensive Annotations: Provides detailed annotations for transcriptomic datasets, epigenomic modification sites, single nucleotide polymorphisms (SNPs) from genome re-sequencing, and gene lists, and deduces functions and pathways for genes lacking prior description by analyzing co-expression networks and gene modules.
• High Confidence Biological Interpretation: Integrates multiple types of functional evidence to support interpretation at the level of gene functional modules, addressing cases where rice gene annotation relies on sequence similarity rather than direct experimental evidence.
• Hypothesis Generation and Data Mining: Supports intensive data mining and hypothesis generation using integrated multi-omics evidence, as demonstrated by four illustrative examples in the publication.

Scientific Applications:

• Functional genomics: Interprets gene expression patterns across transcriptomic datasets to assign or refine gene functions.
• Epigenetics: Maps and analyzes epigenomic modification sites to investigate regulatory modifications across the rice genome.
• Genetic variation analysis: Identifies and characterizes single nucleotide polymorphisms (SNPs) from genome re-sequencing for studies of genetic variation.
• Pathway and network analysis: Elucidates pathways and gene modules using integrated evidence and co-expression network analysis to study complex biological processes.

Methodology:

Systematic integration of multi-omics data leveraging diverse functional evidence, organization of information into gene sets and higher-level gene modules, and analysis of co-expression networks and module-based relationships to deduce functions and pathways.