DRAGoM

DRAGoM detects and profiles noncoding RNAs (ncRNAs) in metagenomic sequencing data using metagenome assembly graphs for homology-based identification and biomarker discovery.

Key Features:

• Assembly-graph-based detection: Operates on metagenome assembly graphs instead of relying solely on unassembled reads or assembled contigs to locate ncRNAs.
• Homology-based identification: Performs homology detection to identify homologous ncRNA sequences within assembly graph paths.
• Improved accuracy and robustness: Enhances ncRNA identification accuracy and robustness compared to conventional read- or contig-based methods.
• Short-read and fragmented assembly handling: Addresses limitations of short reads and fragmented de novo assemblies that impede structural information for ncRNA detection.
• Low-abundance ncRNA detection: Increases sensitivity for detecting low-abundance ncRNAs that may be missed by contig-centric approaches.
• Benchmark validation: Demonstrated superior performance in benchmark experiments against conventional methods.

Scientific Applications:

• ncRNA profiling in metagenomes: Characterizes noncoding RNA repertoires from metagenomic sequencing datasets.
• Biomarker discovery: Identifies ncRNA candidates that can distinguish different biological conditions, including disease versus healthy states.
• Disease characterization (Type-2 diabetes): Applied to a case-control gut microbiome dataset to identify potential ncRNA biomarkers differentiating Type-2 diabetes (T2D) samples from healthy controls.
• Microbial physiology studies: Facilitates study of regulatory ncRNAs involved in gene expression regulation, signaling, protein synthesis, and RNA processing in microbial communities.

Methodology:

Operates on metagenome assembly graphs to detect and predict ncRNAs and applies homology-based identification; performance was evaluated through benchmark experiments comparing to conventional methods.