DAMIAN

DAMIAN identifies pathogenic microorganisms and assembles full viral genomes from diagnostic RNA and DNA sequencing data, enabling strain-level classification and cohort-based discovery of novel pathogens without relying on sequence homology searches.

Key Features:

• Pathogen Identification: Identifies pathogenic microorganisms, including viruses, down to the strain level from RNA and DNA sequencing data.
• Full-Length Genome Assembly: Assembles complete viral genomes from samples, including those co-infected with multiple virus strains.
• Cohort-Based Analysis: Performs cohort-based cross-sample comparisons to identify sequence signatures enriched in case groups versus controls for detection of known and novel pathogens.
• Novel Pathogen Detection Without Homology Searches: Detects novel pathogens without relying on sequence homology searches in existing databases.
• Versatility Across Hosts: Applicable to sequencing data from any host organism.
• Integrated Processing and Reporting: Integrates processing steps from quality control of sequencing reads through comprehensive report generation.
• Benchmarking and Validation: Enables comparison of results with established analysis pipelines and conventional diagnostics for validation.

Scientific Applications:

• Clinical Diagnostics: Identification and classification of pathogens in authentic clinical diagnostic samples using RNA and DNA sequencing.
• Outbreak Investigation and Epidemiology: Strain-level classification and full-genome assembly to support outbreak investigations and epidemiological analyses.
• Novel Pathogen Discovery in Cohorts: Detection of novel or unexpected pathogens through cohort-based signature analysis, as demonstrated in a meningitis outbreak where enterovirus was identified.
• Validation and Benchmarking: Comparison with established pipelines and conventional diagnostics to assess reliability and speed in clinical settings.

Methodology:

Processes include quality control of sequencing reads, full-length viral genome assembly, cohort-based cross-sample signature identification for novel pathogen discovery without homology searches, comprehensive report generation, and comparison with established analysis pipelines and conventional diagnostics.