RiboFrame

RiboFrame identifies 16S rDNA sequences in non-targeted metagenomic datasets and assigns taxonomic classifications to enable accurate microbial community profiling.

Key Features:

• Identification of 16S rDNA Sequences: Uses Hidden Markov Models (HMMs) to detect reads that overlap 16S rRNA genes within non-targeted metagenomic data.
• Taxonomic Classification: Applies naïve Bayesian classification to assign taxonomy to the identified ribosomal reads.
• Topological Positioning: Coherently positions ribosomal reads within the 16S rRNA gene structure, accounting for secondary structure and variable regions to improve abundance analysis.
• Non-targeted Metagenomics Compatibility: Extracts taxonomic signal directly from non-targeted metagenomic datasets rather than relying solely on reference genomes.
• Robustness to Reference Bias: Mitigates limitations of incomplete or biased genomic databases by exploiting 16S rDNA sequences present in metagenomes.

Scientific Applications:

• Non-targeted Metagenomic Profiling: Enables microbial community composition and abundance analysis from shotgun metagenomic reads containing 16S rDNA fragments.
• Environmental Microbiology: Applied to characterize microbial communities in environmental samples using 16S-derived signals from metagenomes.
• Human Health (Gut Microbiome Studies): Supports profiling of gut microbiome composition from non-targeted metagenomic datasets.
• Biotechnology: Facilitates assessment of microbial populations relevant to biotechnological processes using 16S rDNA reads from metagenomes.

Methodology:

Computational steps explicitly include HMM-based detection of reads overlapping 16S rRNA genes, naïve Bayesian taxonomic classification of those reads, coherent positioning within the 16S rRNA gene structure considering secondary structure and variable regions, and validation using simulated ribosomal data, metagenomes, and real datasets.