RiboTaxa

RiboTaxa reconstructs full-length small subunit (SSU) rRNA genes from Illumina (meta)genomic reads and performs high-resolution taxonomic classification to enable precise microbial profiling down to the species level.

Key Features:

• Full-Length SSU rRNA Gene Reconstruction: Employs BBTools, FastQC, SortMeRNA, MetaRib, EMIRGE, VSEARCH, and BBMap to reconstruct full-length SSU rRNA genes from fragmented metagenomic reads.
• High-Resolution Taxonomic Classification: Utilizes QIIME 2's Sklearn classifier for taxonomic classification and was evaluated relative to Kraken2, Centrifuge, METAXA2, phyloFlash, SPINGO, BLCA, and MEGAN.
• Benchmarked Performance: Benchmarked against rRNA-specialized and general-purpose read mappers, reference-targeted assemblers, and taxonomic classifiers, achieving an F-measure of 100% at genus level and 83.7% at species level with no false positive detections.
• Versatility Across Environments: Validated on real datasets from oceanic, soil, and human gut samples and on data produced by metagenomics and gene capture by hybridization.
• Discovery of Microbial Novelties: Enabled identification of microbial novelties previously undetected by existing bioinformatics tools.

Scientific Applications:

• Microbial ecology: Characterizing microbiota composition across diverse ecosystems using high-resolution SSU rRNA profiles.
• Evolutionary biology: Resolving taxonomic lineages and diversity for evolutionary and phylogenetic analyses.
• Biomedicine: Profiling human gut microbiota to investigate associations with health and disease.
• Discovery and functional inference: Detecting previously unrecognized taxa to inform studies of ecological functions and environmental health.

Methodology:

Computational methods explicitly include BBTools, FastQC, SortMeRNA, MetaRib, EMIRGE, VSEARCH, and BBMap for SSU rRNA reconstruction from Illumina (meta)genomic reads, QIIME 2's Sklearn classifier for taxonomic classification, and benchmarking against Kraken2, Centrifuge, METAXA2, phyloFlash, SPINGO, BLCA, and MEGAN.