DATMA

DATMA performs distributed metagenomic assembly, binning, annotation, and 16S rRNA–based taxonomic classification to analyze complex metagenomes.

Key Features:

• Sequencing Quality Control: Filters low-quality sequences to ensure high-quality input reads.
• 16S rRNA Identification: Identifies and classifies 16S ribosomal RNA gene sequences for microbial taxonomic assignment.
• Reads Binning: Groups reads into organism- or species-level bins within the metagenomic sample.
• De Novo Assembly: Assembles short DNA fragments into contiguous sequences (contigs) without reference genomes.
• Contig Annotation: Annotates assembled contigs to provide sequence-level functional and taxonomic information.
• Gene Prediction and ORF Detection: Detects open reading frames and predicts genes within assembled contigs to infer functional potential.
• Taxonomic Annotation: Assigns taxonomic identities to predicted genes or contigs to profile microbial composition.
• Distributed Computing Architecture: Distributes computational tasks across multiple nodes to reduce analysis time and scale to large datasets.

Scientific Applications:

• Microbial Community Profiling: Taxonomic characterization of microbial communities using 16S rRNA and contig-based annotations.
• Genome Recovery from Metagenomes: Extraction and reconstruction of draft genomes from metagenomic data, including recovery of a draft Anaerolineaceae genome from a biosolid metagenome.
• Functional Potential Inference: Prediction of genes and ORFs to infer metabolic and functional potential of community members.
• Environmental and Microbial Ecology Studies: Analysis of high-throughput environmental metagenomes for studies in environmental microbiology and microbial ecology.

Methodology:

Computational steps explicitly include sequencing quality control, 16S rRNA identification, reads binning, de novo assembly, contig annotation, gene prediction and ORF detection, taxonomic annotation, and distribution of tasks across multiple computational nodes.