MAAWf

MAAWf performs automated integrated analyses of microbiome sequencing data from 16S rRNA gene and whole metagenomic shotgun datasets to profile taxonomy, protein-coding genes, metabolic pathways, CAZy enzymes, and antibiotic resistance genes (ARGs), and to perform OTU clustering and diversity and differential analyses.

Key Features:

• Whole Metagenomic Shotgun (WMS) Workflow: Assesses taxonomy, protein-coding gene content, metabolic pathway profiles, carbohydrate-active enzymes (CAZy), and antibiotic resistance genes (ARGs).
• 16S Sequencing Workflow: Performs OTU counting and clustering, computes alpha- and beta-diversity, analyzes inter-group differences, and generates functional annotations.
• Performance and Benchmarking: Benchmarked against DIAMOND-MEGAN6, MG-RAST, DADA2, and QIIME2 on public datasets and reported to produce similar results with reduced running times.
• Input Formats: Supports primary sequence files in FASTQ format and taxonomic formats such as OTU and BIOM.

Scientific Applications:

• Microbial Ecology: Comparative community composition and functional profiling across environmental samples using 16S and shotgun data.
• Human Health: Characterization of microbiome taxonomy, metabolic potential, CAZy, and ARGs in health and disease studies.
• Environmental Monitoring: Detection and functional assessment of microbial communities and ARGs in environmental samples.
• Biotechnology: Identification of metabolic pathways, CAZy enzymes, and gene functions relevant to biotechnological applications.

Methodology:

MAAWf is implemented on Ubuntu 16.04.6 LTS and accepts FASTQ sequence files as well as OTU and BIOM taxonomic formats.