AGS ACN

AGS ACN computes Average Genome Size (AGS) and 16S rRNA gene Average Copy Number (ACN) from metagenomic data to quantify microbial genomic traits relevant to ecological strategies and environmental conditions.

Key Features:

• Component scripts: ags.sh computes Average Genome Size (AGS) and acn.sh computes 16S rRNA gene Average Copy Number (ACN).
• Fast computation: ags.sh achieves up to 11 times faster estimation of AGS compared to existing methods, enabling efficient processing of large datasets.
• High accuracy: Both ags.sh and acn.sh report accuracy comparable to or exceeding other available methods for their respective trait estimates.
• Unassembled metagenome support: The methods operate on unassembled metagenomic data without requiring genome assembly.
• Universal gene annotation: Computations are based on ultra-fast annotation of 35 universally distributed single-copy genes.
• Optimized for large-scale ecological studies: The implementation emphasizes speed and accuracy for analysis of complex environmental metagenomes.

Scientific Applications:

• Microbial ecology insights: Quantification of AGS and ACN informs studies of microbial ecological strategies and life-history traits.
• Environmental condition analysis: Changes in AGS and ACN provide indicators of environmental conditions shaping microbial community composition.
• Metagenomic data enhancement: Integration of AGS and ACN into analyses improves interpretation of unassembled metagenomic datasets.
• Case study application: AGS_ACN was applied to 139 prokaryotic metagenomes from TARA Oceans to reveal ecological strategies across different water layers.

Methodology:

Ultra-fast annotation of 35 universally distributed single-copy genes in unassembled metagenomic datasets is used to estimate Average Genome Size (AGS) and 16S rRNA gene Average Copy Number (ACN) without requiring genome assembly.