N2SIMBA: Synthetic 16S Sequencing Data for Microbial Network Inference

N2SIMBA is a simulation framework for generating synthetic 16S rDNA sequencing count tables with known microbial interaction networks. The framework, created by Matteo Baldan, uses a consumer–resource model to simulate metabolite-mediated interactions and the experimental sequencing process. The dataset was last updated on 2026-05-20 and is available as a 2.8 MB PDF under a CC-BY-4.0 license.

Use Cases

• Benchmarking microbial network inference methods based on known ground-truth interaction structures.
• Simulating realistic 16S sequencing count tables based on weighted and directed input networks.
• Studying the effects of sampling variability and compositional effects on inferred microbial communities.
• Testing hypotheses about cooperative and competitive microbial interactions mediated by shared resources.

Strengths

• Provides a known ground-truth for microbial interactions, which the description states is generally unavailable for real large networks.
• Simulates biologically plausible interaction mechanisms through parameters for consumer preferences and metabolic transformation rules.
• The framework is modular and flexible, supporting the simulation of various interaction structures.

Limitations

• The dataset is a 2.8 MB PDF document describing the framework; the actual synthetic data tables are not directly provided in this record.
• Row and column counts for generated data are unknown, limiting suitability assessment for specific modeling tasks.
• The data is synthetic and may not capture all complexities of real-world microbial communities.

Provenance

Source

Matteo Baldan

Collection Method

In silico generation using the N2SIMBA consumer–resource simulation framework.

Freshness

Last updated 2026-05-20 05:43:48.