Geomosaic: a flexible bioinformatics platform integrating complementary metagenomic analyses from sequencing reads to genomes

Metagenomic analyses can be carried out at various levels, such as read-based, assembly-based, and genome-resolved approaches. Each approach offers unique biological insights, but also introduces specific biases and limitations. Current workflows typically focus on one analysis strategy, making it challenging to combine these complementary data representations in a consistent, reproducible manner.

We introduce Geomosaic, a flexible framework designed to merge these diverse metagenomic representations into one cohesive, adaptable, and repeatable workflow. The platform operates on a graph-based system, utilizing Snakemake for implementation, allowing users to create complete workflows or use individual modules independently, with the ability to choose between various software options.

Geomosaic covers the entire analytical process, from data preprocessing and quality control to taxonomic and functional profiling, assembly, genome reconstruction, genome-resolved annotation, custom HMM-based analyses, and automated result aggregation.

The framework is beginner-friendly, offering automated script generation and modular workflows, while also accommodating experienced users with its adaptable structure. Geomosaic provides native support for high-performance computing (HPC) environments, enabling efficient processing of datasets ranging from single projects to extensive metagenomic surveys.

Instead of viewing read-, assembly-, and genome-resolved metagenomics as separate analytical strategies, Geomosaic treats them as complementary perspectives of the same biological system. This integration enables users to smoothly transition between broad community patterns and detailed organism-specific functional interpretations.

By combining adaptability, computational reproducibility, standardized outputs, and comprehensive documentation, Geomosaic offers a unified platform for environmental metagenomic analyses and promotes reproducible downstream ecological and evolutionary studies.