Geomosaic: a flexible bioinformatics platform integrating complementary metagenomic analyses from sequencing reads to genomes
- 1University of Naples "Federico II"
Abstract
Metagenomic analyses can be performed at multiple analytical levels, including read-based, assembly-based, and genome-resolved approaches, each capturing complementary biological information while introducing distinct analytical biases and trade-offs. However, existing workflows are commonly optimized for a single analytical strategy, making it difficult to integrate these complementary representations within a unified, reproducible framework. Here we present Geomosaic, a modular framework that integrates complementary analytical representations of metagenomic data, from reads to genomes, within a single scalable, customizable, and reproducible workflow. Built on a graph-based architecture implemented in Snakemake, Geomosaic enables users to construct complete end-to-end workflows or execute individual analytical modules while selecting among interchangeable software packages. The framework supports read preprocessing, quality control, taxonomic and functional profiling, assembly, genome reconstruction, genome-resolved annotation, custom HMM-based analyses, and automated downstream result aggregation. Automatic generation of execution scripts, modular workflows, and multiple analysis entry points make Geomosaic accessible to researchers approaching metagenomic analyses for the first time, while providing the flexibility and control required by expert users. Native support for HPC environments enables efficient analysis of datasets ranging from individual projects to large-scale metagenomic surveys. Rather than treating read-, assembly-, and genome-resolved metagenomics as alternative analytical strategies, Geomosaic integrates them as complementary representations of the same biological system, allowing users to move seamlessly between community-wide patterns and organism-resolved functional interpretation. By combining workflow flexibility, computational reproducibility, standardized analysis-ready outputs, and extensive documentation, Geomosaic provides a unified platform for environmental metagenomic analyses and facilitates reproducible downstream ecological and evolutionary investigations.
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BibTeX
@misc{corso2026geomosaic,
title = {{Geomosaic: a flexible bioinformatics platform integrating complementary metagenomic analyses from sequencing reads to genomes}},
author = {D. Corso and E. Taccaliti and B. Barosa and D. Giovannelli},
howpublished = {bioRxiv},
year = {2026},
month = sep,
doi = {10.64898/2026.09.05.749574},
url = {https://www.biorxiv.org/content/10.64898/2026.09.05.749574v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - Geomosaic: a flexible bioinformatics platform integrating complementary metagenomic analyses from sequencing reads to genomes
AU - D. Corso
AU - E. Taccaliti
AU - B. Barosa
AU - D. Giovannelli
PY - 2026
DA - 2026/09/05
PB - bioRxiv
DO - 10.64898/2026.09.05.749574
UR - https://www.biorxiv.org/content/10.64898/2026.09.05.749574v1
AB - Metagenomic analyses can be performed at multiple analytical levels, including read-based, assembly-based, and genome-resolved approaches, each capturing complementary biological information while introducing distinct analytical biases and trade-offs. However, existing workflows are commonly optimized for a single analytical strategy, making it difficult to integrate these complementary representations within a unified, reproducible framework. Here we present Geomosaic, a modular framework that integrates complementary analytical representations of metagenomic data, from reads to genomes, within a single scalable, customizable, and reproducible workflow. Built on a graph-based architecture implemented in Snakemake, Geomosaic enables users to construct complete end-to-end workflows or execute individual analytical modules while selecting among interchangeable software packages. The framework supports read preprocessing, quality control, taxonomic and functional profiling, assembly, genome reconstruction, genome-resolved annotation, custom HMM-based analyses, and automated downstream result aggregation. Automatic generation of execution scripts, modular workflows, and multiple analysis entry points make Geomosaic accessible to researchers approaching metagenomic analyses for the first time, while providing the flexibility and control required by expert users. Native support for HPC environments enables efficient analysis of datasets ranging from individual projects to large-scale metagenomic surveys. Rather than treating read-, assembly-, and genome-resolved metagenomics as alternative analytical strategies, Geomosaic integrates them as complementary representations of the same biological system, allowing users to move seamlessly between community-wide patterns and organism-resolved functional interpretation. By combining workflow flexibility, computational reproducibility, standardized analysis-ready outputs, and extensive documentation, Geomosaic provides a unified platform for environmental metagenomic analyses and facilitates reproducible downstream ecological and evolutionary investigations.
ER -