This technology is a multi-sample, sequence-graph-based framework that improves the accuracy and resolution of metagenomic analysis across microbiomes.
Current methods for analyzing metagenomic sequencing data from microbiomes struggle to deliver accurate results at a high resolution that parallels analysis of microbial isolates without high computational costs and complexity. These limitations prevent a deep analysis of genomic differences in microbiome, reduce scalability and hinder the integration of multi-sample datasets needed to capture microbial diversity and dynamics. As a result, researchers face barriers in extracting meaningful biological insights from microbiome data. There is a need for efficient, high-resolution, and high-accuracy approaches that enable scalable analysis across complex microbiomes.
This technology is a multi-sample, sequence-graph-based framework that enables accurate comparisons of genomic data across microbiomes with low computation cost, paralleling comparative genomics without the need for isolate sequencing. The analysis software achieves this by integrating hybrid coassembly, homology-based graph merging, and graph-optimization algorithm into a unified platform to improve accuracy and scalability of metagenomic analysis.
This technology has been validated in multiple settings, including colorectal cancer, preterm birth, and Vancomycin-resistant Enterococcus.
Patent Pending
IR CU26050
Licensing Contact: Joan Martinez
