{"id":"CU26050","slug":"multi-sample-graph-based--CU26050","source":{"id":"CU26050","dataset":"techtransfer","title":"Multi-sample graph-based framework for comparative microbiomes genomics","description_":"<p>This technology is a multi-sample, sequence-graph-based framework that improves the accuracy and resolution of metagenomic analysis across microbiomes.</p>\r\r<h2>Unmet Need: Accurate, scalable microbiomes genomic analysis that facilitates detection of strain-level differences</h2>\r\r<p>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. </p>\r\r<h2>The Technology: High-resolution and computationally efficient framework for microbiome genomics</h2>\r\r<p>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. </p>\r\r<p>This technology has been validated in multiple settings, including colorectal cancer, preterm birth, and  Vancomycin-resistant Enterococcus. </p>\r\r<h2>Applications:</h2>\r\r<ul>\r<li>Software for comparative metagenomic analysis</li>\r<li>Genome language modeling for artificial intelligence applications </li>\r<li>Biosecurity screening for harmful microorganisms </li>\r</ul>\r\r<h2>Advantages:</h2>\r\r<ul>\r<li>Computationally efficient </li>\r<li>Improves accuracy of sequence and variant detection</li>\r<li>Optimized for comparative genomic analysis </li>\r</ul>\r\r<h2>Lead Inventor:</h2>\r\r<p><a href=\"https://systemsbiology.columbia.edu/faculty/tal-korem\">Tal Korem, Ph.D.</a></p>\r\r<h2>Patent Information:</h2>\r\r<p>Patent Pending</p>\r\r<h2>Related Publications:</h2>\r\r<ul>\r<li><a href=\"https://pmc.ncbi.nlm.nih.gov/articles/PMC12704009/\">Coleman I, Mametyarova N, Zaznaev A, Cai P, Yu L, Meydan Y, Litman A, Sharma A, He L, Simkhovich A, Seeram D, Park H, Nobel YR, Kav AB, Pe’er I, Uhlemann AC, Korem T. “Comparative metagenomics using pan-metagenomic graphs” bioRxiv. 2025 Nov 26.</a></li>\r</ul>\r\r<h2>Tech Ventures Reference:</h2>\r\r<ul>\r<li><p>IR CU26050</p></li>\r<li><p>Licensing Contact: <a href=\"mailto:techtransfer@columbia.edu\">Joan Martinez</a></p></li>\r</ul>\r","tags":[],"file_number":"CU26050","collections":[],"meta_description":"A scalable, graph-based framework enabling high-resolution, cross-sample metagenomic comparisons with low computation.","apriori_judge_output":"{\"scores\":{\"novelty\":4.0,\"potential_impact\":4.0,\"readiness\":3.0,\"scalability\":4.0,\"timeliness\":4.0},\"weighted_score\":3.65,\"risks\":[\"Early-stage validation outside of core contexts could be limited.\",\"Dependency on graph-based methods may have computational or interoperability challenges across platforms.\"],\"one_sentence_take\":\"Strong novelty and impact with solid readiness and scalability, but watch for broader validation and platform interoperability.\"}","inventors":["Izaak Coleman","Tal Korem"],"manager":"Joan Martinez","depts":["Cellular, Molecular and Biomedical Studies","Systems Biology"],"divs":["Columbia University Medical Center"],"date_released":"2026-05-21"},"highlight":{},"matched_queries":null,"score":0.0}