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Liu Z, Wang Q, Ma A, Feng S, Chung D, Zhao J, Ma Q, Liu B. Inference of disease-associated microbial gene modules based on metagenomic and metatranscriptomic data. Comput Biol Med 2023; 165:107458. [PMID: 37703713 DOI: 10.1016/j.compbiomed.2023.107458] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Revised: 08/22/2023] [Accepted: 09/04/2023] [Indexed: 09/15/2023]
Abstract
The identification of microbial characteristics associated with diseases is crucial for disease diagnosis and therapy. However, the presence of heterogeneity, high dimensionality, and large amounts of microbial data presents tremendous challenges in discovering key microbial features. In this paper, we present IDAM, a novel computational method for inferring disease-associated gene modules from metagenomic and metatranscriptomic data. This method integrates gene context conservation (uber-operons) and regulatory mechanisms (gene co-expression patterns) within a mathematical graph model to explore gene modules associated with specific diseases. It alleviates reliance on prior meta-data. We applied IDAM to publicly available datasets from inflammatory bowel disease, melanoma, type 1 diabetes mellitus, and irritable bowel syndrome. The results demonstrated the superior performance of IDAM in inferring disease-associated characteristics compared to existing popular tools. Furthermore, we showcased the high reproducibility of the gene modules inferred by IDAM using independent cohorts with inflammatory bowel disease. We believe that IDAM can be a highly advantageous method for exploring disease-associated microbial characteristics. The source code of IDAM is freely available at https://github.com/OSU-BMBL/IDAM, and the web server can be accessed at https://bmblx.bmi.osumc.edu/idam/.
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Affiliation(s)
- Zhaoqian Liu
- School of Mathematics, Shandong University, Jinan, Shandong, 250100, China
| | - Qi Wang
- School of Mathematics, Shandong University, Jinan, Shandong, 250100, China
| | - Anjun Ma
- Department of Biomedical Informatics, The Ohio State University, Columbus, OH, 43210, USA
| | - Shaohong Feng
- Department of Biomedical Informatics, The Ohio State University, Columbus, OH, 43210, USA
| | - Dongjun Chung
- Department of Biomedical Informatics, The Ohio State University, Columbus, OH, 43210, USA; Pelotonia Institute for Immuno-Oncology, The Ohio State University, Columbus, OH, 43210, USA
| | - Jing Zhao
- Department of Biomedical Informatics, The Ohio State University, Columbus, OH, 43210, USA
| | - Qin Ma
- Department of Biomedical Informatics, The Ohio State University, Columbus, OH, 43210, USA; Pelotonia Institute for Immuno-Oncology, The Ohio State University, Columbus, OH, 43210, USA.
| | - Bingqiang Liu
- School of Mathematics, Shandong University, Jinan, Shandong, 250100, China; Shandong National Center for Applied Mathematics, Jinan, Shandong, 250100, China.
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