RT Journal Article
SR Electronic
T1 Infer disease-associated microbial biomarkers based on metagenomic and metatranscriptomic data
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2021.09.13.460160
DO 10.1101/2021.09.13.460160
A1 Zhaoqian Liu
A1 Qi Wang
A1 Dongjun Chung
A1 Qin Ma
A1 Jing Zhao
A1 Bingqiang Liu
YR 2021
UL http://biorxiv.org/content/early/2021/10/21/2021.09.13.460160.abstract
AB Unveiling disease-associated microbial biomarkers is crucial for disease diagnosis and therapy. However, the heterogeneity, high-dimensionality, and large amounts of microbial data bring tremendous challenges for fundamental characteristics discovery. We present IDAM, a novel method for disease-associated biomarker inference from metagenomic and metatranscriptomic data, without requiring prior metadata. It integrates gene context conservation (uber-operon) and regulatory mechanism (gene co-expression patterns) through a mathematical graph model. We applied IDAM to inflammatory bowel disease associated matched metagenomic and metatranscriptomic datasets, which showed superior performance in biomarker inference. IDAM is freely available at https://github.com/OSU-BMBL/IDAM.Competing Interest StatementThe authors have declared no competing interest.16S rRNA16S ribosome RNAMGMetagenomicMTMetatranscriptomicIBDInflammatory bowel diseaseUCUlcerative colitisCDCrohn’s disease