Carnelian uncovers hidden functional patterns across diverse study populations from whole metagenome sequencing reads
Author(s)Nazeen, Sumaiya; Yu, Yun W; Berger Leighton, Bonnie
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Microbial populations exhibit functional changes in response to different ambient environments. Although whole metagenome sequencing promises enough raw data to study those changes, existing tools are limited in their ability to directly compare microbial metabolic function across samples and studies. We introduce Carnelian, an end-to-end pipeline for metabolic functional profiling uniquely suited to finding functional trends across diverse datasets. Carnelian is able to find shared metabolic pathways, concordant functional dysbioses, and distinguish Enzyme Commission (EC) terms missed by existing methodologies. We demonstrate Carnelian’s effectiveness on type 2 diabetes, Crohn’s disease, Parkinson’s disease, and industrialized and non-industrialized gut microbiome cohorts.
DepartmentMassachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory; Massachusetts Institute of Technology. Department of Mathematics
Nazeen, Sumaiya, Yun W. Yu, and Bonnie Berger. "Carnelian uncovers hidden functional patterns across diverse study populations from whole metagenome sequencing reads." Genome Biology 21 (Feb. 2020): 47 doi 10.1186/s13059-020-1933-7 ©2020 Author(s)
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