BEEM-Static: Accurate inference of ecological interactions from cross-sectional microbiome data

Chenhao Li; Tamar V Av-Shalom; Jun Wei Gerald Tan; Junmei Samantha Kwah; Kern Rei Chng; Niranjan Nagarajan

doi:10.1371/journal.pcbi.1009343

BEEM-Static: Accurate inference of ecological interactions from cross-sectional microbiome data

PLoS Comput Biol. 2021 Sep 8;17(9):e1009343. doi: 10.1371/journal.pcbi.1009343. eCollection 2021 Sep.

Authors

Chenhao Li^{1

2}, Tamar V Av-Shalom^{1

3}, Jun Wei Gerald Tan¹, Junmei Samantha Kwah¹, Kern Rei Chng¹, Niranjan Nagarajan^{1

4

5}

Affiliations

¹ Laboratory of Metagenomic Technologies and Microbial Systems, Genome Institute of Singapore, Singapore, Singapore.
² Center for Computational and Integrative Biology, Massachusetts General Hospital, Boston, Massachusetts, United States of America.
³ Department of Cell & Systems Biology, University of Toronto, Toronto, Canada.
⁴ School of Computing, National University of Singapore, Singapore, Singapore.
⁵ Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.

Abstract

BEEM-Static provides new opportunities for mining ecologically interpretable interactions and systems insights from the growing corpus of microbiome data.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Biomass
Cross-Sectional Studies
Datasets as Topic
Ecosystem*
Gastrointestinal Microbiome*
Humans

Grants and funding

CL, TVAS, JWGT, JSK, KRC and NN are supported by Agency for Science, Technology and Research (A*STAR) and an Industry Alignment Fund - Prepositioning Programs (IAF-PP) grant (H18/01/a0/016) from the Biomedical Research Council (BMRC). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.