Inferring microbial interaction network from microbiome data using RMN algorithm

BMC Systems Biology
Kun-Nan TsaiDaryi Wang

Abstract

Microbial interactions are ubiquitous in nature. Recently, many similarity-based approaches have been developed to study the interaction in microbial ecosystems. These approaches can only explain the non-directional interactions yet a more complete view on how microbes regulate each other remains elusive. In addition, the strength of microbial interactions is difficult to be quantified by only using correlation analysis. In this study, a rule-based microbial network (RMN) algorithm, which integrates regulatory OTU-triplet model with parametric weighting function, is being developed to construct microbial regulatory networks. The RMN algorithm not only can extrapolate the cooperative and competitive relationships between microbes, but also can infer the direction of such interactions. In addition, RMN algorithm can theoretically characterize the regulatory relationship composed of microbial pairs with low correlation coefficient in microbial networks. Our results suggested that Bifidobacterium, Streptococcus, Clostridium XI, and Bacteroides are essential for causing abundance changes of Veillonella in gut microbiome. Furthermore, we inferred some possible microbial interactions, including the competitive relationship between Vei...Continue Reading

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Citations

Apr 4, 2017·Environmental Microbiology·Jakob Pernthaler
Nov 28, 2018·Omics : a Journal of Integrative Biology·Joo-Wook KimSeungyoon Nam
Jun 13, 2019·Proceedings of the National Academy of Sciences of the United States of America·Jared KehePaul C Blainey
Aug 21, 2020·BMC Molecular and Cell Biology·Rajith VidanaarachchiSaman Halgamuge
Nov 27, 2016·BMC Bioinformatics·Grace Tzun-Wen ShawDaryi Wang
Dec 18, 2020·Evolutionary Bioinformatics Online·Limin YuRuilong Xiang
Nov 10, 2021·PLoS Computational Biology·Sean M DevlinIrina Ostrovnaya

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