Ulster University Logo

Microbial co-presence and mutual-exclusion networks in the Bovine rumen microbiome

Wang, Haiying / HY, Zheng, Huiru, Dewhurst, Richard J. and Roehe, Rainer (2017) Microbial co-presence and mutual-exclusion networks in the Bovine rumen microbiome. In: 2017 IEEE Conference on Bioinformatics and Biomedicine, Kansas City, MO, USA. IEEE. 6 pp. [Conference contribution]

[img] Text - Supplemental Material
Indefinitely restricted to Repository staff only.

63kB
[img] Text - Accepted Version
629kB

URL: http://dx.doi.org/10.1109/BIBM.2017.8217635

DOI: 10.1109/BIBM.2017.8217635

Abstract

The recognized significance of rumen microbiome has inspired efforts to examine the composition of rumen microbial communities in a large scale. One of the key research areas is to infer association and dependencies between members of rumen microbial communities through correlation analysis. However, it has been found that due to the compositional nature of data, simply applying correlation-based techniques to the analysis of relative abundance of microbial genes may produce artefactual correlation and loss of information. In an attempt to mitigate the compositional effect on the analysis of rumen microbiome data, this study applied a framework including a compendium of two correlation measures and three dissimilarity metrics that are intrinsically robust to compositionality. Based on the inference of significant positive and negative associations, co-presence and mutual-exclusion networks were constructed. The corresponding modules associated with methane production were identified. The modules are highly enriched with microbial genes associated with methane emissions and encoding enzymes involved in the methane methanogensis pathway. In comparisons to previous studies, our analysis demonstrates that deriving microbial associations based on the correlations between relative abundances may not only lead to missing information but also produce spurious associations.

Item Type:Conference contribution (Paper)
Keywords:rumen microbiome; compositional data; methane emission; co-occurrence networks; correlation analysis
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Computing and Mathematics
Research Institutes and Groups:Computer Science Research Institute > Smart Environments
Computer Science Research Institute
Computer Science Research Institute > Artificial Intelligence and Applications
ID Code:39621
Deposited By: Dr Huiru Zheng
Deposited On:23 Apr 2018 14:22
Last Modified:23 Apr 2018 14:22

Repository Staff Only: item control page