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Microbial abundance analysis and phylogenetic adoption in functional metagenomics

Wassan, Jyotsna Talreja, Wang, Haiying / HY, Fiona, Browne and Zheng, Huiru (2017) Microbial abundance analysis and phylogenetic adoption in functional metagenomics. In: The IEEE International Conference on Computational Intelligence in Bioinformatics and Computational Biology (IEEE CIBCB 2017), Manchester, UK. IEEE. 8 pp. [Conference contribution]

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URL: http://dx.doi.org/10.1109/CIBCB.2017.8058557

DOI: 10.1109/CIBCB.2017.8058557


Metagenomics is an unobtrusive science of studying uncultivated microbes sampled directly from an environment, e.g. soil, ocean, air, human body, or animals, etc. Functional metagenomics particularly deals with linking microbes to environmental derivations, such as classifying the role of human gut microbiome into a diseased or non-diseased state. Ongoing research in this area includes analyzing the structure of microbial communities, and relate it to functional analysis. We present an integrative experimental framework for functional metagenomics, including data driven (abundance count of microbial species) and knowledge driven (phylogenetic tree structure) contexts. Our related experiments, indicate that i) feature selection improves the performance of classifying human microbiome samples, ii) the classification of human microbiome remains a challenging problem while incorporating phylogenetic structures. For example, our best accuracy attained on the Costello body site (CBH) dataset with forehead and external ear as body sites, is 89.13 % with a non-phylogenetic model, and 78.26 % with a phylogenetic model. This forms a potential research direction of further exploration of space for incorporating phylogeny in microbial analysis and hence developing integrative computational models for deriving functional phenotypes, based on metagenomic sequencing data.

Item Type:Conference contribution (Paper)
Keywords:Metagenomics, Phylogeny, Classification, Next Generation Sequencing (NGS), Operational Taxonomical Units (OTUs), Metagenomes, Machine Learning (ML)
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:38811
Deposited By: Dr Huiru Zheng
Deposited On:23 Oct 2017 10:43
Last Modified:23 Oct 2017 10:43

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