A Phylogeny-aware Feature Ranking for Classification of Cattle Rumen Microbiome

Jyotsna Talreja Wassan, Huiru Zheng, Haiying / HY Wang, Browne Fiona, Paul Walsh, Timmy Manning, Richard J. Dewhurst, Rainer Roehe

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Metagenomics is proliferating for studying environmental microbial communities and their role in animal functions. This paper aims to study the role of functions of microbial communities present in cattle (Bos taurus) and their relation to dietary supplement usage. The functional study was conducted as part of the EU H2020 MetaPlat project1. In this research, we proposed a novel phylogeny-driven approach to classify 16S rRNA samples from cattle rumen microbiome and relate them to the functional phenotype of diet (referred to as functional analysis). Phylogeny covers biological relationships from different taxonomical levels combined with their respective evolutionary measures. We performed this analysis by proposing a novel method based on phylogeny-adjusted distance-based indices. These indices are used in ranking microbial feature space derived from the topology of the phylogenetic tree. The integrative approach incorporating phylogeny into feature engineering as part of machine learning (ML) modeling, achieved high predictive performance with Accuracy of 0.962 and Kappa of 0.950 for classifying cattle microbiome into the phenotype of a diet supplemented with oil, nitrate, combined (with oil and nitrate) and controls.
Original languageEnglish
Title of host publication2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2019)
Place of PublicationSan Diego, CA, USA
PublisherIEEE
Pages1900-1906
ISBN (Electronic)978-1-7281-1867-3
ISBN (Print)978-1-7281-1868-0
DOIs
Publication statusPublished - 6 Feb 2020
Event2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) - San Diego, CA, USA
Duration: 18 Nov 201921 Nov 2019

Conference

Conference2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
Period18/11/1921/11/19

Keywords

  • Metagenomics
  • Phylogeny
  • Machine Learning
  • Classification
  • Phylogenetic INteraction-Adjusted index (PINA)

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