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CC BY V4.0, Open Access
Journal article
A Systematic Approach to Bacterial Phylogeny Using Order Level Sampling and Identification of HGT Using Network Science
Microorganisms, Vol.8(2), p.312
2020
Handle:
https://hdl.handle.net/2376/17914
Abstract
Reconstructing and visualizing phylogenetic relationships among living organisms is a fundamental challenge because not all organisms share the same genes. As a result, the first phylogenetic visualizations employed a single gene, e.g., rRNA genes, sufficiently conserved to be present in all organisms but divergent enough to provide discrimination between groups. As more genome data became available, researchers began concatenating different combinations of genes or proteins to construct phylogenetic trees believed to be more robust because they incorporated more information. However, the genes or proteins chosen were based on ad hoc approaches. The large number of complete genome sequences available today allows the use of whole genomes to analyze relationships among organisms rather than using an ad hoc set of genes. We present a systematic approach for constructing a phylogenetic tree based on simultaneously clustering the complete proteomes of 360 bacterial species. From the homologous clusters, we identify 49 protein sequences shared by 99% of the organisms to build a tree. Of the 49 sequences, 47 have homologous sequences in both archaea and eukarya. The clusters are also used to create a network from which bacterial species with horizontally-transferred genes from other phyla are identified.
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Details
- Title
- A Systematic Approach to Bacterial Phylogeny Using Order Level Sampling and Identification of HGT Using Network Science
- Creators
- Ehdieh Khaledian (Author)Kelly A. Brayton (Author)Shira L. Broschat (Author)
- Publication Details
- Microorganisms, Vol.8(2), p.312
- Academic Unit
- Electrical Engineering and Computer Science, School of; Veterinary Microbiology and Pathology, Department of
- Identifiers
- 99900501745301842
- Copyright
- Creative Commons Attribution 4.0 International ; https://creativecommons.org/licenses/by/4.0/
- Language
- English
- Resource Type
- Journal article