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Title 

A tree of cellular life inferred from a genomic census of molecular functions

Authors 

Kyung Mo KimA NasirHwang Kyu InG Caetano-Anolles

Publisher 

Springer Verlag (Germany)

Issue Date 

2014

Citation 

Journal of Molecular Evolution, vol. 79, no. 5, pp. 240-262

Keywords 

EvolutionGene ontologyPhylogenomicsTree of life

Abstract 

Phylogenomics aims to describe evolutionary relatedness between organisms by analyzing genomic data. The common practice is to produce phylogenomic trees from molecular information in the sequence, order, and content of genes in genomes. These phylogenies describe the evolution of life and become valuable tools for taxonomy. The recent availability of structural and functional data for hundreds of genomes now offers the opportunity to study evolution using more deep, conserved, and reliable sets of molecular features. Here, we reconstruct trees of life from the functions of proteins. We start by inferring rooted phylogenomic trees and networks of organisms directly from Gene Ontology annotations. Phylogenies and networks yield novel insights into the emergence and evolution of cellular life. The ancestor of Archaea originated earlier than the ancestors of Bacteria and Eukarya and was thermophilic. In contrast, basal bacterial lineages were non-thermophilic. A close relationship between Plants and Metazoa was also identified that disagrees with the traditional Fungi-Metazoa grouping. While measures of evolutionary reticulation were minimum in Eukarya and maximum in Bacteria, the massive role of horizontal gene transfer in microbes did not materialize in phylogenomic networks. Phylogenies and networks also showed that the best reconstructions were recovered when problematic taxa (i.e., parasitic/symbiotic organisms) and horizontally transferred characters were excluded from analysis. Our results indicate that functionomic data represent a useful addition to the set of molecular characters used for tree reconstruction and that trees of cellular life carry in deep branches considerable predictive power to explain the evolution of living organisms.

ISSN 

0022-2844

Link 

http://dx.doi.org/10.1007/s00239-014-9637-9

Appears in Collections

1. Journal Articles > Journal Articles

Registered Date

2019-05-02


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