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We have developed a machine-learning approach to identify 3537 discrete orthologue protein sequence groups distributed across all available archaeal genomes. We show that treating these orthologue groups as binary detection/non-detection data is sufficient to capture the majority of archaeal phylogeny. We subsequently use the sequence data from these groups to infer a method and substitution-model-independent phylogeny. By holding this phylogeny constrained and interrogating the intersection of this large dataset with both the Eukarya and the Bacteria using Bayesian and maximum-likelihood approaches, we propose and provide evidence for a methanogenic origin of the Archaea. By the same criteria, we also provide evidence in support of an origin for Eukarya either within or as sisters to the Thaumarchaea.

Original publication




Journal article


Proc Biol Sci

Publication Date





1009 - 1018


Amino Acid Sequence, Archaea, Artificial Intelligence, Bacteria, Base Sequence, Bayes Theorem, Classification, Eukaryota, Evolution, Molecular, Genome, Archaeal, Likelihood Functions, Markov Chains, Phylogeny