  Phylogenomic analyses are subject to bias from compositional convergence and
noise from horizontal gene transfer (HGT). Compositional convergence is a
likely cause of controversy regarding phylogeny of the SAR11 group of
Alphaproteobacteria that have extremely streamlined, A+T-biased genomes. While
careful modeling can reduce artifacts caused by convergence, the most
consistent and robust phylogenetic signal in genomes may lie distributed among
encoded functional features that govern macromolecular interactions. Here we
develop a novel phyloclassification method based on signatures derived from
bioinformatically defined tRNA Class-Informative Features (CIFs). tRNA CIFs are
enriched for features that underlie tRNA-protein interactions. Using a simple
tRNA-CIF-based phyloclassifier, we obtained results consistent with those of
bias-corrected whole proteome phylogenomic studies, rejecting monophyly of
SAR11 and affiliating most strains with Rhizobiales with strong statistical
support. Yet SAR11 and Rickettsiales tRNA genes share distinct patterns of
A+T-richness, as expected from their elevated genomic A+T compositions. Using
conventional supermatrix methods on total tRNA sequence data, we could recover
the artifactual result of a monophyletic SAR11 grouping with Rickettsiales.
Thus tRNA CIF-based phyloclassification is more robust to base content
convergence than supermatrix phylogenomics on whole tRNA sequences. Also, given
the notoriously promiscuous HGT of aminoacyl-tRNA synthetases, tRNA CIF-based
phyloclassification may be relatively robust to HGT of network components. We
describe how unique features of tRNA-protein interaction networks facilitate
the mining of traits governing macromolecular interactions from genomic data,
and discuss why interaction-governing traits may be especially useful to solve
difficult problems in microbial classification and phylogeny.
