Functional phylogenomics analysis of bacteria and archaea using consistent genome annotation with UniFam

Abstract

BackgroundPhylogenetic studies have provided detailed knowledge on the evolutionary mechanisms of genes and species in Bacteria and Archaea. However, the evolution of cellular functions, represented by metabolic pathways and biological processes, has not been systematically characterized. Many clades in the prokaryotic tree of life have now been covered by sequenced genomes in GenBank. This enables a large-scale functional phylogenomics study of many computationally inferred cellular functions across all sequenced prokaryotes.ResultsA total of 14,727 GenBank prokaryotic genomes were re-annotated using a new protein family database, UniFam, to obtain consistent functional annotations for accurate comparison. The functional profile of a genome was represented by the biological process Gene Ontology (GO) terms in its annotation. The GO term enrichment analysis differentiated the functional profiles between selected archaeal taxa. 706 prokaryotic metabolic pathways were inferred from these genomes using Pathway Tools and MetaCyc. The consistency between the distribution of metabolic pathways in the genomes and the phylogenetic tree of the genomes was measured using parsimony scores and retention indices. The ancestral functional profiles at the internal nodes of the phylogenetic tree were reconstructed to track the gains and losses of metabolic pathways in evolutionary history.ConclusionsOur functional phylogenomics analysis shows divergent functional profiles of taxa and clades. Such function-phylogeny correlation stems from a set of clade-specific cellular functions with low parsimony scores. On the other hand, many cellular functions are sparsely dispersed across many clades with high parsimony scores. These different types of cellular functions have distinct evolutionary patterns reconstructed from the prokaryotic tree.Electronic supplementary materialThe online version of this article (doi:10.1186/s12862-014-0207-y) contains supplementary material, which is available to authorized users.

Metabolic bacterial genes and the construction of high-level composite lineages of life

“…Such a role is directly consistent with the frequent exchanges of metabolic genes between bacterial lineages themselves [10]. Therefore, not all genes on Earth have the same evolutionary fate and impact.…”

Community proteogenomics reveals the systemic impact of phosphorus availability on microbial functions in tropical soil

“…2. The genomes were re-annotated consistently by UniFam, as described previously 40 . The gene abundance of an EC number or a GO term was quantified in a metagenome based on the base-pair counts of reads mapped onto proteins annotated with this EC number or GO term.…”

“…37) with a minimum overlap length of 40 bp. Genes and proteins were called from the obtained scaffolds using the Prodigal algorithm 47 and annotated using the UniFam workflow 40 . The coverage depths of the scaffolds in the high-and low-molecular-weight DNA samples of a metagenome were estimated by mapping reads from the 2× 250-bp dataset and the 2× 150-bp dataset, respectively, using Bowtie 2 (ref.…”

“…The phylogenetic tree was visualized using the Interactive Tree of Life online interface 53 . The metabolic network of novel genomes was reconstructed using UniFam 40 . The operons of the phytase genes were identified by requiring the same translation direction for all genes in an operon 54 and less than 50 bp of intergenic distance between neighbouring genes in an operon.…”

Integrated proteomics and metabolomics suggests symbiotic metabolism and multimodal regulation in a fungal‐endobacterial system

“…A protein sequence database was constructed from all predicted proteins in the M. elongata genome (BioProject Accession: PRJNA196039, and ID: 196039) and the endobactrium genome (BioProject Accession: PRJNA187127, and ID: 187127). The molecular functions of the predicted proteins in M. elongata and the endobacterium were predicted using the UniFam eukaryotic and prokaryotic databases respectively, as described previously (Chai et al ., ), and then used to reconstruct their metabolic networks with MetaCyc (Caspi et al ., ). Reverse sequences of all predicted proteins were added to the protein sequence database as decoys for estimation of false discovery rates (FDR) of peptide and protein identification (Elias and Gygi, ).…”