The Subsystems Approach to Genome Annotation and its Use in the Project to Annotate 1000 Genomes

Overbeek, Ross; Begley, Tadhg P.; Butler, Ralph; Choudhuri, Jomuna V.; Chuang, Han‐Yu; Cohoon, Matthew; Crécy‐Lagard, Valérie de; Diaz, Naryttza N.; Disz, Terry; Edwards, Robert A.; Fonstein, Michael; Frank, E.D.; Gerdes, Svetlana; Glass, Elizabeth M.; Goesmann, Alexander; Hanson, Andrew C.; Iwata‐Reuyl, Dirk; Jensen, Roy A.; Jamshidi, Neema; Krause, Lutz; Kubal, Michael; Larsen, Niels; Linke, Burkhard; McHardy, Alice C.; Meyer, Patrick; Neuweger, Heiko; Olsen, Gary J.; Olson, Robert; Osterman, Andrei L.; Portnoy, Vasiliy A.; Pusch, Gordon D.; Rodionov, Dmitry A.; Rückert, Christian; Steiner, Jason; Stevens, Rick; Thiele, Ines; Vassieva, Olga; Ye, Yuzhen; Zagnitko, Olga; Vonstein, Veronika

doi:10.1093/nar/gki866

Cited by 1,792 publications

(1,713 citation statements)

References 30 publications

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“…Specialized databases such as the TCDB (http:// www.tcdb.org) were used to classify functional groups of proteins. Context analysis and pathway reconstruction were done using the SEED 47 and KEGG resources. Putative frameshifts were checked and corrected manually.…”

Section: Methodsmentioning

confidence: 99%

Genome sequence of the bioplastic-producing “Knallgas” bacterium Ralstonia eutropha H16

et al. 2006

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Section: Methodsmentioning

confidence: 99%

Genome sequence of the bioplastic-producing “Knallgas” bacterium Ralstonia eutropha H16

et al. 2006

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“…For the functional characterization of subspecies, we annotated the genes to functional categories (Materials and Methods) and ascertained whether these were specific to either SC or SSSC, by testing for enrichment of functional annotations such as SEED (Overbeek et al , 2005) pathways, eggNOG (Huerta‐Cepas et al , 2016b) categories, and KEGG (Kanehisa & Goto, 2000) modules in all the species with subspecies structure (Fig EV3). Compared to the combined SCs, the combined SSSCs were enriched in genes functionally related to phages (including genes for integration and excision), cell surface protein modifications (such as genes from the sortase pathway), chemotaxis, and motility as well as genes of unknown function (Table EV6).…”

Section: Resultsmentioning

confidence: 99%

“…Annotation of the reconstructed gene complements was transferred from a published annotation of the IGC (Kultima et al , 2016). From this study, we specifically used annotations to eggNOG (Huerta‐Cepas et al , 2016b), KEGG (Kanehisa & Goto, 2000), and SEED (Overbeek et al , 2005) as indicated in the main text.…”

Section: Methodsmentioning

confidence: 99%

Subspecies in the global human gut microbiome

Costea

Coelho

Sunagawa

et al. 2017

Molecular Systems Biology

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Population genomics of prokaryotes has been studied in depth in only a small number of primarily pathogenic bacteria, as genome sequences of isolates of diverse origin are lacking for most species. Here, we conducted a large‐scale survey of population structure in prevalent human gut microbial species, sampled from their natural environment, with a culture‐independent metagenomic approach. We examined the variation landscape of 71 species in 2,144 human fecal metagenomes and found that in 44 of these, accounting for 72% of the total assigned microbial abundance, single‐nucleotide variation clearly indicates the existence of sub‐populations (here termed subspecies). A single subspecies (per species) usually dominates within each host, as expected from ecological theory. At the global scale, geographic distributions of subspecies differ between phyla, with Firmicutes subspecies being significantly more geographically restricted. To investigate the functional significance of the delineated subspecies, we identified genes that consistently distinguish them in a manner that is independent of reference genomes. We further associated these subspecies‐specific genes with properties of the microbial community and the host. For example, two of the three Eubacterium rectale subspecies consistently harbor an accessory pro‐inflammatory flagellum operon that is associated with lower gut community diversity, higher host BMI, and higher blood fasting insulin levels. Using an additional 676 human oral samples, we further demonstrate the existence of niche specialized subspecies in the different parts of the oral cavity. Taken together, we provide evidence for subspecies in the majority of abundant gut prokaryotes, leading to a better functional and ecological understanding of the human gut microbiome in conjunction with its host.

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“…1a,c), which is analogous to the comparative microbial genome annotation approach 13 that has enabled accelerated annotation by propagation of refinements to one genome to others. First, we downloaded draft GENREs using Model SEED 14 and KBase (US Department of Energy Systems Biology Knowledgebase, http://kbase.us).…”

Section: Metabolic Reconstruction Pipelinementioning

confidence: 99%

Generation of genome-scale metabolic reconstructions for 773 members of the human gut microbiota

et al. 2016

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1 r e s o u r c eChanges in the composition of the human gut microbiota have been associated with the development of chronic diseases including type 2 diabetes, obesity, and colorectal cancer 1 . Gut bacterial functions, such as synthesis of amino acids and vitamins 2 , breakdown of indigestible plant polysaccharides 3 , and production of metabolites involved in energy metabolism 4 , have been linked to human health. The use of 'omics approaches to study human microbiome communities has led to the generation of enormous data sets whose interpretations require systems biology tools to shed light on the functional capacity of gut microbiomes and their interactions with the human host 5 .In order to infer the metabolic repertoire of a gut metagenome data set, researchers usually map sequenced genes or organisms onto metabolic networks derived from the Kyoto Encyclopedia of Genes and Genomes (KEGG) 6 , and functional annotations from KEGG ontologies 7 . However, this approach cannot identify the contribution of each bacterial species to the metabolic repertoire of the whole gut microbiome, nor can it infer the effects of different gut microbial communities on host metabolism.A technique that can bridge this gap is constraint-based reconstruction and analysis (COBRA) 8 using genome-scale metabolic reconstructions (GENREs) of individual human gut microbes. GENREs are assembled using the genome sequence and experimental information 9 . These reconstructions form the basis for the development of condition-specific metabolic models whose functions are simulated and validated by comparison with experimental results. The models can be used to investigate genotype-phenotype relationships 10 , microbe-microbe interactions 11 , and host-microbe interactions 11 . Numerous tools can be used to automatically generate draft GENREs but such models contain errors 12 and are incomplete.Manual curation of draft reconstructions is time consuming because it involves an extensive literature review and experimental validation of metabolic functions 9 .To provide an extensive resource of GENREs for human gut microbes, we developed a comparative metabolic reconstruction method that enables any refinement to one metabolic reconstruction to be propagated to others. This accelerates reconstruction and improves model quality. We generated AGORA, which includes 773 gut microbes, comprising 205 genera and 605 species. All reconstructions were based on literature-derived experimental data and comparative genomics. The metabolic predictions of two AGORA reconstructions and their derived metabolic models were validated against experimental data. RESULTS Metabolic reconstruction pipelineWe devised a comparative metabolic reconstruction method (Fig. 1a,c), which is analogous to the comparative microbial genome annotation approach 13 that has enabled accelerated annotation by propagation of refinements to one genome to others. First, we downloaded draft GENREs using Model SEED 14 and KBase (US Department of Energy Systems Biology Knowledgebase, http:/...

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The Subsystems Approach to Genome Annotation and its Use in the Project to Annotate 1000 Genomes

Cited by 1,792 publications

References 30 publications

Genome sequence of the bioplastic-producing “Knallgas” bacterium Ralstonia eutropha H16

Genome sequence of the bioplastic-producing “Knallgas” bacterium Ralstonia eutropha H16

Subspecies in the global human gut microbiome

Generation of genome-scale metabolic reconstructions for 773 members of the human gut microbiota

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