TIGRFAMs and Genome Properties: tools for the assignment of molecular function and biological process in prokaryotic genomes

Selengut, Jeremy D.; Haft, Daniel H.; Davidsen, Tanja M.; Ganapathy, Anurhada; Gwinn-Giglio, Michelle; Nelson, Karen E.; Richter, Alexander; White, Owen

doi:10.1093/nar/gkl1043

Cited by 275 publications

(232 citation statements)

References 9 publications

Supporting

Mentioning

230

Contrasting

Unclassified

Order By: Relevance

“…We then tested each module for over-represented functions and phenotypes. Functional annotations for genes were gathered from several manually curated functional databases (30)(31)(32). Phenotypic annotations for species were gathered as described above.…”

Section: Resultsmentioning

confidence: 99%

“…Modules were checked for over-represented functions by using various manually curated functional ontologies (Gene Ontology molecular function and biological process ontologies (30), TIGR Role Categories (32), and COG (31)), as well as the engineering ontology that we developed (SI Methods, Tables S2-S4). To determine which ontology is most predictive of gene module membership, we adapted a method that scores the mutual information between cluster membership and known gene attributes (33), as follows.…”

Section: Identifying Evolutionary Modulesmentioning

confidence: 99%

See 1 more Smart Citation

Modularity of stress response evolution

Singh

Wolf

Wang

et al. 2008

Proc. Natl. Acad. Sci. U.S.A.

View full text Add to dashboard Cite

Responses to extracellular stress directly confer survival fitness by means of complex regulatory networks. Despite their complexity, the networks must be evolvable because of changing ecological and environmental pressures. Although the regulatory networks underlying stress responses are characterized extensively, their mechanism of evolution remains poorly understood. Here, we examine the evolution of three candidate stress response networks (chemotaxis, competence for DNA uptake, and endospore formation) by analyzing their phylogenetic distribution across several hundred diverse bacterial and archaeal lineages. We report that genes in the chemotaxis and sporulation networks group into well defined evolutionary modules with distinct functions, phenotypes, and substitution rates as compared with control sets of randomly chosen genes. The evolutionary modules vary in both number and cohesiveness among the three pathways. Chemotaxis has five coherent modules whose distribution among species shows a clear pattern of interdependence and rewiring. Sporulation, by contrast, is nearly monolithic and seems to be inherited vertically, with three weak modules constituting early and late stages of the pathway. Competence does not seem to exhibit well defined modules either at or below the pathway level. Many of the detected modules are better understood in engineering terms than in protein functional terms, as we demonstrate using a control-based ontology that classifies gene function according to roles such as ''sensor,'' ''regulator,'' and ''actuator.'' Moreover, we show that combinations of the modules predict phenotype, yet surprisingly do not necessarily correlate with phylogenetic inheritance. The architectures of these three pathways are therefore emblematic of different modes and constraints on evolution.chemotaxis ͉ competence ͉ module ͉ regulatory ͉ sporulation

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Identifying Evolutionary Modulesmentioning

confidence: 99%

Modularity of stress response evolution

Singh

Wolf

Wang

et al. 2008

Proc. Natl. Acad. Sci. U.S.A.

View full text Add to dashboard Cite

show abstract

“…Stand-alone RPSBlast was used to align reads (translated into all six possible reading frames) to protein profiles (represented by position-specific scoring matrices). Queries were performed against the complete conserved domains database (Marchler-Bauer et al, 2009) and against the COGs (Tatusov et al, 2003) and Tigrfams (Selengut et al, 2007) databases. Fractions of sequences assigned in each case are shown in Supplementary Table 2.…”

Section: Sequence Analysismentioning

confidence: 99%

The oral metagenome in health and disease

Belda-Ferre¹,

Alcaraz²,

et al. 2011

View full text Add to dashboard Cite

The oral cavity of humans is inhabited by hundreds of bacterial species and some of them have a key role in the development of oral diseases, mainly dental caries and periodontitis. We describe for the first time the metagenome of the human oral cavity under health and diseased conditions, with a focus on supragingival dental plaque and cavities. Direct pyrosequencing of eight samples with different oral-health status produced 1 Gbp of sequence without the biases imposed by PCR or cloning. These data show that cavities are not dominated by Streptococcus mutans (the species originally identified as the ethiological agent of dental caries) but are in fact a complex community formed by tens of bacterial species, in agreement with the view that caries is a polymicrobial disease. The analysis of the reads indicated that the oral cavity is functionally a different environment from the gut, with many functional categories enriched in one of the two environments and depleted in the other. Individuals who had never suffered from dental caries showed an overrepresentation of several functional categories, like genes for antimicrobial peptides and quorum sensing. In addition, they did not have mutans streptococci but displayed high recruitment of other species. Several isolates belonging to these dominant bacteria in healthy individuals were cultured and shown to inhibit the growth of cariogenic bacteria, suggesting the use of these commensal bacterial strains as probiotics to promote oral health and prevent dental caries.

show abstract

“…Large-scale databases that process and deposit metagenomic datasets include centralized servers like MG-RAST, IMG/M and CAMERA [57][58][59]. Reference databases like KEGG [60], eggNOG [61], COG/KOG [62], PFAM [63] and TIGRFAM [64] can be used to give functional context to the metagenome data after assembly. In addition one can also perform the analysis of metagenomic data without depending on the centralized servers if we have strong bioinformatic skills using different software based on UNIX/Linux platforms ( Table 2).…”

Section: Metagenomicsmentioning

confidence: 99%