eBURST: Inferring Patterns of Evolutionary Descent among Clusters of Related Bacterial Genotypes from Multilocus Sequence Typing Data

By E. Feil, Bao C. Li, David M. Aanensen, W. Hanage, B. Spratt. Journal of Bacteriology, 2004

1,914 citations recorded by the source corpus.

Subfields: Epidemiology, Infectious Diseases, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/770d91b5e75db8f5c7a87bf75d35cbcbed4db1af

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Citations in the corpus

Inference of Bacterial Microevolution Using Multilocus Sequence Data (2007, methodology): “et al., 2001; Feil et al., 2004; Spratt et al., 2004).”
The EnteroBase user's guide, with case studies on Salmonella transmissions, Yersinia pestis phylogeny, and Escherichia core genomic diversity (2019, background): “Genome Research 139 www.genome.org approach (Feil et al. 2004) and into their equivalent eBurst groups (eBGs) in Salmonella (Achtman et al. 2012).”
Fast and flexible bacterial genomic epidemiology with PopPUNK (2018, methodology): “Furthermore, as each MLST label is made up from several separate allele labels from each gene in the scheme, minimum-spanning trees can be generated using eBURST (Feil et al. 2004) .”
Multilocus Sequence Typing as a Replacement for Serotyping in Salmonella enterica (2012, background): “the bottom-up definition of eBurst groups on the basis of allelic identity rather than sequence identity [46,47] tends to yield discrete clusters of related organisms even at medium levels of”
BIGSdb: Scalable analysis of bacterial genome variation at the population level (2010, methodology): “The method can utilize the array of analysis tools developed or applicable for analysing MLST data [24,57-60] with the loci chosen to provide the level of discrimination required or to focus on particular aspects of the biology of the organism.”
GrapeTree: visualization of core genomic relationships among 100,000 bacterial pathogens (2017, positive): “During the construction of an MSTree, Bionumerics or goeBURST chooses between multiple co-optimal branches by tie-breaking according to the principles of eBURST (Feil et al. 2004) as summarized and extended by Francisco et al. (2009).”
Sex and virulence in Escherichia coli: an evolutionary perspective (2006, methodology): “…278 STs fall into groups of related STs. Related STs were assigned to 23 so-called ST complexes, using the principles of the eBurst algorithm (Feil et al., 2004): each ST complex includes at least three STs that differ from their nearest neighbour by no more than two of the seven loci while…”
PHYLOViZ: phylogenetic inference and data visualization for sequence based typing methods (2012, methodology): “analysis, including the algorithms START [13], eBURST [14] and goeBURST [15].”
Multiresistant Gram-negative bacteria: the role of high-risk clones in the dissemination of antibiotic resistance. (2011, background): “The relationships between the various STs and CCs can conveniently be depicted by e-BURST diagrams (Feil et al., 2004).”
Pseudomonas aeruginosa Evolutionary Adaptation and Diversification in Cystic Fibrosis Chronic Lung Infections (2016, methodology): “The figure shows a population structure based on 15 variable traits using the eBURST algorithm [83].”
PHYLOViZ 2.0: providing scalable data integration and visualization for multiple phylogenetic inference methods (2017, methodology): “Several tools are available for data analysis and visualization of microbial typing data, such as START (Jolley, 2001), eBURST (Feil et al., 2004) and goeBURST (Francisco et al.”