PHYLOViZ: phylogenetic inference and data visualization for sequence based typing methods

There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

Abstract

Background

With the decrease of DNA sequencing costs, sequence-based typing methods are rapidly becoming the gold standard for epidemiological surveillance. These methods provide reproducible and comparable results needed for a global scale bacterial population analysis, while retaining their usefulness for local epidemiological surveys. Online databases that collect the generated allelic profiles and associated epidemiological data are available but this wealth of data remains underused and are frequently poorly annotated since no user-friendly tool exists to analyze and explore it.

Results

PHYLOViZ is platform independent Java software that allows the integrated analysis of sequence-based typing methods, including SNP data generated from whole genome sequence approaches, and associated epidemiological data. goeBURST and its Minimum Spanning Tree expansion are used for visualizing the possible evolutionary relationships between isolates. The results can be displayed as an annotated graph overlaying the query results of any other epidemiological data available.

Conclusions

PHYLOViZ is a user-friendly software that allows the combined analysis of multiple data sources for microbial epidemiological and population studies. It is freely available at http://www.phyloviz.net.

Related collections

Most cited references 12

Record: found
Abstract: found
Article: not found

eBURST: inferring patterns of evolutionary descent among clusters of related bacterial genotypes from multilocus sequence typing data.

E Feil, B. C. Li, D M Aanensen … (2004)

The introduction of multilocus sequence typing (MLST) for the precise characterization of isolates of bacterial pathogens has had a marked impact on both routine epidemiological surveillance and microbial population biology. In both fields, a key prerequisite for exploiting this resource is the ability to discern the relatedness and patterns of evolutionary descent among isolates with similar genotypes. Traditional clustering techniques, such as dendrograms, provide a very poor representation of recent evolutionary events, as they attempt to reconstruct relationships in the absence of a realistic model of the way in which bacterial clones emerge and diversify to form clonal complexes. An increasingly popular approach, called BURST, has been used as an alternative, but present implementations are unable to cope with very large data sets and offer crude graphical outputs. Here we present a new implementation of this algorithm, eBURST, which divides an MLST data set of any size into groups of related isolates and clonal complexes, predicts the founding (ancestral) genotype of each clonal complex, and computes the bootstrap support for the assignment. The most parsimonious patterns of descent of all isolates in each clonal complex from the predicted founder(s) are then displayed. The advantages of eBURST for exploring patterns of evolutionary descent are demonstrated with a number of examples, including the simple Spain(23F)-1 clonal complex of Streptococcus pneumoniae, "population snapshots" of the entire S. pneumoniae and Staphylococcus aureus MLST databases, and the more complicated clonal complexes observed for Campylobacter jejuni and Neisseria meningitidis.

0 comments Cited 607 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: found
Article: not found

Multilocus sequence typing: a portable approach to the identification of clones within populations of pathogenic microorganisms.

M. C. J. Maiden, J. Bygraves, E Feil … (1998)

Traditional and molecular typing schemes for the characterization of pathogenic microorganisms are poorly portable because they index variation that is difficult to compare among laboratories. To overcome these problems, we propose multilocus sequence typing (MLST), which exploits the unambiguous nature and electronic portability of nucleotide sequence data for the characterization of microorganisms. To evaluate MLST, we determined the sequences of approximately 470-bp fragments from 11 housekeeping genes in a reference set of 107 isolates of Neisseria meningitidis from invasive disease and healthy carriers. For each locus, alleles were assigned arbitrary numbers and dendrograms were constructed from the pairwise differences in multilocus allelic profiles by cluster analysis. The strain associations obtained were consistent with clonal groupings previously determined by multilocus enzyme electrophoresis. A subset of six gene fragments was chosen that retained the resolution and congruence achieved by using all 11 loci. Most isolates from hyper-virulent lineages of serogroups A, B, and C meningococci were identical for all loci or differed from the majority type at only a single locus. MLST using six loci therefore reliably identified the major meningococcal lineages associated with invasive disease. MLST can be applied to almost all bacterial species and other haploid organisms, including those that are difficult to cultivate. The overwhelming advantage of MLST over other molecular typing methods is that sequence data are truly portable between laboratories, permitting one expanding global database per species to be placed on a World-Wide Web site, thus enabling exchange of molecular typing data for global epidemiology via the Internet.

0 comments Cited 604 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: found
Article: not found

Sequence type analysis and recombinational tests (START).

K Jolley, E Feil, M S Chan … (2001)

The 32-bit Windows application START is implemented using Visual Basic and C(++) and performs analyses to aid in the investigation of bacterial population structure using multilocus sequence data. These analyses include data summary, lineage assignment, and tests for recombination and selection. START is available at http://outbreak.ceid.ox.ac.uk/software.htm. keith.jolley@ceid.ox.ac.uk

0 comments Cited 140 times – based on 0 reviews      Review now

Bookmark

All references

Author and article information

Journal

Journal ID (nlm-ta): BMC Bioinformatics

Journal ID (iso-abbrev): BMC Bioinformatics

Title: BMC Bioinformatics

Publisher: BioMed Central

ISSN (Electronic): 1471-2105

Publication date Collection: 2012

Publication date (Electronic): 8 May 2012

Volume: 13

Page: 87

Affiliations

[1 ]KDBIO, INESC-ID, , R. Alves Redol 9, 1000-029 Lisboa, Portugal

[2 ]CSE Dept, IST, Tech Univ of Lisbon, Av. Rovisco Pais 1, 1049-001 Lisboa, Portugal

[3 ]DEETC, ISEL, Poly Inst of Lisbon, R. Cons. Emdio Navarro 1, 1959-007 Lisboa, Portugal

[4 ]Inst de Microbiologia, Inst de Medicina Molecular, Fac de Medicina, Univ of Lisbon, Av. Prof. Egas Moniz, 1649-028 Lisboa, Portugal

[5 ]Instituto Gulbenkian de Ciência, 2781-901 Oeiras, Portugal

Article

Publisher ID: 1471-2105-13-87

DOI: 10.1186/1471-2105-13-87

PMC ID: 3403920

PubMed ID: 22568821

SO-VID: 6bf1a273-d546-4294-a7ed-7fe09651f65a

License:

This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

PHYLOViZ: phylogenetic inference and data visualization for sequence based typing methods

Read this article at

Abstract

Background

Results

Conclusions

Related collections

REPO4EU WP2 Databases

Most cited references 12

eBURST: inferring patterns of evolutionary descent among clusters of related bacterial genotypes from multilocus sequence typing data.

Multilocus sequence typing: a portable approach to the identification of clones within populations of pathogenic microorganisms.

Sequence type analysis and recombinational tests (START).

Author and article information

Journal

Affiliations

Article

History

Categories

Comments

Comment on this article

Similar content 210

Cited by 211

Most referenced authors 832