Biomedical subjects

Timothy G Lilburn

Publications and source records attributed to Timothy G Lilburn.

4 recordsLinked to original sources

PubMed2007

Genome evolution and functional divergence in Yersinia.

The steadily increasing number of prokaryotic genomes has accelerated the study of genome evolution; in particular, the availability of sets of genomes from closely related bacteria has made exploration of questions surrounding the evolution of pathogenesis tractable. Here we present the results of a detailed comparison of the genomes of Yersinia pseudotuberculosis IP32593 and three strains of Yersinia pestis (CO92, KIM10, and 91001). There appear to be between 241 and 275 multigene families in these organisms. There are 2,568 genes that are identical in the three Y. pestis strains, but differ from the Y. pseudotuberculosis strain. The changes found in some of these families, such as the kinases, proteases, and transporters, are illustrative of how the evolutionary jump from the free-living enteropathogen Y. pseudotuberculosis to the obligate host-borne blood pathogen Y. pestis was achieved. We discuss the composition of some of the most important families and discuss the observed divergence between Y. pseudotuberculosis and Y. pestis homologs.

Base Sequence↗

PubMed2006

Computational aspects of systematic biology.

We review the resources available to systematic biologists who wish to use computers to build classifications. Algorithm development is in an early stage, and only a few examples of integrated applications for systematic biology are available. The availability of data is crucial if systematic biology is to enter the computer age.

Algorithms↗

PubMed2005

Self-organizing and self-correcting classifications of biological data.

MOTIVATION: Rapid, automated means of organizing biological data are required if we hope to keep abreast of the flood of data emanating from sequencing, microarray and similar high-throughput analyses. Faced with the need to validate the annotation of thousands of sequences and to generate biologically meaningful classifications based on the sequence data, we turned to statistical methods in order to automate these processes. RESULTS: An algorithm for automated classification based on evolutionary distance data was written in S. The algorithm was tested on a dataset of 1436 small subunit ribosomal RNA sequences and was able to classify the sequences according to an extant scheme, use statistical measurements of group membership to detect sequences that were misclassified within this scheme and produce a new classification. In this study, the use of the algorithm to address problems in prokaryotic taxonomy is discussed. AVAILABILITY: S-Plus is available from Insightful, Inc. An S-Plus implementation of the algorithm and the associated data are available at http://taxoweb.mmg.msu.edu/datasets

Algorithms↗

PubMed2004

Exploring prokaryotic taxonomy.

Techniques drawn from exploratory data analysis, using tools found in the S-Plus statistical software package, have been used to inspect and maintain the Bergey's Taxonomic Outline and to move towards an automated and community-based means of working on the outline. These techniques can be used to classify sequences from unnamed and uncultured organisms, to visualize errors in the taxonomy or in the curation of the sequences, to suggest emendations to the taxonomy or to the classification of extant species and to complement the visualization of phylogenies based on treeing methods. A dataset of more than 9200 aligned small-subunit rRNA sequences was analysed in the context of the current taxonomic outline. The use of the algorithm in exploring and modifying the taxonomy is illustrated with an example drawn from the family Comamonadaceae.

Bacteria↗