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Joseph Mellor

Publications and source records attributed to Joseph Mellor.

3 recordsLinked to original sources

Comparative assessment of performance and genome dependence among phylogenetic profiling methods.

BACKGROUND: The rapidly increasing speed with which genome sequence data can be generated will be accompanied by an exponential increase in the number of sequenced eukaryotes. With the increasing number of sequenced eukaryotic genomes comes a need for bioinformatic techniques to aid in functional annotation. Ideally, genome context based techniques such as proximity, fusion, and phylogenetic profiling, which have been so successful in prokaryotes, could be utilized in eukaryotes. Here we explore the application of phylogenetic profiling, a method that exploits the evolutionary co-occurrence of genes in the assignment of functional linkages, to eukaryotic genomes. RESULTS: In order to evaluate the performance of phylogenetic profiling in eukaryotes, we assessed the relative performance of commonly used profile construction techniques and genome compositions in predicting functional linkages in both prokaryotic and eukaryotic organisms. When predicting linkages in E. coli with a prokaryotic profile, the use of continuous values constructed from transformed BLAST bit-scores performed better than profiles composed of discretized E-values; the use of discretized E-values resulted in more accurate linkages when using S. cerevisiae as the query organism. Extending this analysis by incorporating several eukaryotic genomes in profiles containing a majority of prokaryotes resulted in similar overall accuracy, but with a surprising reduction in pathway diversity among the most significant linkages. Furthermore, the application of phylogenetic profiling using profiles composed of only eukaryotes resulted in the loss of the strong correlation between common KEGG pathway membership and profile similarity score. Profile construction methods, orthology definitions, ontology and domain complexity were explored as possible sources of the poor performance of eukaryotic profiles, but with no improvement in results. CONCLUSION: Given the current set of completely sequenced eukaryotic organisms, phylogenetic profiling using profiles generated from any of the commonly used techniques was found to yield extremely poor results. These findings imply genome-specific requirements for constructing functionally relevant phylogenetic profiles, and suggest that differences in the evolutionary history between different kingdoms might generally limit the usefulness of phylogenetic profiling in eukaryotes.

Bacterial Proteins↗

VisANT: an online visualization and analysis tool for biological interaction data.

BACKGROUND: New techniques for determining relationships between biomolecules of all types--genes, proteins, noncoding DNA, metabolites and small molecules--are now making a substantial contribution to the widely discussed explosion of facts about the cell. The data generated by these techniques promote a picture of the cell as an interconnected information network, with molecular components linked with one another in topologies that can encode and represent many features of cellular function. This networked view of biology brings the potential for systematic understanding of living molecular systems. RESULTS: We present VisANT, an application for integrating biomolecular interaction data into a cohesive, graphical interface. This software features a multi-tiered architecture for data flexibility, separating back-end modules for data retrieval from a front-end visualization and analysis package. VisANT is a freely available, open-source tool for researchers, and offers an online interface for a large range of published data sets on biomolecular interactions, including those entered by users. This system is integrated with standard databases for organized annotation, including GenBank, KEGG and SwissProt. VisANT is a Java-based, platform-independent tool suitable for a wide range of biological applications, including studies of pathways, gene regulation and systems biology. CONCLUSION: VisANT has been developed to provide interactive visual mining of biological interaction data sets. The new software provides a general tool for mining and visualizing such data in the context of sequence, pathway, structure, and associated annotations. Interaction and predicted association data can be combined, overlaid, manipulated and analyzed using a variety of built-in functions. VisANT is available at http://visant.bu.edu.

Animals↗

Comparisons of predicted genetic modules: identification of co-expressed genes through module gene flow.

A question of fundamental importance is the definition and identification of modules from microarray experiments. A wide variety of techniques have been used to gain insight into the elucidation of such modules. One problem, however, is the inability to directly compare results between the different data sets produced due to the inherent parameterizations of their approaches. We first aim to provide a mechanism by which different approaches to module finding can be directly compared. Moreover, the same approach can be used to internally compare the modules predicted by the same technique, but at different parameterizations. We apply this approach to analyze the flow of genes through modules at different module thresholds of the Barkai Signature method, thereby further resolving the modules into sets of co-expressed genes.

Algorithms↗