Tree Pruner: An efficient tool for selecting data from a biased genetic database

Background: Large databases of genetic data are often biased in their representation. Thus, selection of genetic data with desired properties, such as evolutionary representation or shared genotypes, is problematic. Selection on the basis of epidemiological variables may not achieve the desired properties. Available automated approaches to the selection of influenza genetic data make a tradeoff between speed and simplicity on the one hand and control over quality and contents of the dataset on the other hand. A poorly chosen dataset may be detrimental to subsequent analyses. Results: We developed a tool, Tree Pruner, for obtaining a dataset with desired evolutionary properties from a large, biased genetic database. Tree Pruner provides the user with an interactive phylogenetic tree as a means of editing the initial dataset from which the tree was inferred. The tree visualization changes dynamically, using colors and shading, reflecting Tree Pruner actions. At the end of a Tree Pruner ...

• Type: Journal
• Year: 2011
• Where: BMCBI
• Authors: Mohan Krishnamoorthy, Pragneshkumar Patel, Mira Dimitrijevic, Jonathan Dietrich, Margaret Green, Catherine Macken
• Keywords: Artificial Intelligence, BMCBI 2011, Dataset, Genetic Data, Tree Pruner