PhyloFinder: An intelligent search engine for phylogenetic tree databases

Department of Computer Science, Iowa State University, Ames, IA 50011, USA.
BMC Evolutionary Biology (Impact Factor: 3.41). 02/2008; 8:90. DOI: 10.1186/1471-2148-8-90
Source: PubMed

ABSTRACT Bioinformatic tools are needed to store and access the rapidly growing phylogenetic data. These tools should enable users to identify existing phylogenetic trees containing a specified taxon or set of taxa and to compare a specified phylogenetic hypothesis to existing phylogenetic trees.
PhyloFinder is an intelligent search engine for phylogenetic databases that we have implemented using trees from TreeBASE. It enables taxonomic queries, in which it identifies trees in the database containing the exact name of the query taxon and/or any synonymous taxon names, and it provides spelling suggestions for the query when there is no match. Additionally, PhyloFinder can identify trees containing descendants or direct ancestors of the query taxon. PhyloFinder also performs phylogenetic queries, in which it identifies trees that contain the query tree or topologies that are similar to the query tree.
PhyloFinder can enhance the utility of any tree database by providing tools for both taxonomic and phylogenetic queries as well as visualization tools that highlight the query results and provide links to NCBI and TBMap. An implementation of PhyloFinder using trees from TreeBASE is available from the web client application found in the availability and requirements section.

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    ABSTRACT: TreeBASE, the only data repository for phylogenetic studies, is not being used effectively since it does not meet the taxonomic data retrieval requirements of the systematics community. We show, through an examination of the queries performed on TreeBASE, that data retrieval using taxon names is unsatisfactory. We report on a new wrapper supporting taxon queries on TreeBASE by utilising a Taxonomy and Classification Database (TCl-Db) we created. TCl-Db holds merged and consolidated taxonomic names from multiple data sources and can be used to translate hierarchical, vernacular and synonym queries into specific query terms in TreeBASE. The query expansion supported by TCl-Db shows very significant information retrieval quality improvement. The wrapper can be accessed at the URL methodology we developed is scalable and can be applied to new data, as those become available in the future. Significantly improved data retrieval quality is shown for all queries, and additional flexibility is achieved via user-driven taxonomy selection.
    BMC Evolutionary Biology 02/2009; 9:93. DOI:10.1186/1471-2148-9-93 · 3.41 Impact Factor
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    ABSTRACT: Many important problems in evolutionary biology require molecular phylogenies to be reconstructed. Phylogenetic trees must then be manipulated for subsequent inclusion in publications or analyses such as supertree inference and tree comparisons. However, no tool is currently available to facilitate the management of tree collections providing, for instance: standardisation of taxon names among trees with respect to a reference taxonomy; selection of relevant subsets of trees or sub-trees according to a taxonomic query; or simply computation of descriptive statistics on the collection. Moreover, although several databases of phylogenetic trees exist, there is currently no easy way to find trees that are both relevant and complementary to a given collection of trees. We propose a tool to facilitate assessment and management of phylogenetic tree collections. Given an input collection of rooted trees, PhyloExplorer provides facilities for obtaining statistics describing the collection, correcting invalid taxon names, extracting taxonomically relevant parts of the collection using a dedicated query language, and identifying related trees in the TreeBASE database. PhyloExplorer is a simple and interactive website implemented through underlying Python libraries and MySQL databases. It is available at: and the source code can be downloaded from:
    BMC Evolutionary Biology 06/2009; 9:108. DOI:10.1186/1471-2148-9-108 · 3.41 Impact Factor
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    ABSTRACT: Researchers in Systems Biology routinely access vast collection of hidden web research resources freely available on the internet. These collections include online data repositories, online and downloadable data analysis tools, publications, text mining systems, visualization artifacts, etc. Almost always, these resources have complex data formats that are heterogeneous in representation, data type, interpretation and even identity. They are often forced to develop analysis pipelines and data management applications that involve extensive and prohibitive manual interactions. Such approaches act as a barrier for optimal use of these resources and thus impede the progress of research. In this paper, we discuss our experience of building a new middleware approach to data and application integration for Systems Biology that leverages recent developments in schema matching, wrapper generation, workflow management, and query language design. In this approach, ad hoc integration of arbitrary resources and computational pipeline construction using a declarative language is advocated. We highlight the features and advantages of this new data management system, called LifeDB, and its query language BioFlow. Based on our experience, we highlight the new challenges it raises, and potential solutions to meet these new research issues toward a viable platform for large scale autonomous data integration. We believe the research issues we raise have general interest in the autonomous data integration community and will be applicable equally to research unrelated to LifeDB.

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