BACKGROUND: The digitization of biodiversity data is leading to the widespread application of taxon names that are superfluous, ambiguous or incorrect, resulting in mismatched records and inflated species numbers. The ultimate consequences of misspelled names and bad taxonomy are erroneous scientific conclusions and faulty policy decisions. The lack of tools for correcting this 'names problem' has become a fundamental obstacle to integrating disparate data sources and advancing the progress of biodiversity science. RESULTS: The TNRS, or Taxonomic Name Resolution Service, is an online application for automated and user-supervised standardization of plant scientific names. The TNRS builds upon and extends existing open-source applications for name parsing and fuzzy matching. Names are standardized against multiple reference taxonomies, including the Missouri Botanical Garden's Tropicos database. Capable of processing thousands of names in a single operation, the TNRS parses and corrects misspelled names and authorities, standardizes variant spellings, and converts nomenclatural synonyms to accepted names. Family names can be included to increase match accuracy and resolve many types of homonyms. Partial matching of higher taxa combined with extraction of annotations, accession numbers and morphospecies allows the TNRS to standardize taxonomy across a broad range of active and legacy datasets. CONCLUSIONS: We show how the TNRS can resolve many forms of taxonomic semantic heterogeneity, correct spelling errors and eliminate spurious names. As a result, the TNRS can aid the integration of disparate biological datasets. Although the TNRS was developed to aid in standardizing plant names, its underlying algorithms and design can be extended to all organisms and nomenclatural codes. The TNRS is accessible via a web interface at http://tnrs.iplantcollaborative.org/ and as a RESTful web service and application programming interface. Source code is available at https://github.com/iPlantCollaborativeOpenSource/TNRS/.

Abstract Summary: PhyloWidget is a web-based tool for the visualization and manipulation of phylogenetic tree data. It can be accessed online or downloaded as a standalone application. A simple URL-based API allows databases to easily link to and customize PhyloWidget for interactively viewing medium- to large-sized trees. Availability: PhyloWidget is available for online use or download at http://www.phylowidget.org/. Its source code is released under the GNU General Public License. Contact: [email protected]

Currently, phylogenetic tree techniques are being used in multiple areas, from Tree of Life problems to pathogen recognition to drug discovery. With all of these applications for phylogenetic tree techniques, methods are needed to exploit the knowledge modeled in phylogenetic trees more thoroughly. One such information point of interest is the behavior of frequent patterns in phylogenetic trees. While there are many techniques that look at maximal, consensus and supertreepatterns, there are few techniques that look at frequent, but not maximal pattern. This demonstration paper presents PhyloMiner, a tool that automatically discovers frequent agreement subtrees from multiple phylogenies. It introduces this topic of frequent agreement subtrees and then concludes with describing the PhyloMiner tool that implements these concepts and is available freely on the World Wide Web.

We examine the organizational impact on phylogenetic databases of the increasing sophistication in the need and use of phylogenetic data. A primary issue is the use of the unnormalized representation of phylogenies in Newick format as a primitive data type in existing phylogenetic databases. In particular we identify and enumerate a list of potential applications of such databases and queries (use-cases) that biologists may wish to see integrated into a phylogenetic database management system. We show there are many queries that would best be supported by a normalized data model where phylogenies are stored as lists of edges. Since many of the queries require transitive traversals of the phylogenies we demonstrate, constructively, that complex phylogenetic queries can be conveniently constructed as Datalog programs. We address concerns with respect to the cost and performance of the normalized representation by developing and empirically evaluating a feasibility prototype.

Phylogenetic trees are unordered labeled trees in which each leaf node has a label and the order among siblings is unimportant. In this paper we propose a new similarity measure, called TreeRank, for phylogenetic trees and present an algorithm for computing TreeRank scores. Given a query or pattern tree P and a data tree D, the TreeRank score from P to D is a measure of the topological relationships in P that are found to be the same or similar in D. The proposed algorithm calculates the TreeRank score in O(M/sup 2/ + N) time where M is the number of nodes appearing in both P and D, and N is the number of nodes in D. We then develop a search engine that, given a query or pattern tree P and a database of trees D, finds and ranks the nearest neighbors of P in D where the "nearness" is measured by the proposed similarity function. This structure-based search engine is fully operational and is available on the World Wide Web.

MOTIVATION: A noble and ultimate objective of phyloinformatic research is to assemble, synthesize, and explore the evolutionary history of life on earth. Data mining methods for performing these tasks are not yet well developed, but one avenue of research suggests that network connectivity dynamics will play an important role in future methods. Analysis of disordered networks, such as small-world networks, has applications as diverse as disease propagation, collaborative networks, and power grids. Here we apply similar analyses to networks of phylogenetic trees in order to understand how synthetic information can emerge from a database of phylogenies. RESULTS: Analyses of tree network connectivity in TreeBASE show that a collection of phylogenetic trees behaves as a small-world network-while on the one hand the trees are clustered, like a non-random lattice, on the other hand they have short characteristic path lengths, like a random graph. Tree connectivities follow a dual-scale power-law distribution (first power-law exponent approximately 1.87; second approximately 4.82). This unusual pattern is due, in part, to the presence of alternative tree topologies that enter the database with each published study. As expected, small collections of trees decrease connectivity as new trees are added, while large collections of trees increase connectivity. However, the inflection point is surprisingly low: after about 600 trees the network suddenly jumps to a higher level of coherence. More stringent definitions of 'neighbour' greatly delay the threshold whence a database achieves sufficient maturity for a coherent network to emerge. However, more stringent definitions of 'neighbour' would also likely show improved focus in data mining. AVAILABILITY: http://treebase.org

Phylogenetic trees are essential for understanding the relationships among organisms or taxa. Many of the current techniques for searching phylogenetic repositories allow the user to perform a keyword-type search or an aligned sequence data search, or to browse a hierarchical list of taxa. Here we describe a new search engine that allows the user to present an example phylogeny, or a query tree, and then searches a phylogenetic database for trees that contain the query structure. The presented search engine is fully operational and is available on the World Wide Web.