Evolutionary placement of Xanthomonadales based on conserved protein signature sequences

Xanthomonadales comprises one of the largest phytopathogenic bacterial groups, and is currently classified within the gamma-proteobacteria. However, the phylogenetic placement of this group is not clearly resolved, and the results of different studies contradict one another. In this work, the evolutionary position of Xanthomonadales was determined by analyzing the presence of shared insertions and deletions (INDELs) in highly conserved proteins. Several distinctive insertions found in most of the members of the gamma-proteobacteria are absent in Xanthomonadales and groups such as Legionelalles, Chromatiales, Methylococcales, Thiotrichales and Cardiobacteriales. These INDELs were most likely introduced after the branching of Xanthomonadales from most of the gamma-proteobacteria and provide evidence for the phylogenetic placement of the early gamma-proteobacteria. Moreover, other proteins contain insertions exclusive to the Xanthomonadales order, confirming that this is a monophyletic group and provide important specific genetic markers. Thus, the data presented clearly support the Xanthomonadales group as an independent subdivision, and constitute one of the deepest branching lineage within the gamma-proteobacteria clade. (C) 2009 Elsevier Inc. All rights reserved.

HiPP: A Novel Hierarchical Point Placement Strategy and its Application to the Exploration of Document Collections

Point placement strategies aim at mapping data points represented in higher dimensions to bi-dimensional spaces and are frequently used to visualize relationships amongst data instances. They have been valuable tools for analysis and exploration of data sets of various kinds. Many conventional techniques, however, do not behave well when the number of dimensions is high, such as in the case of documents collections. Later approaches handle that shortcoming, but may cause too much clutter to allow flexible exploration to take place. In this work we present a novel hierarchical point placement technique that is capable of dealing with these problems. While good grouping and separation of data with high similarity is maintained without increasing computation cost, its hierarchical structure lends itself both to exploration in various levels of detail and to handling data in subsets, improving analysis capability and also allowing manipulation of larger data sets.