Motivation: We explored the feasibility of using unaligned rRNA gene sequences as DNA barcodes, based on correlation analysis of composition vectors derived from nucleotide strings. We tested this method with seven rRNA (including 12S, 16S, 18S, 26S and 28S) datasets from a wide variety of organisms (from archaea to tetrapods) at taxonomic levels ranging from class to species. Result: Our results indicate that grouping of taxa based on composition vector analysis is always in good agreement with the phylogenetic trees generated by traditional approaches, although in some cases the relationships among the higher systemic groups may differ. The effectiveness of our analysis might be related to the length and divergence among sequences in a dataset. Nevertheless, the correct grouping of sequences and accurate assignment of unknown taxa make our analysis a reliable and convenient approach in analyzing unaligned sequence datasets of various rRNAs for barcoding purposes. Availability: The newly designed software (CVTree 1.0) is publicly available at the Composition Vector Tree (CVTree) web server

Mathematical characterizations of biological sequences form one of the main elements of bioinformatics. In this work, a class of DNA sequence characterization, namely computational genomics signatures, which capture global features of these sequences is used to address emerging computational biology challenges. Because of the species specificity and pervasiveness of genome signatures, it is possible to use these signatures to characterize and identify a genome or a taxonomic unit using a short genome fragment from that source. However, the identification accuracy is generally poor when the sequence model and the sequence distance measure are not selected carefully. We show that the use of relative distance measures instead of absolute metrics makes it possible to obtain better detection accuracy. Furthermore, the use of relative metrics can create opportunities for using more complex models to develop genome signatures, which cannot be used efficiently when conventional distance measures are used. Using a relative distance measure and a model based on the relative abundance

doi:10.1093/nar/gkl938 Power and limitations of the chloroplast trnL (UAA) intron for plant DNA barcoding