Motivation: Deep metagenomic sequencing of biological samples has the potential to recover otherwise difficult-to-detect microorganisms and accurately characterize biological samples with limited prior knowledge of sample contents. Existing metagenomic taxonomic classification algorithms, however, do not scale well to analyze large metagenomic datasets, and balancing classification accuracy with computational efficiency presents a fundamental challenge.Results: A method is presented to shift computational costs to an off-line computation by creating a taxonomy/genome index that supports scalable metagenomic classification. Scalable performance is demonstrated on real and simulated data to show accurate classification in the presence of novel organisms on samples that include viruses, prokaryotes, fungi and protists. Taxonomic classification of the previously published 150 giga-base Tyrolean Iceman dataset was found to take <20 h on a single node 40 core large memory machine and provide new insights on the metagenomic contents of the sample.Availability: Software was implemented in C++ and is freely available at http://sourceforge.net/projects/lmatContact: allen99@llnl.govSupplementary information: Supplementary data are available at Bioinformatics online.

The increasing interest in the potential use of scal incentives as a mechanism for stimulating urban renewal has been highlighted by a number of in uential policy sources. This paper assesses the application and outcomes of tax-based incentives in urban regeneration, with particular focus upon the differing models represented by Dublin (Ireland) and Chicago (USA). Issues considered include utilisation of tax incentives, drawing-down of bene ts, role of actor groups, ability to lever private-sector nance, impact on property market performance and wider economic in uences. Conclusions advance the case for tax-based mechanisms as an instrument in the delivery of urban regeneration but stress the need for complementary structures to exploit fully the scal incentives.

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