Integrating distributed data sources with OGSA-DAI DQP and VIEWS.

OGSA-DAI (Open Grid Services Architecture Data Access and Integration) is a framework for building distributed data access and integration systems. Until recently, it lacked the built-in functionality that would allow easy creation of federations of distributed data sources. The latest release of the OGSA-DAI framework introduced the OGSA-DAI DQP (Distributed Query Processing) resource. The new resource encapsulates a distributed query processor, that is able to orchestrate distributed data sources when answering declarative user queries. The query processor has many extensibility points, making it easy to customize. We have also introduced a new OGSA-DAI Views resource that provides a flexible method for defining views over relational data. The interoperability of the two new resources, together with the flexibility of the OGSA-DAI framework, allows the building of highly customized data integration solutions.

Linking pollution induced community tolerance (PICT) and microbial community structure in chronically metal polluted estuarine sediments.

We tested the ability of pollution induced community tolerance (PICT) to detect the effects of chronic metal pollution on estuarine sediment microbial communities, along a gradient spanning two orders of magnitude in metal concentrations. In tandem, we investigated the associated microbial community structure using terminal restriction fragment length polymorphism (T-RFLP). Tolerance of microbes to Cu, measured as IC50 (inhibitory concentration 50%), was strongly correlated with pore water Cu concentration (r(2)=0.842). No strong correlation existed for other metals tested, highlighting the ability of PICT to identify the pollutant causing a toxic effect. There was no correlation between microbial community structure and community tolerance to metals tested, but analysis of community structure did provide some information on reasons for observed PICT response. PICT methodology used here provided a greater strength and consistency of association with pollutant concentration compared to microbial community structure and can be recommended as a sensitive indicator of metal pollution on estuarine sediment microbial communities.

The CATH Domain Structure Database and related resources Gene3D and DHS provide comprehensive domain family information for genome analysis.

The CATH database of protein domain structures (http://www.biochem.ucl.ac.uk/bsm/cath/) currently contains 43,229 domains classified into 1467 superfamilies and 5107 sequence families. Each structural family is expanded with sequence relatives from GenBank and completed genomes, using a variety of efficient sequence search protocols and reliable thresholds. This extended CATH protein family database contains 616,470 domain sequences classified into 23,876 sequence families. This results in the significant expansion of the CATH HMM model library to include models built from the CATH sequence relatives, giving a 10% increase in coverage for detecting remote homologues. An improved Dictionary of Homologous superfamilies (DHS) (http://www.biochem.ucl.ac.uk/bsm/dhs/) containing specific sequence, structural and functional information for each superfamily in CATH considerably assists manual validation of homologues. Information on sequence relatives in CATH superfamilies, GenBank and completed genomes is presented in the CATH associated DHS and Gene3D resources. Domain partnership information can be obtained from Gene3D (http://www.biochem.ucl.ac.uk/bsm/cath/Gene3D/). A new CATH server has been implemented (http://www.biochem.ucl.ac.uk/cgi-bin/cath/CathServer.pl) providing automatic classification of newly determined sequences and structures using a suite of rapid sequence and structure comparison methods. The statistical significance of matches is assessed and links are provided to the putative superfamily or fold group to which the query sequence or structure is assigned.