Life sciences are yielding huge data sets that underpin scientific discoveries fundamental to improvement in human health, agriculture and the environment. In support of these discoveries, a plethora of databases and tools are deployed, in technically complex and diverse implementations, across a spectrum of scientific disciplines. The corpus of documentation of these resources is fragmented across the Web, with much redundancy, and has lacked a common standard of information. The outcome is that scientists must often struggle to find, understand, compare and use the best resources for the task at hand.Here we present a community-driven curation effort, supported by ELIXIR—the European infrastructure for biological information—that aspires to a comprehensive and consistent registry of information about bioinformatics resources. The sustainable upkeep of this Tools and Data Services Registry is assured by a curation effort driven by and tailored to local needs, and shared amongst a network of engaged partners.As of November 2015, the registry includes 1785 resources, with depositions from 126 individual registrations including 52 institutional providers and 74 individuals. With community support, the registry can become a standard for dissemination of information about bioinformatics resources: we welcome everyone to join us in this common endeavour. The registry is freely available at https://bio.tools.

The Cell Line Data Base (CLDB) is a well-known reference information source on human and animal cell lines including information on more than 6000 cell lines. Main biological features are coded according to controlled vocabularies derived from international lists and taxonomies. HyperCLDB (http://bioinformatics.istge.it/hypercldb/) is a hypertext version of CLDB that improves data accessibility by also allowing information retrieval through web spiders. Access to HyperCLDB is provided through indexes of biological characteristics and navigation in the hypertext is granted by many internal links. HyperCLDB also includes links to external resources. Recently, an interest was raised for a reference nomenclature for cell lines and CLDB was seen as an authoritative system. Furthermore, to overcome the cell line misidentification problem, molecular authentication methods, such as fingerprinting, single-locus short tandem repeat (STR) profile and single nucleotide polymorphisms validation, were proposed. Since this data is distributed, a reference portal on authentication of human cell lines is needed. We present here the architecture and contents of CLDB, its recent enhancements and perspectives. We also present a new related database, the Cell Line Integrated Molecular Authentication (CLIMA) database (http://bioinformatics.istge.it/clima/), that allows to link authentication data to actual cell lines.

Our results do not support the hypothesis that, in patients undergoing ECV of chronic persistent AF, supplementation with PUFAs in addition to the usual antiarrhythmic treatment reduces recurrent AF.

Data integration is needed in order to cope with the huge amounts of biological information now available and to perform data mining effectively. Current data integration systems have strict limitations, mainly due to the number of resources, their size and frequency of updates, their heterogeneity and distribution on the Internet. Integration must therefore be achieved by accessing network services through flexible and extensible data integration and analysis network tools. EXtensible Markup Language (XML), Web Services and Workflow Management Systems (WMS) can support the creation and deployment of such systems. Many XML languages and Web Services for bioinformatics have already been designed and implemented and some WMS have been proposed. In this article, we review a methodology for data integration in biomedical research that is based on these technologies. We also briefly describe some of the available WMS and discuss the current limitations of this methodology and the ways in which they can be overcome.

Dialysis-to-dialysis variability in blood pressure is a predictor of cardiovascular mortality in hemodialysis patients, and blood pressure variability may be used in managing hypertension and predicting outcomes in dialysis patients.