Tachyon search speeds up retrieval of similar sequences by several orders of magnitude.

UNLABELLED: The usage of current sequence search tools becomes increasingly slower as databases of protein sequences continue to grow exponentially. Tachyon, a new algorithm that identifies closely related protein sequences ~200 times faster than standard BLAST, circumvents this limitation with a reduced database and oligopeptide matching heuristic. AVAILABILITY AND IMPLEMENTATION: The tool is publicly accessible as a webserver at http://tachyon.bii.a-star.edu.sg and can also be accessed programmatically through SOAP.

Parameterization of disorder predictors for large-scale applications requiring high specificity by using an extended benchmark dataset.

BACKGROUND: Algorithms designed to predict protein disorder play an important role in structural and functional genomics, as disordered regions have been reported to participate in important cellular processes. Consequently, several methods with different underlying principles for disorder prediction have been independently developed by various groups. For assessing their usability in automated workflows, we are interested in identifying parameter settings and threshold selections, under which the performance of these predictors becomes directly comparable. RESULTS: First, we derived a new benchmark set that accounts for different flavours of disorder complemented with a similar amount of order annotation derived for the same protein set. We show that, using the recommended default parameters, the programs tested are producing a wide range of predictions at different levels of specificity and sensitivity. We identify settings, in which the different predictors have the same false positive rate. We assess conditions when sets of predictors can be run together to derive consensus or complementary predictions. This is useful in the framework of proteome-wide applications where high specificity is required such as in our in-house sequence analysis pipeline and the ANNIE webserver. CONCLUSIONS: This work identifies parameter settings and thresholds for a selection of disorder predictors to produce comparable results at a desired level of specificity over a newly derived benchmark dataset that accounts equally for ordered and disordered regions of different lengths.

Knowledge environments representing molecular entities for the virtual physiological human.

In essence, the virtual physiological human (VPH) is a multiscale representation of human physiology spanning from the molecular level via cellular processes and multicellular organization of tissues to complex organ function. The different scales of the VPH deal with different entities, relationships and processes, and in consequence the models used to describe and simulate biological functions vary significantly. Here, we describe methods and strategies to generate knowledge environments representing molecular entities that can be used for modelling the molecular scale of the VPH. Our strategy to generate knowledge environments representing molecular entities is based on the combination of information extraction from scientific text and the integration of information from biomolecular databases. We introduce @neuLink, a first prototype of an automatically generated, disease-specific knowledge environment combining biomolecular, chemical, genetic and medical information. Finally, we provide a perspective for the future implementation and use of knowledge environments representing molecular entities for the VPH.

@neuLink: a service-oriented application for biomedical knowledge discovery.

We introduce the architecture of @neuLink, a service-oriented environment for biomedical knowledge discovery which has been developed in the course of EU Integrated Project @neurIST. The application integrates data from databases with information extracted from unstructured text sources. Moreover, @neuLink supports the analysis of primary biomolecular data associated with individual patients and thus enables the interpretation of molecular data inside a clinical research environment. Based on an assembly of data services, @neuLink interacts with the complex @neurIST grid infrastructure through a dedicated data access and data mediation service. Data types integrated by @neuLink are covering the entire span of biomolecular entities: from gene names in text to entries in EntrezGene; from mentions of drugs to Drugbank, from information on allelic variants in scientific literature to entries in dbSNP. The architecture of @neuLink allows easy integration of other webservice-based applications and thus the spectrum of analysis capabilities of @neuLink can be extended following the requirements of the users of the @neurIST system.