Affinity maturation is an evolutionary process by which the affinity of antibodies (Abs) against specific antigens (Ags) increases through rounds of B-cell proliferation, somatic hypermutation, and positive selection in germinal centres (GC). The positive selection of B cells depends on affinity, but the underlying mechanisms of affinity discrimination and affinity-based selection are not well understood. It has been suggested that selection in GC depends on both rapid binding of B-cell receptors (BcRs) to Ags which is kinetically favourable and tight binding of BcRs to Ags, which is thermodynamically favourable; however, it has not been shown whether a selection bias for kinetic properties is present in the GC. To investigate the GC selection bias towards rapid and tight binding, we developed an agent-based model of GC and compared the evolution of founder B cells with initially identical low affinities but with different association/dissociation rates for Ag presented by follicular dendritic cells in three Ag collection mechanisms. We compared an Ag collection mechanism based on association/dissociation rates of B-cell interaction with presented Ag, which includes a probabilistic rupture of bonds between the B-cell and Ag (Scenario-1) with a reference scenario based on an affinity-based Ag collection mechanism (Scenario-0). Simulations showed that the mechanism of Ag collection affects the GC dynamics and the GC outputs concerning fast/slow (un)binding of B cells to FDC-presented Ags. In particular, clones with lower dissociation rates outcompete clones with higher association rates in Scenario-1, while remaining B cells from clones with higher association rates reach higher affinities. Accordingly, plasma cell and memory B cell populations were biased towards B-cell clones with lower dissociation rates. Without such probabilistic ruptures during the Ag extraction process (Scenario-2), the selective advantage for clones with very low dissociation rates diminished, and the affinity maturation level of all clones decreased to the reference level.

UNLABELLED: Currently, the Gene Expression Omnibus (GEO) contains public data of over 1 million samples from more than 40 000 microarray-based functional genomics experiments. This provides a rich source of information for novel biological discoveries. However, unlocking this potential often requires retrieving and storing a large number of expression profiles from a wide range of different studies and platforms. The compendiumdb R package provides an environment for downloading functional genomics data from GEO, parsing the information into a local or remote database and interacting with the database using dedicated R functions, thus enabling seamless integration with other tools available in R/Bioconductor. AVAILABILITY AND IMPLEMENTATION: The compendiumdb package is written in R, MySQL and Perl. Source code and binaries are available from CRAN (http://cran.r-project.org/web/packages/compendiumdb/) for all major platforms (Linux, MS Windows and OS X) under the GPLv3 license. CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: Two-dimensional differential gel electrophoresis (2D-DIGE) provides a powerful technique to separate proteins on their isoelectric point and apparent molecular mass and quantify changes in protein expression. Abundantly available proteins in spots can be identified using mass spectrometry-based approaches. However, identification is often not possible for low-abundant proteins. RESULTS: We present a novel computational approach to prioritize candidate proteins for unidentified spots. Our approach exploits noisy information on the isoelectric point and apparent molecular mass of a protein spot in combination with functional similarities of candidate proteins to already identified proteins to select and rank candidates. We evaluated our method on a 2D-DIGE dataset comparing protein expression in uninfected and HIV-1 infected T-cells. Using leave-one-out cross-validation, we show that the true-positive rate for the top-5 ranked proteins is 43.8%. CONCLUSIONS: Our approach shows good performance on a 2D-DIGE dataset comparing protein expression in uninfected and HIV-1 infected T-cells. We expect our method to be highly useful in (re-)mining other 2D-DIGE experiments in which especially the low-abundant protein spots remain to be identified.

Summary Science gateways provide UIs and high‐level services to access and manage applications and data collections on distributed resources. They facilitate users to perform data analysis on distributed computing infrastructures without getting involved into the technical details. The e‐BioInfra Gateway is a science gateway for biomedical data analysis on a national grid infrastructure, which has been successfully adopted for neuroscience research. This paper describes the motivation, requirements, and design of a new generation of e‐BioInfra Gateway, which is based on the grid and cloud user support environment (also known as WS‐PGRADE/gUSE framework) and supports heterogeneous infrastructures. The new gateway has been designed to have additional data and meta‐data management facilities to access and manage (biomedical) data servers, and to provide data‐centric user interaction. We have implemented and deployed the new gateway for the computational neuroscience research community of the Academic Medical Center of the University of Amsterdam. This paper presents the system architecture of the new gateway, highlights the improvements that have been achieved, discusses the choices that we have made, and reflects on those based on initial user feedback. Copyright © 2014 John Wiley & Sons, Ltd.

The accurate representation of all aspects of a metabolic network in a structured format, such that it can be used for a wide variety of computational analyses, is a challenge faced by a growing number of researchers. Analysis of five major metabolic pathway databases reveals that each database has made widely different choices to address this challenge, including how to deal with knowledge that is uncertain or missing. In concise overviews, we show how concepts such as compartments, enzymatic complexes and the direction of reactions are represented in each database. Importantly, also concepts which a database does not represent are described. Which aspects of the metabolic network need to be available in a structured format and to what detail differs per application. For example, for in silico phenotype prediction, a detailed representation of gene-protein-reaction relations and the compartmentalization of the network is essential. Our analysis also shows that current databases are still limited in capturing all details of the biology of the metabolic network, further illustrated with a detailed analysis of three metabolic processes. Finally, we conclude that the conceptual differences between the databases, which make knowledge exchange and integration a challenge, have not been resolved, so far, by the exchange formats in which knowledge representation is standardized.

Large experiments on distributed infrastructures become increasingly complex to manage, in particular to trace all computations that gave origin to a piece of data or an event such as an error. The work presented in this paper describes the design and implementation of an architecture to support experiment provenance and its deployment in the concrete case of a particular e-infrastructure for biosciences. The proposed solution consists of: (a) a data provenance repository to capture scientific experiments and their execution path, (b) a software tool (crawler) that gathers, classifies, links, and stores the information collected from various sources, and (c) a set of user interfaces through which the end-user can access the provenance data, interpret the results, and trace the sources of failure. The approach is based on an OPM-compliant API, PLIER, that is flexible to support future extensions and facilitates interoperability among heterogeneous application systems.

Diffusion Tensor MRI (DTI) is a rather recent image acquisition modality that can help identify disease processes in nerve bundles in the brain. Due to the large and complex nature of such data, its analysis requires new and sophisticated pipelines that are more efficiently executed within a grid environment. We present our progress over the past four years in the development and porting of the DTI analysis pipeline to grids. Starting with simple jobs submitted from the command-line, we moved towards a workflow-based implementation and finally into a web service that can be accessed via web browsers by end-users. The analysis algorithms evolved from basic to state-of-the-art, currently enabling the automatic calculation of a population-specific `atlas' where even complex brain regions are described in an anatomically correct way. Performance statistics show a clear improvement over the years, representing a mutual benefit from both a technology push and application pull.

BACKGROUND: Bioinformatics is confronted with a new data explosion due to the availability of high throughput DNA sequencers. Data storage and analysis becomes a problem on local servers, and therefore it is needed to switch to other IT infrastructures. Grid and workflow technology can help to handle the data more efficiently, as well as facilitate collaborations. However, interfaces to grids are often unfriendly to novice users. RESULTS: In this study we reused a platform that was developed in the VL-e project for the analysis of medical images. Data transfer, workflow execution and job monitoring are operated from one graphical interface. We developed workflows for two sequence alignment tools (BLAST and BLAT) as a proof of concept. The analysis time was significantly reduced. All workflows and executables are available for the members of the Dutch Life Science Grid and the VL-e Medical virtual organizations All components are open source and can be transported to other grid infrastructures. CONCLUSIONS: The availability of in-house expertise and tools facilitates the usage of grid resources by new users. Our first results indicate that this is a practical, powerful and scalable solution to address the capacity and collaboration issues raised by the deployment of next generation sequencers. We currently adopt this methodology on a daily basis for DNA sequencing and other applications. More information and source code is available via http://www.bioinformaticslaboratory.nl/

Summary: Although whole-genome sequences have been analysed for the presence of anomalous DNA, no dedicated application is currently available to analyse the composition of individual sequence entries, for instance those derived by experimental techniques, such as subtractive hybridization. Since genomic dinucleotide frequency values are conserved between related species, a representative genome sequence can often be found to score for anomalous sequence composition for many of these putative horizontally transferred sequences. We developed the application δρ-web, which enables the determination of the differences between the dinucleotide composition of an input sequence and that of a selected genome in a size-dependent manner. A feature allowing batch comparisons is included as well. In addition, δρ-web allows the analysis of the dinucleotide composition of complete genomes. This provides complementary information for the identification of large anomalous gene clusters. Availability: The application is available through http://deltarho.amc.uva.nl and the software is available from the authors. Contact: [email protected] Supplementary information An online help file with more extensive user guidelines is supplied at http://deltarho.amc.uva.nl

Abstract Motivation: To enhance the exploration of gene expression data in a metabolic context, one requires an application that allows the integration of this data and which represents this data in a (genome-wide) metabolic map. The layout of this metabolic map must be highly flexible to enable discoveries of biological phenomena. Moreover, it must allow the simultaneous representation of additional information about genes and enzymes. Since the layout and properties of existing maps did not fulfill our requirements, we developed a new way of representing gene expression data in metabolic charts. Results: ViMAc generates user-specified (genome-wide) metabolic maps to explore gene expression data. To enhance the interpretation of these maps information such as sub-cellular localization is included. ViMAc can be used to analyse human or yeast expression data obtained with DNA microarrays or SAGE. We introduce our metabolic map method and demonstrate how it can be applied to explore DNA microarray data for yeast. Availability: ViMAc is freely available for academic institutions on request from the authors. Contact: [email protected] * To whom correspondence should be addressed.

MOTIVATION: SAGE enables the determination of genome-wide mRNA expression profiles. A comprehensive analysis of SAGE data requires software, which integrates (statistical) data analysis methods with a database system. Furthermore, to facilitate data sharing between users, the application should reside on a central server and be accessed via the internet. Since such an application was not available we developed the USAGE package. RESULTS: USAGE is a web-based application that comprises an integrated set of tools, which offers many functions for analysing and comparing SAGE data. Additionally, USAGE includes a statistical method for the planning of new SAGE experiments. USAGE is available in a multi-user environment giving users the option of sharing data. USAGE is interfaced to a relational database to store data and analysis results. The USAGE query editor allows the composition of queries for searching this database. Several database functions have been included which enable the selection and combination of data. USAGE provides the biologist increased functionality and flexibility for analysing SAGE data. AVAILABILITY: USAGE is freely accessible for academic institutions at http://www.cmbi.kun.nl/usage/. The source code of USAGE is freely available for academic institutions on request from the first author.