Trends in life science grid: from computing grid to knowledge grid

BACKGROUND: Grid computing has great potential to become a standard cyberinfrastructure for life sciences which often require high-performance computing and large data handling which exceeds the computing capacity of a single institution. RESULTS: This survey reviews the latest grid technologies from the viewpoints of computing grid, data grid and knowledge grid. Computing grid technologies have been matured enough to solve high-throughput real-world life scientific problems. Data grid technologies are strong candidates for realizing "resourceome" for bioinformatics. Knowledge grids should be designed not only from sharing explicit knowledge on computers but also from community formulation for sharing tacit knowledge among a community. CONCLUSION: Extending the concept of grid from computing grid to knowledge grid, it is possible to make use of a grid as not only sharable computing resources, but also as time and place in which people work together, create knowledge, and share knowledge and experiences in a community

Computational Methods towards Personalized Cancer Vaccines and their Application through a Web-based Platform

Cancer immunotherapy is a treatment option that involves or uses components of a patient’s immune system. Today, it is heading towards becoming an integral part of treatment plans together with chemotherapy, surgery, and radiotherapy. Personalized epitope-based vaccines (EVs) serve as one strategy that is truly personalized. Each patient possesses a distinct immune system, and each tumor is unique, rendering the design of a potent vaccine challenging and dependent on the patient and the tumor. The potency of a vaccine is reliant on the ability of its constituent epitopes – short, immunogenic antigen fragments – to trigger an immune response. To assess this ability, one has to take into account the individuality of the immune system, among others conditioned by the variability of the human leukocyte antigen (HLA) gene cluster. Determining the HLA genotype with traditional experimental techniques can be time- and cost-intensive. We proposed a novel HLA genotyping algorithm based on integer linear programming that is independent of dedicated data generation for the sole purpose of HLA typing. On publicly available next-generation sequencing (NGS) data, our method outperformed previously published approaches. HLA binding is a prerequisite for T-cell recognition, and precise prediction algorithms exist. However, this information is not sufficient to assess the immunogenic potential of a peptide. To induce an immune response, reactive T-cell clones with receptors specific for a peptide-HLA complex have to be present. We suggested a method for the prediction of immunogenicity that includes peripheral tolerance models, based on gut microbiome data, in addition to central tolerance, previously shown to increase performance. The comparison to a previously published method suggests that the incorporation of gut microbiome data and HLA-binding stability estimates do not enhance prediction performance. High-throughput sequencing provides the basis for the design of personalized EVs. Through genome and transcriptome sequencing of tumor and matched non-malignant tissue samples, cancer-specific mutations can be identified, which can be further validated using other technologies such as mass spectrometry (MS). Multi-omics approaches can result in the acquisition of several hundreds of gigabytes of data. Handling and analysis of such data usually require data management solutions and high-performance computing (HPC) infrastructures. We developed the web-based platform qPortal for data-driven biomedical research that allows users to manage and analyze quantitative biological data intuitively. To emphasize the advantages of our data-driven approach with an integrated workflow system, we conducted a comparison to Galaxy. Building on qPortal, we implemented the web-based platform iVacPortal for the design of personalized EVs to facilitate data management and data analysis in such projects. Further, we applied the implemented methods through iVacPortal in two studies of two distinct cancer entities, indicating the added value of our platform for the assessment of personalized EV candidates and alternative targets for cancer immunotherapy

The Java CoG kit grid desktop : a simple and central approach to grid computing using the graphical desktop paradigm.

Grid computing is evolving as a service based, flexible and secure resource sharing environment. Currently, with the help of Grid middleware toolkits, Grids are exposing their services through programming models and command line interfaces, requiring much technical knowledge of the backend Grid systems. Grid portals also exist, but fall short on integrating with native environments and maintaining a uniform user interface from portal to portal. In order to gain wider acceptance within the large and less technical oriented user communities, we need a homogeneous graphical user environment that supports the challenging task of providing Grid users an easy to use, seamless and transparent interface requiring minimal user participation. Motivated by the needs of these users, we are presenting the Grid Desktop based on the popularity of the graphical desktop paradigms such as KDE and Windows XP. The Java CoG Kit Grid Desktop is a user centric workspace that enhances the normal operating system desktop paradigm by interlacing Grid concepts and leveraging commodity technologies like Java. The Grid Desktop contributes to the Java CoG Kit architecture and delivers ubiquitous computing through the Java CoG Kit abstractions, portability through XML and Java Web start technologies, and a simple user interface by following the vastly popular desktop patterns such as drag-n-drop

Computational Methods for Interactive and Explorative Study Design and Integration of High-throughput Biological Data

The increase in the use of high-throughput methods to gain insights into biological systems has come with new challenges. Genomics, transcriptomics, proteomics, and metabolomics lead to a massive amount of data and metadata. While this wealth of information has resulted in many scientific discoveries, new strategies are needed to cope with the ever-growing variety and volume of metadata. Despite efforts to standardize the collection of study metadata, many experiments cannot be reproduced or replicated. One reason for this is the difficulty to provide the necessary metadata. The large sample sizes that modern omics experiments enable, also make it increasingly complicated for scientists to keep track of every sample and the needed annotations. The many data transformations that are often needed to normalize and analyze omics data require a further collection of all parameters and tools involved. A second possible cause is missing knowledge about statistical design of studies, both related to study factors as well as the required sample size to make significant discoveries. In this thesis, we develop a multi-tier model for experimental design and a portlet for interactive web-based study design. Through the input of experimental factors and the number of replicates, users can easily create large, factorial experimental designs. Changes or additional metadata can be quickly uploaded via user-defined spreadsheets including sample identifiers. In order to comply with existing standards and provide users with a quick way to import existing studies, we provide full interoperability with the ISA-Tab format. We show that both data model and portlet are easily extensible to create additional tiers of samples annotated with technology-specific metadata. We tackle the problem of unwieldy experimental designs by creating an aggregation graph. Based on our multi-tier experimental design model, similar samples, their sources, and analytes are summarized, creating an interactive summary graph that focuses on study factors and replicates. Thus, we give researchers a quick overview of sample sizes and the aim of different studies. This graph can be included in our portlets or used as a stand alone application and is compatible with the ISA-Tab format. We show that this approach can be used to explore the quality of publicly available experimental designs and metadata annotation. The third part of this thesis contributes to a more statistically sound experiment planning for differential gene expression experiments. We integrate two tools for the prediction of statistical power and sample size estimation into our portal. This integration enables the use of existing data, in order to arrive at more accurate calculation for sample variability. Additionally, the statistical power of existing experimental designs of certain sample sizes can be analyzed. All results and parameters are stored and can be used for later comparison. Even perfectly planned and annotated experiments cannot eliminate human error. Based on our model we develop an automated workflow for microarray quality control, enabling users to inspect the quality of normalization and cluster samples by study factor levels. We import a publicly available microarray dataset to assess our contributions to reproducibility and explore alternative analysis methods based on statistical power analysis

The fate of RNA and RNA binding proteins in Sindbis virus infection

RNA binding proteins (RBPs) accompany RNA throughout its whole life cycle. Therefore, the interaction of RBPs and target RNAs is particularly essential for post-transcriptional regulation. Not only can RBPs affect the RNA’s expression, they can also control the localization, degradation, translation, and other activities of RNA. Capitalizing on recent advances in high-throughput sequencing, this thesis describes the use of transcriptomic and proteomic technologies to systematically study the interplay of RNA and RBPs under the context of viral infection. In brief, we infect the human cell line HEK293 with the Sindbis RNA virus, with the aim of demonstrating how the viral infection remodels the host transcriptome and proteome. While it is commonly accepted that RBPs play a role in the regulation of gene expression, their contributions are still poorly understood. By using RNA interactome capture to track dynamic changes in RNA-binding proteome along the course of viral infection of Sindbis virus in human cells, we aim to assess the global impact of Sindbis virus infection on host transcriptome and proteome, and to identify host RBPs that interact with the Sindbis virus during its reproduction. This thesis reviewed the interplay dynamics between RNA and RBPs in human HEK293 cell line at three different viral infection stages. We observed a remodelling of binding activities of RBPs and the subsequent activation of the immune responses in the host cell. To our surprise, most RBPs demonstrating altered RNA binding did not show protein-level changes. Besides using statistical methods to evaluate the relative effects of different RNA processes, we also demonstrated that RNA degradation pathways had the biggest contribution to changes in RNA abundance change in SINV infected cells. Similar machinery may also apply to other alphaviruses, such as Chikungunya and Mayaro viruses, and thus we hope this study may contribute for the development of drugs to help solving public health problems caused by similar viruses in around the world

SITC/iSBTc Cancer Immunotherapy Biomarkers Resource Document: Online resources and useful tools - a compass in the land of biomarker discovery

Recent positive clinical results in cancer immunotherapy point to the potential of immune-based strategies to provide effective treatment of a variety of cancers. In some patients, the responses to cancer immunotherapy are durable, dramatically extending survival. Extensive research efforts are being made to identify and validate biomarkers that can help identify subsets of cancer patients that will benefit most from these novel immunotherapies. In addition to the clear advantage of such predictive biomarkers, immune biomarkers are playing an important role in the development, clinical evaluation and monitoring of cancer immunotherapies. This Cancer Immunotherapy Resource Document, prepared by the Society for Immunotherapy of Cancer (SITC, formerly the International Society for Biological Therapy of Cancer, iSBTc), provides key references and online resources relevant to the discovery, evaluation and clinical application of immune biomarkers. These key resources were identified by experts in the field who are actively pursuing research in biomarker identification and validation. This organized collection of the most useful references, online resources and tools serves as a compass to guide discovery of biomarkers essential to advancing novel cancer immunotherapies

IMass time: The future, in future!

Joseph John Thomson discovered and proved the existence of electrons through a series of experiments. His work earned him a Nobel Prize in 1906 and initiated the era of mass spectrometry (MS). In the intervening time, other researchers have also been awarded the Nobel Prize for significant advances in MS technology. The development of soft ionization techniques was central to the application of MS to large biological molecules and led to an unprecedented interest in the study of biomolecules such as proteins (proteomics), metabolites (metabolomics), carbohydrates (glycomics), and lipids (lipidomics), allowing a better understanding of the molecular underpinnings of health and disease. The interest in large molecules drove improvements in MS resolution and now the challenge is in data deconvolution, intelligent exploitation of heterogeneous data, and interpretation, all of which can be ameliorated with a proposed IMass technology. We define IMass as a combination of MS and artificial intelligence, with each performing a specific role. IMass will offer advantages such as improving speed, sensitivity, and analyses of large data that are presently not possible with MS alone. In this study, we present an overview of the MS considering historical perspectives and applications, challenges, as well as insightful highlights of IMass

LabKey Server: An open source platform for scientific data integration, analysis and collaboration

<p>Abstract</p> <p>Background</p> <p>Broad-based collaborations are becoming increasingly common among disease researchers. For example, the Global HIV Enterprise has united cross-disciplinary consortia to speed progress towards HIV vaccines through coordinated research across the boundaries of institutions, continents and specialties. New, end-to-end software tools for data and specimen management are necessary to achieve the ambitious goals of such alliances. These tools must enable researchers to organize and integrate heterogeneous data early in the discovery process, standardize processes, gain new insights into pooled data and collaborate securely.</p> <p>Results</p> <p>To meet these needs, we enhanced the LabKey Server platform, formerly known as CPAS. This freely available, open source software is maintained by professional engineers who use commercially proven practices for software development and maintenance. Recent enhancements support: (i) Submitting specimens requests across collaborating organizations (ii) Graphically defining new experimental data types, metadata and wizards for data collection (iii) Transitioning experimental results from a multiplicity of spreadsheets to custom tables in a shared database (iv) Securely organizing, integrating, analyzing, visualizing and sharing diverse data types, from clinical records to specimens to complex assays (v) Interacting dynamically with external data sources (vi) Tracking study participants and cohorts over time (vii) Developing custom interfaces using client libraries (viii) Authoring custom visualizations in a built-in R scripting environment.</p> <p>Diverse research organizations have adopted and adapted LabKey Server, including consortia within the Global HIV Enterprise. Atlas is an installation of LabKey Server that has been tailored to serve these consortia. It is in production use and demonstrates the core capabilities of LabKey Server. Atlas now has over 2,800 active user accounts originating from approximately 36 countries and 350 organizations. It tracks roughly 27,000 assay runs, 860,000 specimen vials and 1,300,000 vial transfers.</p> <p>Conclusions</p> <p>Sharing data, analysis tools and infrastructure can speed the efforts of large research consortia by enhancing efficiency and enabling new insights. The Atlas installation of LabKey Server demonstrates the utility of the LabKey platform for collaborative research. Stable, supported builds of LabKey Server are freely available for download at <url>http://www.labkey.org</url>. Documentation and source code are available under the Apache License 2.0.</p