On an infrastructure to support sharing and aggregating pre- and post-publication systems biology research data

The move towards in silico experimentation has resulted in the use of computational models, in addition to traditional experimental models, to generate the raw data that is analysed and published as research findings. This change requires new methods to be introduced to facilitate independent validation of the underlying models and the reported results. The promotion of co-operative research has the potential to help to both validate results and explore wider problem areas. In this paper we leverage and extend two existing software frameworks to develop an infrastructure that has the potential to both promote the sharing of data between researchers pre-publication and enable access to the data for interested parties post-publication. The pre-publication sharing of data would enable larger problem spaces to be explored by distributed research groups; enabling access to the data post-publication would allow reviewers and the wider community to independently verify the published results which would, in the longer term, help to increase confidence in published results. The framework is used to perform reproducible and numerically validated individual-based computational experiments into the onset of colorectal cancer. Existing results are verified and new insights into the top-down versus bottom-up hypothesis of colorectal crypt invasion are given.     

Using multiple ontologies to integrate complex biological data

The strength of the rat as a model organism lies in its utility in pharmacology, biochemistry and physiology research. Data resulting from such studies is difficult to represent in databases and the creation of user-friendly data mining tools has proved difficult. The Rat Genome Database has developed a comprehensive ontology-based data structure and annotation system to integrate physiological data along with environmental and experimental factors, as well as genetic and genomic information. RGD uses multiple ontologies to integrate complex biological information from the molecular level to the whole organism, and to develop data mining and presentation tools. This approach allows RGD to indicate not only the phenotypes seen in a strain but also the specific values under each diet and atmospheric condition, as well as gender differences. Harnessing the power of ontologies in this way allows the user to gather and filter data in a customized fashion, so that a researcher can retrieve all phenotype readings for which a high hypoxia is a factor. Utilizing the same data structure for expression data, pathways and biological processes, RGD will provide a comprehensive research platform which allows users to investigate the conditions under which biological processes are altered and to elucidate the mechanisms of disease.     

The DCCD: A digital data infrastructure for tree-ring research

Existing on-line databases for dendrochronology are not flexible in terms of user permissions, tree-ring data formats, metadata administration and language. This is why we developed the Digital Collaboratory for Cultural Dendrochronology (DCCD). This TRiDaS-based multi-lingual database allows users to control data access, to perform queries, to upload and download (meta)data in a variety of digital formats, and to edit metadata on line. The content of the DCCD conforms to EU best practices regarding the long-term preservation of digital research data.     

Added predictive value of high-throughput molecular data to clinical data and its validation

Hundreds of 'molecular signatures' have been proposed in the literature to predict patient outcome in clinical settings from high-dimensional data, many of which eventually failed to get validated. Validation of such molecular research findings is thus becoming an increasingly important branch of clinical bioinformatics. Moreover, in practice well-known clinical predictors are often already available. From a statistical and bioinformatics point of view, poor attention has been given to the evaluation of the added predictive value of a molecular signature given that clinical predictors or an established index are available. This article reviews procedures that assess and validate the added predictive value of high-dimensional molecular data. It critically surveys various approaches for the construction of combined prediction models using both clinical and molecular data, for validating added predictive value based on independent data, and for assessing added predictive value using a single data set.     

Conference retrospective: An appropriate modeling infrastructure for cancer research

Presentations and discussions at the symposium illustrate some general issues in biomedical modeling for cancer research. Given the motivations for modeling and assumptions concerning who should be involved in the modeling process, one can identify some basic needs to be met in supports to modelers. These concern both the models themselves and ways of presenting them to users. In conclusion, some thoughts are offered on economic and educational issues that may affect the infusion of modeling into biomedical research.     

Melatonin in clinical oncology

The aim of this article is provide a survey of the current knowledge relating to the analysis of melatonin and its administration to cancer patients. On the basis of this compilation of data it can be discussed under which conditions melatonin may be used for diagnostic and/or therapeutic purposes in clinical oncology. Melatonin is depressed in patients with cancers of different origins during the phase of primary tumour growth whereas a normal or sometimes elevated pineal melatonin secretory activity is found during early stages of tumour development or when recidivations arise. The clinical studies of Lissoni show that melatonin, particularly if combined with interleukin-2, is able to favourably influence the course of advanced malignant disease leading to a prolonged survival as well as to an improved quality of life. These findings require to be verified by independent and controlled replication studies. If they can be confirmed it should be attempted to administer melatonin to patients with earlier stages of cancer parallel to standard oncological treatment regimens. In such trials it should be tested whether a substitutional therapy in patients with endogenously depressed melatonin may favourably affect the course of the disease both in quantitative (inhibitory effect on tumour growth and spread) and qualitative terms (improved performance status).     

The use of molecular data in mangrove plant research

The use of molecular data in plant studies has increased dramatically duringthe last decade. Recently, molecular techniques have been applied tomangrove plants to investigate population structure and phylogeneticrelationships. We briefly review research on mangrove plants based onprotein polymorphism and DNA data. Results of these molecular studieshave provided new insights into conservation issues, systematics,biogeography, and population biology of mangrove plants, in many casesrevealing unexpected and surprising patterns.     

The clinical application of chronobiology to oncology

The introduction to medical practice of chemical agents for fighting human cancer some 30 years ago brought hope to a field of medicine previously shrouded in despair and impeded by superstition. Gradually more and better agents have become available to the physician and to the patient suffering from cancer. The physician-scientist has, in turn, learned a great deal about normal and abnormal cellular biology by using these drugs as probes. The observations that certain tissues and certain tumors share patterns of drug toxicity have led to a broadening of biologic understanding and to the use of combinations of drugs with shared antitumor activity and unshared toxicities. This empiric art of cancer chemotherapy has resulted in great progress in the treatment of a large number of advanced cancers. As important, however, is that this experience has resulted in knowledge which is leading to the development of rationally designed therapeutic regimens; to drug analogues seeking greater therapeutic-toxic ratios; to the development of methods for chemically interfering with toxic drug effects while allowing or enhancing antitumor effect; and to work defining effects of drug timing. Drug timing research considers drug dosage in respect to the timing of a drug relative to the timing of other drugs (drug-time-drug interactions) or to other doses of that same drug (drug-drug interval); the order of drugs (drug-drug sequence); and the timing of drugs relative to an internal organismic time structure (time-drug interactions). Data in this brief review clearly show that drug timing needs to be considered when designing rational chemotherapy for a living organism suffering from a cancer. The beautiful spatiotemporal complexity of life is not to be ignored or avoided, but should be considered as a golden opportunity to use what few imprecise chemical weapons we have a little more effectively.     

Problems and pitfalls in a clinical research data management system

The problems and pitfalls encountered in the computerized data bank for the Netherlands Coronary Surgery (NCS) study are reviewed. This study involved 848 patients seen before coronary artery surgery and at 1 and 3 yr after surgery. Nineteen data forms were used resulting in maximally 1142 variables per patient. The importance of quality control is emphasized as well as the efficient transfer of information from data bank to statistical processing.     

Antisense therapy in clinical oncology

Nucleic acid molecules have emerged as versatile tools with promising utility as therapeutics for human diseases. The specificity of hybridization of an antisense oligonucleotide (AS ODN) to the target mRNA makes the AS strategy attractive to selectively modulate the expression of genes involved in the pathogenesis of malignant or non-malignant diseases. One AS drug has been approved for local therapy of cytomegalovirus retinitis, and a number of AS ODN are currently tested in clinical trials including ODN that target bcl-2, survivin, and DNA methyltransferase. The clinical studies indicate that AS ODN are well tolerated and may have therapeutic activity. In this overview, we summarize therapeutic concepts, clinical studies, and new promising molecular targets to treat human cancer with AS ODN.     

Merging clinical chemistry biomarker data with a COPD database - building a clinical infrastructure for proteomic studies

Background

Data from biological samples and medical evaluations plays an essential part in clinical decision making. This data is equally important in clinical studies and it is critical to have an infrastructure that ensures that its quality is preserved throughout its entire lifetime. We are running a 5-year longitudinal clinical study, KOL-Örestad, with the objective to identify new COPD (Chronic Obstructive Pulmonary Disease) biomarkers in blood. In the study, clinical data and blood samples are collected from both private and public health-care institutions and stored at our research center in databases and biobanks, respectively. The blood is analyzed by Mass Spectrometry and the results from this analysis then linked to the clinical data.

Method

We built an infrastructure that allows us to efficiently collect and analyze the data. We chose to use REDCap as the EDC (Electronic Data Capture) tool for the study due to its short setup-time, ease of use, and flexibility. REDCap allows users to easily design data collection modules based on existing templates. In addition, it provides two functions that allow users to import batches of data; through a web API (Application Programming Interface) as well as by uploading CSV-files (Comma Separated Values).

Results

We created a software, DART (Data Rapid Translation), that translates our biomarker data into a format that fits REDCap's CSV-templates. In addition, DART is configurable to work with many other data formats as well. We use DART to import our clinical chemistry data to the REDCap database.

Conclusion

We have shown that a powerful and internationally adopted EDC tool such as REDCap can be extended so that it can be used efficiently in proteomic studies. In our study, we accomplish this by using DART to translate our clinical chemistry data to a format that fits the templates of REDCap.

     

An overview of clinical molecular genetics

Clinical molecular genetics has recently become recognized as a diagnostic discipline. This article covers the evolution, structure, and possible forward development of clinical molecular genetics. Topics covered include general test categories, introducing new tests, laboratory facilities, staffing and training, and overview of quality issues.     

CGO: utilizing and integrating gene expression microarray data in clinical research and data management

Clinical GeneOrganizer (CGO) is a novel windows-based archiving, organization and data mining software for the integration of gene expression profiling in clinical medicine. The program implements various user-friendly tools and extracts data for further statistical analysis. This software was written for Affymetrix GeneChip *.txt files, but can also be used for any other microarray-derived data. The MS-SQL server version acts as a data mart and links microarray data with clinical parameters of any other existing database and therefore represents a valuable tool for combining gene expression analysis and clinical disease characteristics.     

Detection and location of OP-degrading activity: A model to integrate education and research

The Environmental Sampling Research Module (ESRM) is an investigative/discovery module that provides undergraduate research experiences for students as part of an interdisciplinary research-based biotechnology curriculum at the University of Houston campus. As part of the ESRM, students collect soil samples from various locations to test for the presence of organophosphorous (OP) degrading bacteria. At the end of this research project students submit a research paper on their field and laboratory activities and discuss their experimental data and observations. Students also record the date, location of collection, and the results of testing the sample for the degradation of two pesticides, methyl parathion or paraoxon, in an electronic laboratory notebook (ELN). Each collection site is recorded on a Google Maps module and the data from student research activities is made available to other undergraduate students. This data is then used to generate a microorganism database of pesticide degrading activity and promote reading, critical thinking, and analytical skills as part of the curriculum. Our sampling of agricultural sites and wastewater within and around the city of Houston has identified seven distinct genera of OP degrading organisms, including Pseudomonas, Stenotrophomonas, Exiguobacterium, Delftia, Agrobacterium, Aeromonas, and Rhizobium. Collected strains exhibit phosphotriesterase-like enzymatic activity with isolates of Pseudomonas putida and Stenotrophomonas maltophilia capable of degrading both the phosphotriester paraoxon and the phosphorothioate methyl parathion. Using this collection of OP-degrading microorganisms, undergraduate students have evaluated their potential for enhancing the removal of harmful organophosphates and their toxic metabolites from contaminated agricultural soil and adjacent bodies of water. This analytical data can potentially be utilized for environmental and industrial applications in bioremediation and ecology providing an innovative method for integrating education and research. In addition, the versatility of the ESRM itself provides for easy and rapid adaptation into varying environmental science courses with significant potential for the discovery and isolation of new and unique organisms to be used as part of ongoing research in the laboratory.