Problems and pitfalls in a clinical resarch 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.     

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.     

TURBOVEG,a comprehensive data base management system for vegetation data

Abstract. The computer software package TURBOVEG (for Microsoft® Windows®) was developed in The Netherlands for the processing of phytosociological data. This package comprises an easy‐to‐use data base management system. The data bank to be managed can be divided into several data bases which may consist of up to 100 000 relevés each. The program provides methods for input, import, selection, and export of relevés. In 1994, TURBOVEG was accepted as the standard computer package for the European Vegetation Survey. Currently it has been installed in more than 25 countries throughout Europe and overseas.     

Automating data manipulation for genetic analysis using a data base management system

Inefficient coding and manipulation of pedigree data have often hindered the progress of genetic studies. In this paper we present the methodology for interfacing a data base management system (DBMS) called MEGADATS with a linkage analysis program called LIPED. Two families that segregate a dominant trait and one test marker were used in a simulated exercise to demonstrate how a DBMS can be used to automate tedious clerical steps and improve the efficiency of a genetic analysis. The merits of this approach to data management are discussed. We conclude that a standardized format for genetic analysis programs would greatly facilitate data analysis.     

PRISM: a data management system for high-throughput proteomics

Advanced proteomic research efforts involving areas such as systems biology or biomarker discovery are enabled by the use of high level informatics tools that allow the effective analysis of large quantities of differing types of data originating from various studies. Performing such analyses on a large scale is not feasible without a computational platform that performs data processing and management tasks. Such a platform must be able to provide high-throughput operation while having sufficient flexibility to accommodate evolving data analysis tools and methodologies. The Proteomics Research Information Storage and Management system (PRISM) provides a platform that serves the needs of the accurate mass and time tag approach developed at Pacific Northwest National Laboratory. PRISM incorporates a diverse set of analysis tools and allows a wide range of operations to be incorporated by using a state machine that is accessible to independent, distributed computational nodes. The system has scaled well as data volume has increased over several years, while allowing adaptability for incorporating new and improved data analysis tools for more effective proteomics research.     

Problems and pitfalls in assessing human T-lymphocyte mutant frequencies

The measurement of 6-thioguanine-resistant frequencies in human T-lymphocytes has been used to quantitate the in vivo HPRT mutant frequency. The data so far indicate a large variability in normal healthy individuals. The reliability with which wells are identified for clonal growth in the assay was investigated using 5 different methods of scoring: visual scoring, uptake of [3H]thymidine (either by cut off point or by statistical analysis), cell count and cytogenetic analysis. None of these methods presented a viable means of scoring the assay. An examination of the statistical precision of the assay under the limitations imposed by the experimental conditions leads to the conclusion that there is a large inherent error associated with the estimated mutant frequencies. Analysis of the T-lymphocyte subpopulations by cell surface monoclonal antibodies also leads us to believe that the observed mutant frequencies may not be representative of the true in vivo mutant frequencies. If the assay is to be used as a sensitive screen for individual or population exposure to possible mutagens, a closer understanding of the biology of the assay is indicated, and a comprehensive reevaluation of the methodology required. The utility of the system for studying qualitative aspects of human mutagenesis is not in doubt.     

Caveats and pitfalls of ROC analysis in clinical microarray research (and how to avoid them)

The receiver operating characteristic (ROC) has emerged as the gold standard for assessing and comparing the performance of classifiers in a wide range of disciplines including the life sciences. ROC curves are frequently summarized in a single scalar, the area under the curve (AUC). This article discusses the caveats and pitfalls of ROC analysis in clinical microarray research, particularly in relation to (i) the interpretation of AUC (especially a value close to 0.5); (ii) model comparisons based on AUC; (iii) the differences between ranking and classification; (iv) effects due to multiple hypotheses testing; (v) the importance of confidence intervals for AUC; and (vi) the choice of the appropriate performance metric. With a discussion of illustrative examples and concrete real-world studies, this article highlights critical misconceptions that can profoundly impact the conclusions about the observed performance.     

FAIR research data management as community approach in bioengineering

Research data management (RDM) requires standards, policies, and guidelines. Findable, accessible, interoperable, and reusable (FAIR) data management is critical for sustainable research. Therefore, collaborative approaches for managing FAIR-structured data are becoming increasingly important for long-term, sustainable RDM. However, they are rather hesitantly applied in bioengineering. One of the reasons may be found in the interdisciplinary character of the research field. In addition, bioengineering as application of principles of biology and tools of process engineering, often have to meet different criteria. In consequence, RDM is complicated by the fact that researchers from different scientific institutions must meet the criteria of their home institution, which can lead to additional conflicts. Therefore, centrally provided general repositories implementing a collaborative approach that enables data storage from the outset In a biotechnology research network with over 20 tandem projects, it was demonstrated how FAIR-RDM can be implemented through a collaborative approach and the use of a data structure. In addition, the importance of a structure within a repository was demonstrated to keep biotechnology research data available throughout the entire data lifecycle. Furthermore, the biotechnology research network highlighted the importance of a structure within a repository to keep research data available throughout the entire data lifecycle.     

Problems in dealing with missing data and informative censoring in clinical trials

A common problem in clinical trials is the missing data that occurs when patients do not complete the study and drop out without further measurements. Missing data cause the usual statistical analysis of complete or all available data to be subject to bias. There are no universally applicable methods for handling missing data. We recommend the following: (1) Report reasons for dropouts and proportions for each treatment group; (2) Conduct sensitivity analyses to encompass different scenarios of assumptions and discuss consistency or discrepancy among them; (3) Pay attention to minimize the chance of dropouts at the design stage and during trial monitoring; (4) Collect post-dropout data on the primary endpoints, if at all possible; and (5) Consider the dropout event itself an important endpoint in studies with many.     

Avermectins in arthropod vector management - prospects and pitfalls

The proven impact of avermectins against a wide variety of arthropod vectors suggests that this new family of compounds holds promise in reducing the incidence of vector-borne disease. Experimentally, decreased survival and abundance of various vector species indicate that certain vector populations may be so manipulated. In addition, sublethal effects on individuals include lengthened development, decreased fecundity and diminished parasite uptake. Enthusiasm must be cautious, given possible impacts on non-target species and the eventual development of resistance. Here Mark Wilson emphasizes that the present challenge is to study how this new toxin may be integrated into vector-management schemes that already employ multiple, diverse interventions. Ultimately, the value of such action must be measured not simply in terms of reduced vector abundance, but also with the more complex equation of reduced parasite transmission in mind.     

海洋污损生物数据管理系统的设计与构建

固着或栖息在船舶和人工设施水下部位的海洋污损生物, 会对人们的涉海活动产生不利影响, 其群落的形成和发展过程与温度、盐度、深度、季节、海域、浸海时间、离岸距离和附着基类型等多种因素密切相关。为便于系统分析和综合处理各海区污损生物资料, 理清各要素之间的内在关系, 需要一个能将上述因子与生物群落参数有机地结合起来的数据平台, 将分散、零星的资料予以归纳整合并通过网络共享, 以更好地为生产实践和科学研究服务。本研究采用Internet技术, 应用ASP.NET框架和MySQL数据库, 使用MS Visual Studio 2013设计并开发了服务端部署在Windows 7或Windows Server 2008 R2 (推荐)操作系统上的海洋污损生物数据管理系统, 实现了基于网络的海洋污损生物数据集成、储存与管理, 可完成来源不同、时相变化和海区多样的污损生物数据资料的集成与储存, 能通过单一或多种组合条件进行查询和检索, 并可根据用户的需要导出多种格式的检索结果报表。该系统具备操作简便、方便网络共享、易于升级更新和开拓新功能等特点, 能有效满足科研、生产和管理部门的需要。     

An experimental metagenome data management and analysis system

The application of shotgun sequencing to environmental samples has revealed a new universe of microbial community genomes (metagenomes) involving previously uncultured organisms. Metagenome analysis, which is expected to provide a comprehensive picture of the gene functions and metabolic capacity for microbial communities, needs to be conducted in the context of a comprehensive data management and analysis system. We present in this paper IMG/M, an experimental metagenome data management and analysis system that is based on the Integrated Microbial Genomes (IMG) system. IMG/M provides tools and viewers for analyzing both metagenomes and isolate genomes individually or in a comparative context. IMG/M is available at http://img.jgi.doe.gov/m.     

Promises and pitfalls of anti-angiogenic therapy in clinical trials

A significant body of research has implicated the process of angiogenesis in the growth and spread of tumors. Elucidation of the mechanisms of tumor angiogenesis has led to the development of multiple anti-angiogenic agents. However, the perceived differences between the results of preclinical studies and those of early phases of clinical trials have led to questions being asked regarding the efficacy of these agents. There are many reasons for this discrepancy, including difficulties in the appropriate interpretation of preclinical data and clinical trial design. Further insights into the complex process of angiogenesis are essential for the development of effective anti-angiogenic regimens.