Standardizing data exchange for clinical research protocols and case report forms: An assessment of the suitability of the Clinical Data Interchange Standards Consortium (CDISC) Operational Data Model (ODM).

Efficient communication of a clinical study protocol and case report forms during all stages of a human clinical study is important for many stakeholders. An electronic and structured study representation format that can be used throughout the whole study life-span can improve such communication and potentially lower total study costs. The most relevant standard for representing clinical study data, applicable to unregulated as well as regulated studies, is the Operational Data Model (ODM) in development since 1999 by the Clinical Data Interchange Standards Consortium (CDISC). ODM's initial objective was exchange of case report forms data but it is increasingly utilized in other contexts. An ODM extension called Study Design Model, introduced in 2011, provides additional protocol representation elements. Using a case study approach, we evaluated ODM's ability to capture all necessary protocol elements during a complete clinical study lifecycle in the Intramural Research Program of the National Institutes of Health. ODM offers the advantage of a single format for institutions that deal with hundreds or thousands of concurrent clinical studies and maintain a data warehouse for these studies. For each study stage, we present a list of gaps in the ODM standard and identify necessary vendor or institutional extensions that can compensate for such gaps. The current version of ODM (1.3.2) has only partial support for study protocol and study registration data mainly because it is outside the original development goal. ODM provides comprehensive support for representation of case report forms (in both the design stage and with patient level data). Inclusion of requirements of observational, non-regulated or investigator-initiated studies (outside Food and Drug Administration (FDA) regulation) can further improve future revisions of the standard.

GonadSAGE: a comprehensive SAGE database for transcript discovery on male embryonic gonad development.

SUMMARY: Serial analysis of gene expression (SAGE) provides an alternative, with additional advantages, to microarray gene expression studies. GonadSAGE is the first publicly available web-based SAGE database on male gonad development that covers six male mouse embryonic gonad stages, including E10.5, E11.5, E12.5, E13.5, E15.5 and E17.5. The sequence coverage of each SAGE library is beyond 150K, 'which is the most extensive sequence-based male gonadal transcriptome to date'. An interactive web interface with customizable parameters is provided for analyzing male gonad transcriptome information. Furthermore, the data can be visualized and analyzed with the other genomic features in the UCSC genome browser. It represents an integrated platform that leads to a better understanding of male gonad development, and allows discovery of related novel targets and regulatory pathways.

GermSAGE: a comprehensive SAGE database for transcript discovery on male germ cell development.

GermSAGE is a comprehensive web-based database generated by Serial Analysis of Gene Expression (SAGE) representing major stages in mouse male germ cell development, with 150,000 sequence tags in each SAGE library. A total of 452,095 tags derived from type A spermatogonia (Spga), pachytene spermatocytes (Spcy) and round spermatids (Sptd) were included. GermSAGE provides web-based tools for browsing, comparing and searching male germ cell transcriptome data at different stages with customizable searching parameters. The data can be visualized in a tabulated format or further analyzed by aligning with various annotations available in the UCSC genome browser. This flexible platform will be useful for gaining better understanding of the genetic networks that regulate spermatogonial cell renewal and differentiation, and will allow novel gene discovery. GermSAGE is freely available at http://germsage.nichd.nih.gov/

The effect of clinical UVA/B exposures on urinary urocanic acid isomer levels in individuals with caucasian type (II/III) skin types.

Terrestrial ultraviolet radiation (UVR) exposure, consisting of ultraviolet A (320-40 nm) and B (290-320 nm), results in the photoisomerizion of epidermal trans-urocanic acid (trans-UCA) to cis-urocanic acid (cis-UCA), a potential suppressor of local and systemic immune responses. This study examines urinary UCA isomers as biomarkers of UVA/B exposure. It presents results measuring both cis- and trans-UCA in human urine samples collected from a group of study subjects (skin types II/III) that underwent controlled UVA/B exposures similar to those administered in commercial suntanning parlors. The UCA isomers were purified from urine using C18 solid-phase extraction columns followed by high-performance liquid chromatography (HPLC) with UV absorbance (268 nm) detection. The UCA biomarker was expressed as the ratio of cis-UCA to trans-UCA (UCA ratio), or as cis-UCA concentration corrected for urine volume using creatinine (cis-UCA-Cr). The UCA ratio increased over baseline in the urine of individuals exposed to UVA/B. A single exposure to approximately 70 percent minimal erythema dose (MED) of UVR (95 % UVA/5 % UVB to approximately 90 % of skin area) produced a 4.75-fold increase in the UCA ratio (p< 0.001) relative to baseline. Repeated daily UV exposures of similar doses produced a minimal increase in UCA ratio above that of the single UV exposure. These findings indicate that UCA cis-trans ratio holds promise as a biomarker for recent solar UV exposure.

Mapping genomic features of tiling microarray data by TileMapper.

The recent revolution of genomics techniques has allowed the detection of various sequence features and biological variations on whole-genome scale. However, these high-resolution data present significant challenges for experimental biologists to understand and analyze. The conventional way is to use genome browsers to locate and visualize regions of interest. But it lacks user-friendly data mining functionality. Here we present a protocol that allows rapid annotation of genomic coordinate data by using TileMapper. Interesting biological annotations from large-scale genomic data, such as transcriptome analysis, chromatin immunoprecipitation on chip, or methyl-DNA immunoprecipitation (MeDIP) studies generated from the tiling microarrays and other platforms, could be analyzed without requiring computational skills. The outputs are saved in tabulated format, which permit flexible and simple processing in spreadsheet software, or to be exported to other pipelines for subsequent analysis.