Dataset Information

GRAPE: GRaphical Abstracted Protein Explorer.

ABSTRACT: The region surrounding a protein, known as the surface of interaction or molecular surface, can provide valuable insight into its function. Unfortunately, due to the complexity of both their geometry and their surface fields, study of these surfaces can be slow and difficult and important features may be hard to identify. Here, we describe our GRaphical Abstracted Protein Explorer, or GRAPE, a web server that allows users to explore abstracted representations of proteins. These abstracted surfaces effectively reduce the level of detail of the surface of a macromolecule, using a specialized algorithm that removes small bumps and pockets, while preserving large-scale structural features. Scalar fields, such as electrostatic potential and hydropathy, are smoothed to further reduce visual complexity. This entirely new way of looking at proteins complements more traditional views of the molecular surface. GRAPE includes a thin 3D viewer that allows users to quickly flip back and forth between both views. Abstracted views provide a fast way to assess both a molecule's shape and its different surface field distributions. GRAPE is freely available at http://grape.uwbacter.org.

SUBMITTER: Cipriano G

PROVIDER: S-EPMC2896102 | biostudies-literature | 2010 Jul

REPOSITORIES: biostudies-literature

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GRAPE: GRaphical Abstracted Protein Explorer.

Cipriano Gregory G Wesenberg Gary G Grim Tom T Phillips George N GN Gleicher Michael M

Nucleic acids research 20100512 Web Server issue

The region surrounding a protein, known as the surface of interaction or molecular surface, can provide valuable insight into its function. Unfortunately, due to the complexity of both their geometry and their surface fields, study of these surfaces can be slow and difficult and important features may be hard to identify. Here, we describe our GRaphical Abstracted Protein Explorer, or GRAPE, a web server that allows users to explore abstracted representations of proteins. These abstracted surfac ...[more]

PMID: 20462864

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Project description:Medical record abstraction (MRA) is often cited as a significant source of error in research data, yet MRA methodology has rarely been the subject of investigation. Lack of a common framework has hindered application of the extant literature in practice, and, until now, there were no evidence-based guidelines for ensuring data quality in MRA. We aimed to identify the factors affecting the accuracy of data abstracted from medical records and to generate a framework for data quality assurance and control in MRA.Candidate factors were identified from published reports of MRA. Content validity of the top candidate factors was assessed via a four-round two-group Delphi process with expert abstractors with experience in clinical research, registries, and quality improvement. The resulting coded factors were categorized into a control theory-based framework of MRA. Coverage of the framework was evaluated using the recent published literature.Analysis of the identified articles yielded 292 unique factors that affect the accuracy of abstracted data. Delphi processes overall refuted three of the top factors identified from the literature based on importance and five based on reliability (six total factors refuted). Four new factors were identified by the Delphi. The generated framework demonstrated comprehensive coverage. Significant underreporting of MRA methodology in recent studies was discovered.The framework generated from this research provides a guide for planning data quality assurance and control for studies using MRA. The large number and variability of factors indicate that while prospective quality assurance likely increases the accuracy of abstracted data, monitoring the accuracy during the abstraction process is also required. Recent studies reporting research results based on MRA rarely reported data quality assurance or control measures, and even less frequently reported data quality metrics with research results. Given the demonstrated variability, these methods and measures should be reported with research results.

Dataset Information

GRAPE: GRaphical Abstracted Protein Explorer.

Publications

GRAPE: GRaphical Abstracted Protein Explorer.

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