Limits...
Answering biological questions: querying a systems biology database for nutrigenomics.

Evelo CT, van Bochove K, Saito JT - Genes Nutr (2010)

Bottom Line: Our contribution points out critical points, describes several technical hurdles.It demonstrates how pathway analysis can improve queries and comparisons for nutrition studies.Finally, three directions for future research are given.

View Article: PubMed Central - PubMed

ABSTRACT
The requirement of systems biology for connecting different levels of biological research leads directly to a need for integrating vast amounts of diverse information in general and of omics data in particular. The nutritional phenotype database addresses this challenge for nutrigenomics. A particularly urgent objective in coping with the data avalanche is making biologically meaningful information accessible to the researcher. This contribution describes how we intend to meet this objective with the nutritional phenotype database. We outline relevant parts of the system architecture, describe the kinds of data managed by it, and show how the system can support retrieval of biologically meaningful information by means of ontologies, full-text queries, and structured queries. Our contribution points out critical points, describes several technical hurdles. It demonstrates how pathway analysis can improve queries and comparisons for nutrition studies. Finally, three directions for future research are given.

No MeSH data available.


Schematic overview of the dbNP with generic study capturing framework (GSF), simple assay, transcriptomics, and metabolomics module
© Copyright Policy
Related In: Results  -  Collection


getmorefigures.php?uid=PMC3040802&req=5

Fig1: Schematic overview of the dbNP with generic study capturing framework (GSF), simple assay, transcriptomics, and metabolomics module

Mentions: To facilitate the complex functionality, the dbNP is realized in a modular architecture. Each module contains specific data or functionality to operate on special data (cf. Fig. 1). For instance, the transcriptomics module stores raw data from assays used in nutrition studies and also preprocesses and organizes the data for retrieval in combination with other modules. The Simple Assay Module contains all clinical measurement data and units, reference values, and uniform terminology, for referring to measurements such as cholesterol levels in blood samples, or weights of liver samples. Similarly, the metabolomics data stores its raw data as peak tables, which in processed form lead to metabolite concentrations.Fig. 1


Answering biological questions: querying a systems biology database for nutrigenomics.

Evelo CT, van Bochove K, Saito JT - Genes Nutr (2010)

Schematic overview of the dbNP with generic study capturing framework (GSF), simple assay, transcriptomics, and metabolomics module
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3040802&req=5

Fig1: Schematic overview of the dbNP with generic study capturing framework (GSF), simple assay, transcriptomics, and metabolomics module
Mentions: To facilitate the complex functionality, the dbNP is realized in a modular architecture. Each module contains specific data or functionality to operate on special data (cf. Fig. 1). For instance, the transcriptomics module stores raw data from assays used in nutrition studies and also preprocesses and organizes the data for retrieval in combination with other modules. The Simple Assay Module contains all clinical measurement data and units, reference values, and uniform terminology, for referring to measurements such as cholesterol levels in blood samples, or weights of liver samples. Similarly, the metabolomics data stores its raw data as peak tables, which in processed form lead to metabolite concentrations.Fig. 1

Bottom Line: Our contribution points out critical points, describes several technical hurdles.It demonstrates how pathway analysis can improve queries and comparisons for nutrition studies.Finally, three directions for future research are given.

View Article: PubMed Central - PubMed

ABSTRACT
The requirement of systems biology for connecting different levels of biological research leads directly to a need for integrating vast amounts of diverse information in general and of omics data in particular. The nutritional phenotype database addresses this challenge for nutrigenomics. A particularly urgent objective in coping with the data avalanche is making biologically meaningful information accessible to the researcher. This contribution describes how we intend to meet this objective with the nutritional phenotype database. We outline relevant parts of the system architecture, describe the kinds of data managed by it, and show how the system can support retrieval of biologically meaningful information by means of ontologies, full-text queries, and structured queries. Our contribution points out critical points, describes several technical hurdles. It demonstrates how pathway analysis can improve queries and comparisons for nutrition studies. Finally, three directions for future research are given.

No MeSH data available.