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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.


Overview: studies with regulation on selected pathway (screenshot of prototype)
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Fig3: Overview: studies with regulation on selected pathway (screenshot of prototype)

Mentions: Figure 2 shows how two sets of array data are compared. In this case, the data stems from different tissues within the same study. The two subtables represent the pathways in both kinds of tissue (liver on the left-hand, muscle on the right-hand side). The pathways in both tables are sorted by the strength of the regulation. Thus, the Z-scores are decreasing from top to bottom. In Fig. 3, additional information on the pathway for the linoleic acid metabolism is given. This pathway appeared in the liver tissue sample of Fig. 2 in which it ranked fifth in regulation based on its Z score. Figure 3 indicates a list of studies in which the same pathway is regulated. Other liver samples appear among the listed studies indicating potentially related outcomes.Fig. 2


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

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

Overview: studies with regulation on selected pathway (screenshot of prototype)
© Copyright Policy
Related In: Results  -  Collection

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

Fig3: Overview: studies with regulation on selected pathway (screenshot of prototype)
Mentions: Figure 2 shows how two sets of array data are compared. In this case, the data stems from different tissues within the same study. The two subtables represent the pathways in both kinds of tissue (liver on the left-hand, muscle on the right-hand side). The pathways in both tables are sorted by the strength of the regulation. Thus, the Z-scores are decreasing from top to bottom. In Fig. 3, additional information on the pathway for the linoleic acid metabolism is given. This pathway appeared in the liver tissue sample of Fig. 2 in which it ranked fifth in regulation based on its Z score. Figure 3 indicates a list of studies in which the same pathway is regulated. Other liver samples appear among the listed studies indicating potentially related outcomes.Fig. 2

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.