当前位置： SCI文献检索 > BMC GENOMICS期刊下所有文献 > Semantic integration of gene expression analysis tools and data sources using software connectors.

Semantic integration of gene expression analysis tools and data sources using software connectors.

Abstract：

BACKGROUND:The study and analysis of gene expression measurements is the primary focus of functional genomics. Once expression data is available, biologists are faced with the task of extracting (new) knowledge associated to the underlying biological phenomenon. Most often, in order to perform this task, biologists execute a number of analysis activities on the available gene expression dataset rather than a single analysis activity. The integration of heterogeneous tools and data sources to create an integrated analysis environment represents a challenging and error-prone task. Semantic integration enables the assignment of unambiguous meanings to data shared among different applications in an integrated environment, allowing the exchange of data in a semantically consistent and meaningful way. This work aims at developing an ontology-based methodology for the semantic integration of gene expression analysis tools and data sources. The proposed methodology relies on software connectors to support not only the access to heterogeneous data sources but also the definition of transformation rules on exchanged data. RESULTS:We have studied the different challenges involved in the integration of computer systems and the role software connectors play in this task. We have also studied a number of gene expression technologies, analysis tools and related ontologies in order to devise basic integration scenarios and propose a reference ontology for the gene expression domain. Then, we have defined a number of activities and associated guidelines to prescribe how the development of connectors should be carried out. Finally, we have applied the proposed methodology in the construction of three different integration scenarios involving the use of different tools for the analysis of different types of gene expression data. CONCLUSIONS:The proposed methodology facilitates the development of connectors capable of semantically integrating different gene expression analysis tools and data sources. The methodology can be used in the development of connectors supporting both simple and nontrivial processing requirements, thus assuring accurate data exchange and information interpretation from exchanged data.

journal_name

BMC Genomics

journal_title

BMC genomics

authors

Miyazaki FA,Guardia GD,Vêncio RZ,de Farias CR

doi

10.1186/1471-2164-14-S6-S2

subject

Has Abstract

pub_date

2013-10-25 00:00:00

pages

issn

1471-2164

pii

1471-2164-14-S6-S2

journal_volume

14 Suppl 6

pub_type

杂志文章

在线工具

Semantic integration of gene expression analysis tools and data sources using software connectors.