Abstract
BackgroundThe 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 heteregeneous 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.ResultsWe 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.ConclusionsThe 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.
Highlights
The study and analysis of gene expression measurements is the primary focus of functional genomics
We have studied a number of gene expression technologies, analysis tools and related ontologies in order to devise basic integration scenarios and propose a gene expression domain ontology
Five basic integration scenarios can be identified: (a) data stored in D are transferred to TA; (b) data produced by TA are transferred to D; (c) data stored in D are transferred to TA and later data produced by TA are transferred back to D; (d) data and/or control from TA are transferred to TB; and (e) data and/or control from TA are transferred to TB and later data and/or control from TB are transferred back to TA
Summary
The study and analysis of gene expression measurements is the primary focus of functional genomics. 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. This work aims at developing an ontology-based methodology for the semantic integration of gene expression analysis tools and data sources. High-throughput expression measurements of entire transcriptomes can be obtained through different techniques. Once expression data is available, biologists are faced with the task of extracting (new) knowledge associated to the underlying biological phenomenon. In order to carry out this task, biologists perform a sequence of analysis activities on the available gene expression dataset rather than a single analysis activity. Analysis activities include data normalization, identification of differentially expressed genes, pathway analysis, cluster analysis and classification, functional annotation and modelling gene regulatory networks
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