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A Methodology for the Development of RESTful Semantic Web Services for Gene Expression Analysis.

Guardia GD, Pires LF, Vêncio RZ, Malmegrim KC, de Farias CR - PLoS ONE (2015)

Bottom Line: In addition, to the best of our knowledge, no suitable approach has been defined for the functional genomics domain.Our methodology provides concrete guidelines and technical details in order to facilitate the systematic development of semantic web services.Moreover, it encourages the development and reuse of these services for the creation of semantically integrated solutions for gene expression analysis.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Science and Mathematics-Faculty of Philosophy, Sciences and Letters of Ribeirão Preto (FFCLRP)-University of São Paulo (USP), Ribeirão Preto, Brazil.

ABSTRACT
Gene expression studies are generally performed through multi-step analysis processes, which require the integrated use of a number of analysis tools. In order to facilitate tool/data integration, an increasing number of analysis tools have been developed as or adapted to semantic web services. In recent years, some approaches have been defined for the development and semantic annotation of web services created from legacy software tools, but these approaches still present many limitations. In addition, to the best of our knowledge, no suitable approach has been defined for the functional genomics domain. Therefore, this paper aims at defining an integrated methodology for the implementation of RESTful semantic web services created from gene expression analysis tools and the semantic annotation of such services. We have applied our methodology to the development of a number of services to support the analysis of different types of gene expression data, including microarray and RNASeq. All developed services are publicly available in the Gene Expression Analysis Services (GEAS) Repository at http://dcm.ffclrp.usp.br/lssb/geas. Additionally, we have used a number of the developed services to create different integrated analysis scenarios to reproduce parts of two gene expression studies documented in the literature. The first study involves the analysis of one-color microarray data obtained from multiple sclerosis patients and healthy donors. The second study comprises the analysis of RNA-Seq data obtained from melanoma cells to investigate the role of the remodeller BRG1 in the proliferation and morphology of these cells. Our methodology provides concrete guidelines and technical details in order to facilitate the systematic development of semantic web services. Moreover, it encourages the development and reuse of these services for the creation of semantically integrated solutions for gene expression analysis.

No MeSH data available.


Related in: MedlinePlus

One-color microarray data analysis scenario service architecture.A rectangle represents a data source, while a rectangle with rounded corners represents a RESTful analysis service. A dotted circle represents a RESTful software connector. An one-way arrow represents a directed flow of data and/or control.
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pone.0134011.g007: One-color microarray data analysis scenario service architecture.A rectangle represents a data source, while a rectangle with rounded corners represents a RESTful analysis service. A dotted circle represents a RESTful software connector. An one-way arrow represents a directed flow of data and/or control.

Mentions: During the definition of this scenario, we have realized that interactions among services in our pipeline were not straightforward, and that some adaptation of the data to be exchanged would be required in order to guarantee service interoperability. Thus, in order to compose one service output with another service input we have defined a number of software connectors. A software connector is an architectural element used to accommodate different types of interactions among computational/data components of a software system through the definition of a set of rules that govern these interactions. In general, software connectors allow the transfer of data and/or control between different components of a software system. The identified connectors were initially developed as standalone applications using the methodology proposed in [55] and latter wrapped as RESTful web services using the methodology proposed in this work. The development and benefits of using semantic software connectors are discussed in [55]. Fig 7 shows the architecture of our analysis scenario with focus on the data flow.


A Methodology for the Development of RESTful Semantic Web Services for Gene Expression Analysis.

Guardia GD, Pires LF, Vêncio RZ, Malmegrim KC, de Farias CR - PLoS ONE (2015)

One-color microarray data analysis scenario service architecture.A rectangle represents a data source, while a rectangle with rounded corners represents a RESTful analysis service. A dotted circle represents a RESTful software connector. An one-way arrow represents a directed flow of data and/or control.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4514690&req=5

pone.0134011.g007: One-color microarray data analysis scenario service architecture.A rectangle represents a data source, while a rectangle with rounded corners represents a RESTful analysis service. A dotted circle represents a RESTful software connector. An one-way arrow represents a directed flow of data and/or control.
Mentions: During the definition of this scenario, we have realized that interactions among services in our pipeline were not straightforward, and that some adaptation of the data to be exchanged would be required in order to guarantee service interoperability. Thus, in order to compose one service output with another service input we have defined a number of software connectors. A software connector is an architectural element used to accommodate different types of interactions among computational/data components of a software system through the definition of a set of rules that govern these interactions. In general, software connectors allow the transfer of data and/or control between different components of a software system. The identified connectors were initially developed as standalone applications using the methodology proposed in [55] and latter wrapped as RESTful web services using the methodology proposed in this work. The development and benefits of using semantic software connectors are discussed in [55]. Fig 7 shows the architecture of our analysis scenario with focus on the data flow.

Bottom Line: In addition, to the best of our knowledge, no suitable approach has been defined for the functional genomics domain.Our methodology provides concrete guidelines and technical details in order to facilitate the systematic development of semantic web services.Moreover, it encourages the development and reuse of these services for the creation of semantically integrated solutions for gene expression analysis.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Science and Mathematics-Faculty of Philosophy, Sciences and Letters of Ribeirão Preto (FFCLRP)-University of São Paulo (USP), Ribeirão Preto, Brazil.

ABSTRACT
Gene expression studies are generally performed through multi-step analysis processes, which require the integrated use of a number of analysis tools. In order to facilitate tool/data integration, an increasing number of analysis tools have been developed as or adapted to semantic web services. In recent years, some approaches have been defined for the development and semantic annotation of web services created from legacy software tools, but these approaches still present many limitations. In addition, to the best of our knowledge, no suitable approach has been defined for the functional genomics domain. Therefore, this paper aims at defining an integrated methodology for the implementation of RESTful semantic web services created from gene expression analysis tools and the semantic annotation of such services. We have applied our methodology to the development of a number of services to support the analysis of different types of gene expression data, including microarray and RNASeq. All developed services are publicly available in the Gene Expression Analysis Services (GEAS) Repository at http://dcm.ffclrp.usp.br/lssb/geas. Additionally, we have used a number of the developed services to create different integrated analysis scenarios to reproduce parts of two gene expression studies documented in the literature. The first study involves the analysis of one-color microarray data obtained from multiple sclerosis patients and healthy donors. The second study comprises the analysis of RNA-Seq data obtained from melanoma cells to investigate the role of the remodeller BRG1 in the proliferation and morphology of these cells. Our methodology provides concrete guidelines and technical details in order to facilitate the systematic development of semantic web services. Moreover, it encourages the development and reuse of these services for the creation of semantically integrated solutions for gene expression analysis.

No MeSH data available.


Related in: MedlinePlus