Bioassay ontology to describe high-throughput screening assays and their results

Uma Vempati; Ubbo Visser; Saminda Abeyruwan; Kunie Sakurai; Magdalena Przydzial; Caty Chung; Robin Smith; Ainar Koleti; Christopher Mader; Vance Lemmon; Stephan Schürer

Bioassay ontology to describe high-throughput screening assays and their results

Uma Vempati, Ubbo Visser, Saminda Abeyruwan, Kunie Sakurai, Magdalena Przydzial, Caty Chung, Robin Smith, Ainar Koleti, Christopher Mader, Vance Lemmon, Stephan Schürer

Research output: Contribution to journal › Conference article › peer-review

Abstract

Huge amounts of high-throughput screening (HTS) data are generated in the pharmaceutical industry and more recently in the public sector. These are typically analyzed on a per-project basis. Comparison and analysis across many diverse HTS datasets are hindered by the lack of standardized descriptions of biological assays and screening results. Here, we present the BioAssay Ontology (BAO), which enables the categorization of biological assays by concepts relevant to interpret and compare HTS data and thus facilitates data analysis across many HTS campaigns. We used BAO to annotate assays from the largest public HTS data repository. PubChem. Here we demonstrate how BAO can be applied to access and analyze HTS data. BAO makes use of expressive description logic and has potential for discovering implicit knowledge using inference. BAO is publically available from the NCBO BioPortal at http://bioportal.bioontology. org/ontologies/44531.

Original language	English (US)
Pages (from-to)	209-216
Number of pages	8
Journal	CEUR Workshop Proceedings
Volume	833
State	Published - Dec 1 2011
Event	2nd International Conference on Biomedical Ontology, ICBO 2011 - Buffalo, NY, United States Duration: Jul 26 2011 → Jul 30 2011

Keywords

Assay ontology
Bioassay
Bioassay ontology
Biological assay
Biological screening
Data analysis
Description logic
High-throughput screening
HTS
Semantic integration

ASJC Scopus subject areas

Computer Science(all)

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Bioassay ontology to describe high-throughput screening assays and their results. / Vempati, Uma; Visser, Ubbo; Abeyruwan, Saminda; Sakurai, Kunie; Przydzial, Magdalena; Chung, Caty; Smith, Robin; Koleti, Ainar; Mader, Christopher; Lemmon, Vance ; Schürer, Stephan.

In: CEUR Workshop Proceedings, Vol. 833, 01.12.2011, p. 209-216.

Research output: Contribution to journal › Conference article › peer-review

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AU - Vempati, Uma

AU - Visser, Ubbo

AU - Abeyruwan, Saminda

AU - Sakurai, Kunie

AU - Przydzial, Magdalena

AU - Chung, Caty

AU - Smith, Robin

AU - Koleti, Ainar

AU - Mader, Christopher

AU - Lemmon, Vance

AU - Schürer, Stephan

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N2 - Huge amounts of high-throughput screening (HTS) data are generated in the pharmaceutical industry and more recently in the public sector. These are typically analyzed on a per-project basis. Comparison and analysis across many diverse HTS datasets are hindered by the lack of standardized descriptions of biological assays and screening results. Here, we present the BioAssay Ontology (BAO), which enables the categorization of biological assays by concepts relevant to interpret and compare HTS data and thus facilitates data analysis across many HTS campaigns. We used BAO to annotate assays from the largest public HTS data repository. PubChem. Here we demonstrate how BAO can be applied to access and analyze HTS data. BAO makes use of expressive description logic and has potential for discovering implicit knowledge using inference. BAO is publically available from the NCBO BioPortal at http://bioportal.bioontology. org/ontologies/44531.

AB - Huge amounts of high-throughput screening (HTS) data are generated in the pharmaceutical industry and more recently in the public sector. These are typically analyzed on a per-project basis. Comparison and analysis across many diverse HTS datasets are hindered by the lack of standardized descriptions of biological assays and screening results. Here, we present the BioAssay Ontology (BAO), which enables the categorization of biological assays by concepts relevant to interpret and compare HTS data and thus facilitates data analysis across many HTS campaigns. We used BAO to annotate assays from the largest public HTS data repository. PubChem. Here we demonstrate how BAO can be applied to access and analyze HTS data. BAO makes use of expressive description logic and has potential for discovering implicit knowledge using inference. BAO is publically available from the NCBO BioPortal at http://bioportal.bioontology. org/ontologies/44531.

KW - Assay ontology

KW - Bioassay

KW - Bioassay ontology

KW - Biological assay

KW - Biological screening

KW - Data analysis

KW - Description logic

KW - High-throughput screening

KW - HTS

KW - Semantic integration

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Y2 - 26 July 2011 through 30 July 2011

ER -

Bioassay ontology to describe high-throughput screening assays and their results

Abstract

Keywords

ASJC Scopus subject areas

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