Semantic Search among Heterogeneous Biological Databases Based on Gene Ontology

2004 ◽  
Vol 36 (5) ◽  
pp. 365-370 ◽  
Author(s):  
Shun-Liang Cao ◽  
Lei Qin ◽  
Wei-Zhong He ◽  
Yang Zhong ◽  
Yang-Yong Zhu ◽  
...  
Keyword(s):  
Gene Ontology ◽  
Data Warehouse ◽  
Semantic Similarity ◽  
Biological Data ◽  
Semantic Search ◽  
Data Sources ◽  
Biological Databases ◽  
Go Terms ◽  
Correlate Gene ◽  
Record Similarity

Abstract Semantic search is a key issue in integration of heterogeneous biological databases. In this paper, we present a methodology for implementing semantic search in BioDW, an integrated biological data warehouse. Two tables are presented: the DB2GO table to correlate Gene Ontology (GO) annotated entries from BioDW data sources with GO, and the semantic similarity table to record similarity scores derived from any pair of GO terms. Based on the two tables, multifarious ways for semantic search are provided and the corresponding entries in heterogeneous biological databases in semantic terms can be expediently searched.

GO-Based Term Semantic Similarity

2011 ◽  
pp. 174-185 ◽  
Author(s):  
Marco A. Alvarez ◽  
Xiaojun Qi ◽  
Changhui Yan
Keyword(s):  
Gene Ontology ◽  
Semantic Similarity ◽  
Data Sources ◽  
Future Research ◽  
External Data ◽  
Basic Concepts ◽  
Similarity Method ◽  
Go Terms

As the Gene Ontology (GO) plays more and more important roles in bioinformatics research, there has been great interest in developing objective and accurate methods for calculating semantic similarity between GO terms. In this chapter, the authors first introduce the basic concepts related to the GO and then briefly review the current advances and challenges in the development of methods for calculating semantic similarity between GO terms. Then, the authors introduce a semantic similarity method that does not rely on external data sources. Using this method as an example, the authors show how different properties of the GO can be explored to calculate semantic similarities between pairs of GO terms. The authors conclude the chapter by presenting some thoughts on the directions for future research in this field.

GO-Based Term Semantic Similarity

2013 ◽  
pp. 93-104
Author(s):  
Marco A. Alvarez ◽  
Xiaojun Qi ◽  
Changhui Yan
Keyword(s):  
Gene Ontology ◽  
Semantic Similarity ◽  
Data Sources ◽  
Future Research ◽  
External Data ◽  
Basic Concepts ◽  
Similarity Method ◽  
Go Terms

As the Gene Ontology (GO) plays more and more important roles in bioinformatics research, there has been great interest in developing objective and accurate methods for calculating semantic similarity between GO terms. In this chapter, the authors first introduce the basic concepts related to the GO and then briefly review the current advances and challenges in the development of methods for calculating semantic similarity between GO terms. Then, the authors introduce a semantic similarity method that does not rely on external data sources. Using this method as an example, the authors show how different properties of the GO can be explored to calculate semantic similarities between pairs of GO terms. The authors conclude the chapter by presenting some thoughts on the directions for future research in this field.

scholarly journals An integrated information-based similarity measurement of gene ontology terms

2015 ◽  
Vol 12 (4) ◽  
pp. 1235-1253 ◽  
Author(s):  
Shu-Bo Zhang ◽  
Jian-Huang Lai
Keyword(s):  
Gene Ontology ◽  
Semantic Similarity ◽  
Semantic Information ◽  
Gene Expression Dataset ◽  
Similarity Measurement ◽  
Depth Information ◽  
Go Terms ◽  
Validation Experiments ◽  
Integrated Information ◽  
Common Ancestors

Measuring the semantic similarity between pairs of terms in Gene Ontology (GO) can help to compare genes that can not be compared by other computational methods. In this study, we proposed an integrated information-based similarity measurement (IISM) to calculate the semantic similarity between two GO terms by taking into account multiple common ancestors that they share, and aggregating the semantic information and depth information of the non-redundant common ancestors. Our method searches for non-redundant common ancestors in an effective way. Validation experiments were conducted on both gene expression dataset and pathway dataset, and the experimental results suggest the superiority of our method against some existing methods.

scholarly journals Enabling semantic queries across federated bioinformatics databases

Database ◽  
2019 ◽  
Vol 2019 ◽  
Author(s):  
Ana Claudia Sima ◽  
Tarcisio Mendes de Farias ◽  
Erich Zbinden ◽  
Maria Anisimova ◽  
Manuel Gil ◽  
...  
Keyword(s):  
Gene Expression ◽  
Data Integration ◽  
Heterogeneous Data ◽  
Biological Data ◽  
Data Sources ◽  
Biological Knowledge ◽  
Biological Databases ◽  
Semantic Level ◽  
Sparql Endpoint ◽  
Description Framework

Abstract Motivation: Data integration promises to be one of the main catalysts in enabling new insights to be drawn from the wealth of biological data available publicly. However, the heterogeneity of the different data sources, both at the syntactic and the semantic level, still poses significant challenges for achieving interoperability among biological databases. Results: We introduce an ontology-based federated approach for data integration. We applied this approach to three heterogeneous data stores that span different areas of biological knowledge: (i) Bgee, a gene expression relational database; (ii) Orthologous Matrix (OMA), a Hierarchical Data Format 5 orthology DS; and (iii) UniProtKB, a Resource Description Framework (RDF) store containing protein sequence and functional information. To enable federated queries across these sources, we first defined a new semantic model for gene expression called GenEx. We then show how the relational data in Bgee can be expressed as a virtual RDF graph, instantiating GenEx, through dedicated relational-to-RDF mappings. By applying these mappings, Bgee data are now accessible through a public SPARQL endpoint. Similarly, the materialized RDF data of OMA, expressed in terms of the Orthology ontology, is made available in a public SPARQL endpoint. We identified and formally described intersection points (i.e. virtual links) among the three data sources. These allow performing joint queries across the data stores. Finally, we lay the groundwork to enable nontechnical users to benefit from the integrated data, by providing a natural language template-based search interface.

A GRAPH-BASED SEMANTIC SIMILARITY MEASURE FOR THE GENE ONTOLOGY

2011 ◽  
Vol 09 (06) ◽  
pp. 681-695 ◽  
Author(s):  
MARCO A. ALVAREZ ◽  
CHANGHUI YAN
Keyword(s):  
Gene Ontology ◽  
Semantic Similarity ◽  
Common Ancestor ◽  
State Of The Art ◽  
Sequence Similarity ◽  
Similarity Score ◽  
Gene Products ◽  
Semantic Similarity Measure ◽  
Similarity Algorithm ◽  
Go Terms

Existing methods for calculating semantic similarities between pairs of Gene Ontology (GO) terms and gene products often rely on external databases like Gene Ontology Annotation (GOA) that annotate gene products using the GO terms. This dependency leads to some limitations in real applications. Here, we present a semantic similarity algorithm (SSA), that relies exclusively on the GO. When calculating the semantic similarity between a pair of input GO terms, SSA takes into account the shortest path between them, the depth of their nearest common ancestor, and a novel similarity score calculated between the definitions of the involved GO terms. In our work, we use SSA to calculate semantic similarities between pairs of proteins by combining pairwise semantic similarities between the GO terms that annotate the involved proteins. The reliability of SSA was evaluated by comparing the resulting semantic similarities between proteins with the functional similarities between proteins derived from expert annotations or sequence similarity. Comparisons with existing state-of-the-art methods showed that SSA is highly competitive with the other methods. SSA provides a reliable measure for semantics similarity independent of external databases of functional-annotation observations.

Summary Visualisations of Gene Ontology Terms with GO-Figure!

2020 ◽  
Author(s):  
Maarten JMF Reijnders ◽  
Robert M Waterhouse
Keyword(s):  
Gene Ontology ◽  
Open Source ◽  
Large Scale ◽  
Semantic Space ◽  
Biological Data ◽  
Multiple Dataset ◽  
Term Enrichment ◽  
Genomics Research ◽  
Exploratory Data ◽  
Go Terms

AbstractThe Gene Ontology (GO) is a cornerstone of functional genomics research that drives discoveries through knowledge-informed computational analysis of biological data from large- scale assays. Key to this success is how the GO can be used to support hypotheses or conclusions about the biology or evolution of a study system by identifying annotated functions that are overrepresented in subsets of genes of interest. Graphical visualisations of such GO term enrichment results are critical to aid interpretation and avoid biases by presenting researchers with intuitive visual data summaries. Amongst current visualisation tools and resources there is a lack of standalone open-source software solutions that facilitate systematic comparisons of multiple lists of GO terms. To address this we developed GO-Figure!, an open-source Python software for producing user-customisable semantic similarity scatterplots of redundancy-reduced GO term lists. The lists are simplified by grouping together GO terms with similar functions using their quantified information contents and semantic similarities, with user-control over grouping thresholds. Representatives are then selected for plotting in two-dimensional semantic space where similar GO terms are placed closer to each other on the scatterplot, with an array of user-customisable graphical attributes. GO-Figure! offers a simple solution for command-line plotting of informative summary visualisations of lists of GO terms, designed to support exploratory data analyses and multiple dataset comparisons.

scholarly journals Summary Visualizations of Gene Ontology Terms With GO-Figure!

2021 ◽  
Vol 1 ◽  
Author(s):  
Maarten J. M. F. Reijnders ◽  
Robert M. Waterhouse
Keyword(s):  
Gene Ontology ◽  
Open Source ◽  
Large Scale ◽  
Semantic Space ◽  
Biological Data ◽  
Term Enrichment ◽  
Genomics Research ◽  
Exploratory Data ◽  
Data Summaries ◽  
Go Terms

The Gene Ontology (GO) is a cornerstone of functional genomics research that drives discoveries through knowledge-informed computational analysis of biological data from large-scale assays. Key to this success is how the GO can be used to support hypotheses or conclusions about the biology or evolution of a study system by identifying annotated functions that are overrepresented in subsets of genes of interest. Graphical visualizations of such GO term enrichment results are critical to aid interpretation and avoid biases by presenting researchers with intuitive visual data summaries. Amongst current visualization tools and resources there is a lack of standalone open-source software solutions that facilitate explorations of key features of multiple lists of GO terms. To address this we developed GO-Figure!, an open-source Python software for producing user-customisable semantic similarity scatterplots of redundancy-reduced GO term lists. The lists are simplified by grouping together terms with similar functions using their quantified information contents and semantic similarities, with user-control over grouping thresholds. Representatives are then selected for plotting in two-dimensional semantic space where similar terms are placed closer to each other on the scatterplot, with an array of user-customisable graphical attributes. GO-Figure! offers a simple solution for command-line plotting of informative summary visualizations of lists of GO terms, designed to support exploratory data analyses and dataset comparisons.

scholarly journals Integration of Structured Biological Data Sources using Biological Expression Language

2019 ◽  
Author(s):  
Charles Tapley Hoyt ◽  
Daniel Domingo-Fernández ◽  
Sarah Mubeen ◽  
Josep Marin Llaó ◽  
Andrej Konotopez ◽  
...  
Keyword(s):  
Semantic Integration ◽  
Biological Data ◽  
Data Sources ◽  
Joint Analysis ◽  
Biological Databases ◽  
Multi Scale ◽  
Machine Learning Applications ◽  
Heterogeneous Multiscale ◽  
Python Programming ◽  
Pathway Databases

AbstractBackgroundThe integration of heterogeneous, multiscale, and multimodal knowledge and data has become a common prerequisite for joint analysis to unravel the mechanisms and aetiologies of complex diseases. Because of its unique ability to capture this variety, Biological Expression Language (BEL) is well suited to be further used as a platform for semantic integration and harmonization in networks and systems biology.ResultsWe have developed numerous independent packages capable of downloading, structuring, and serializing various biological data sources to BEL. Each Bio2BEL package is implemented in the Python programming language and distributed through GitHub (https://github.com/bio2bel) and PyPI.ConclusionsThe philosophy of Bio2BEL encourages reproducibility, accessibility, and democratization of biological databases. We present several applications of Bio2BEL packages including their ability to support the curation of pathway mappings, integration of pathway databases, and machine learning applications.TweetA suite of independent Python packages for downloading, parsing, warehousing, and converting multi-modal and multi-scale biological databases to Biological Expression Language

scholarly journals Enabling Semantic Queries Across Federated Bioinformatics Databases

2019 ◽  
Author(s):  
Ana Claudia Sima ◽  
Tarcisio Mendes de Farias ◽  
Erich Zbinden ◽  
Maria Anisimova ◽  
Manuel Gil ◽  
...  
Keyword(s):  
Gene Expression ◽  
Data Integration ◽  
Heterogeneous Data ◽  
Biological Data ◽  
Data Sources ◽  
Biological Knowledge ◽  
Biological Databases ◽  
Semantic Level ◽  
Sparql Endpoint ◽  
Link Type

MotivationData integration promises to be one of the main catalysts in enabling new insights to be drawn from the wealth of biological data available publicly. However, the heterogeneity of the different data sources, both at the syntactic and the semantic level, still poses significant challenges for achieving interoperability among biological databases.ResultsWe introduce an ontology-based federated approach for data integration. We applied this approach to three heterogeneous data stores that span different areas of biological knowledge: 1) Bgee, a gene expression relational database; 2) OMA, a Hierarchical Data Format 5 (HDF5) orthology data store, and 3) UniProtKB, a Resource Description Framework (RDF) store containing protein sequence and functional information. To enable federated queries across these sources, we first defined a new semantic model for gene expression called GenEx. We then show how the relational data in Bgee can be expressed as a virtual RDF graph, instantiating GenEx, through dedicated relational-to-RDF mappings. By applying these mappings, Bgee data are now accessible through a public SPARQL endpoint. Similarly, the materialised RDF data of OMA, expressed in terms of the Orthology ontology, is made available in a public SPARQL endpoint. We identified and formally described intersection points (i.e. virtual links) among the three data sources. These allow performing joint queries across the data stores. Finally, we lay the groundwork to enable nontechnical users to benefit from the integrated data, by providing a natural language template-based search interface.Project URLhttp://biosoda.expasy.org, https://github.com/biosoda/bioquery

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