Using semantic web rules to reason on an ontology of pseudogenes

Holford, Matthew; Khurana, Ekta; Cheung, Kei‐Hoi; Gerstein, Mark

doi:10.1093/bioinformatics/btq173

Cited by 16 publications

(12 citation statements)

References 15 publications

(14 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The hierarchy of these properties is shown in Supplemental Figure 3. The ontology allows not only comprehensive annotation of pseudogenes but also automatic queries against the pseudogene knowledge database (Holford et al 2010). The breakdown of the different biotypes within the GENCODE data set can be seen in Supplemental Table 4.…”

Section: Integration Of Pseudogenes Into Gencodementioning

confidence: 99%

GENCODE: The reference human genome annotation for The ENCODE Project

et al. 2012

View full text Add to dashboard Cite

The GENCODE Consortium aims to identify all gene features in the human genome using a combination of computational analysis, manual annotation, and experimental validation. Since the first public release of this annotation data set, few new protein-coding loci have been added, yet the number of alternative splicing transcripts annotated has steadily increased. The GENCODE 7 release contains 20,687 protein-coding and 9640 long noncoding RNA loci and has 33,977 coding transcripts not represented in UCSC genes and RefSeq. It also has the most comprehensive annotation of long noncoding RNA (lncRNA) loci publicly available with the predominant transcript form consisting of two exons. We have examined the completeness of the transcript annotation and found that 35% of transcriptional start sites are supported by CAGE clusters and 62% of protein-coding genes have annotated polyA sites. Over one-third of GENCODE proteincoding genes are supported by peptide hits derived from mass spectrometry spectra submitted to Peptide Atlas. New models derived from the Illumina Body Map 2.0 RNA-seq data identify 3689 new loci not currently in GENCODE, of which 3127 consist of two exon models indicating that they are possibly unannotated long noncoding loci. GENCODE 7 is publicly available from gencodegenes.org and via the Ensembl and UCSC Genome Browsers.

show abstract

Section: Integration Of Pseudogenes Into Gencodementioning

confidence: 99%

GENCODE: The reference human genome annotation for The ENCODE Project

et al. 2012

View full text Add to dashboard Cite

show abstract

“…On the other hand, OWL has been designed from its inception to enable computer-assisted processing. Sadly enough, we and others observed that reasoning over large OWL ontologies suffers from severe computational tractability problems [11,22].…”

Section: Metarelmentioning

confidence: 94%

“…Metarel is an RDF vocabulary that utilizes some of the language constructs offered by OWL Full, and it provides logical semantics to relations between classes. Deployment of Metarel in combination with the SPARQL/Update language [22] on an RDF store implements a query system that makes the implicit knowledge explicit and augments the store with inferred knowledge. This is achieved through the use of five rules (closures): Reflexivity, Transitivity, Priority over Subsumption, Super-relations and Chains.…”

Section: Metarelmentioning

confidence: 99%

Semantic systems biology

Antezana

Blondé

Venkatesan

et al. 2011

Proceedings of the International Conference on Web Intelligence, Mining and Semantics

View full text Add to dashboard Cite

The vast amounts of knowledge in the biomedical domain have paved the way for a new paradigm in biological research called Systems Biology, essentially an approach that relies on the integration of all available knowledge of a biological system in a single model. This approach promotes a comprehensive understanding of biological systems, driven by data integration and mathematical modelling. However, the sheer volume, variation and complexity of the current biological data pose a number of hurdles in knowledge management that need to be overcome. The Semantic Web offers various solutions to these challenges. With our initiative, named Semantic Systems Biology (SSB), we augment the systems biology approach with semantic web technologies to enable smooth data integration, rigorous knowledge representation, efficient querying, and hypothesis generation. Here we present an overview of the projects associated with the SSB initiative. Access to our resources developed within the SSB frame is provided on our website: http://www.semantic-systems-biology.org.

show abstract

“…53 These three support the features of OWL 2, although only Pellet and HermiT support SWRL rules. However, the performance of these reasoner engines is compromised in the case of very large and complex ontologies, 54 and as of yet unfortunately, none of them is able to process the whole version of DINTO. Therefore, we adopt different strategies to simplify the ontology while maintaining the information that is necessary to retrieve the desired inferences.…”

Section: ■ Inference Experimentsmentioning

confidence: 99%

DINTO: Using OWL Ontologies and SWRL Rules to Infer Drug–Drug Interactions and Their Mechanisms

Herrero-Zazo

Segura-Bedmar

Hastings

et al. 2015

J. Chem. Inf. Model.

View full text Add to dashboard Cite

The early detection of drug-drug interactions (DDIs) is limited by the diffuse spread of DDI information in heterogeneous sources. Computational methods promise to play a key role in the identification and explanation of DDIs on a large scale. However, such methods rely on the availability of computable representations describing the relevant domain knowledge. Current modeling efforts have focused on partial and shallow representations of the DDI domain, failing to adequately support computational inference and discovery applications. In this paper, we describe a comprehensive ontology for DDI knowledge (DINTO), which is the first formal representation of different types of DDIs and their mechanisms and its application in the prediction of DDIs. This project has been developed using currently available semantic web technologies, standards, and tools, and we have demonstrated that the combination of drug-related facts in DINTO and Semantic Web Rule Language (SWRL) rules can be used to infer DDIs and their different mechanisms on a large scale. The ontology is available from https://code.google.com/p/dinto/.

show abstract

Using semantic web rules to reason on an ontology of pseudogenes

Cited by 16 publications

References 15 publications

GENCODE: The reference human genome annotation for The ENCODE Project

GENCODE: The reference human genome annotation for The ENCODE Project

Semantic systems biology

DINTO: Using OWL Ontologies and SWRL Rules to Infer Drug–Drug Interactions and Their Mechanisms

Contact Info

Product

Resources

About