STON: exploring biological pathways using the SBGN standard and graph databases

Touré, Vasundra; Mazein, Alexander; Waltemath, Dagmar; Balaur, Irina; Saqi, Mansoor; Henkel, Ron; Pellet, Johann; Auffray, Charles

doi:10.1186/s12859-016-1394-x

Cited by 22 publications

(16 citation statements)

References 28 publications

(28 reference statements)

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“…Some software tools already visualize element alignments between models as network graphs (e.g. BudHat [ 53 ], SemanticSBML, STON [ 61 ]), or present ranking scores for retrieved models (e.g. MASYMOS [ 45 ], SemanticSBML).…”

Section: Discussionmentioning

confidence: 99%

Notions of similarity for systems biology models

Henkel¹,

Hoehndorf²,

Kacprowski³

et al. 2016

Brief Bioinform

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Systems biology models are rapidly increasing in complexity, size and numbers. When building large models, researchers rely on software tools for the retrieval, comparison, combination and merging of models, as well as for version control. These tools need to be able to quantify the differences and similarities between computational models. However, depending on the specific application, the notion of ‘similarity’ may greatly vary. A general notion of model similarity, applicable to various types of models, is still missing. Here we survey existing methods for the comparison of models, introduce quantitative measures for model similarity, and discuss potential applications of combined similarity measures. To frame model comparison as a general problem, we describe a theoretical approach to defining and computing similarities based on a combination of different model aspects. The six aspects that we define as potentially relevant for similarity are underlying encoding, references to biological entities, quantitative behaviour, qualitative behaviour, mathematical equations and parameters and network structure. We argue that future similarity measures will benefit from combining these model aspects in flexible, problem-specific ways to mimic users’ intuition about model similarity, and to support complex model searches in databases.

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Section: Discussionmentioning

confidence: 99%

Notions of similarity for systems biology models

Henkel¹,

Hoehndorf²,

Kacprowski³

et al. 2016

Brief Bioinform

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“…In [ 9 ] the authors explored the potential of using a graph database to facilitate data management and analysis to provide biological context to disease-related genes and proteins. Toure et al developed a Java-based framework that transforms biological pathways represented in SBGN format into the Neo4j graph database, enabling more powerful management and querying of complex biological networks [ 10 ]. Balaur et al demonstrated that advanced exploration of highly connected and comprehensive genome-scale metabolic reconstructions can benefit from an integrated graph representation of the model and associated data [ 11 ].…”

Section: Design and Implementationmentioning

confidence: 99%

Reactome graph database: Efficient access to complex pathway data

Fabregat¹,

Korninger²,

Viteri³

et al. 2018

PLoS Comput Biol

232

196

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Reactome is a free, open-source, open-data, curated and peer-reviewed knowledgebase of biomolecular pathways. One of its main priorities is to provide easy and efficient access to its high quality curated data. At present, biological pathway databases typically store their contents in relational databases. This limits access efficiency because there are performance issues associated with queries traversing highly interconnected data. The same data in a graph database can be queried more efficiently. Here we present the rationale behind the adoption of a graph database (Neo4j) as well as the new ContentService (REST API) that provides access to these data. The Neo4j graph database and its query language, Cypher, provide efficient access to the complex Reactome data model, facilitating easy traversal and knowledge discovery. The adoption of this technology greatly improved query efficiency, reducing the average query time by 93%. The web service built on top of the graph database provides programmatic access to Reactome data by object oriented queries, but also supports more complex queries that take advantage of the new underlying graph-based data storage. By adopting graph database technology we are providing a high performance pathway data resource to the community. The Reactome graph database use case shows the power of NoSQL database engines for complex biological data types.

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“…Annotations have contributed to the successful reuse and exploration of models and data in tasks such as comparison 27,28 , interpretation 29 , retrieval [30][31][32] , integration [33][34][35][36][37] , simulation 38 , translation between formats 29,37,[39][40][41][42] (see also http://sbfc.sourceforge.net/mediawiki/index.php/SBML2BioPAX), and visualization 37,[43][44][45] . Semantic annotations are also a key component for model-driven design of synthetic biological systems where they are used in model composition tasks when constructing optimum biological systems built from models 41,[45][46][47][48] .…”

Section: Semantic Annotations and Their Utilitymentioning

confidence: 99%

Harmonizing semantic annotations for computational models in biology

Neal

König

Nickerson

et al. 2018

Preprint

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Life science researchers use computational models to articulate and test hypotheses about the behavior of biological systems. Semantic annotation is a critical component for enhancing the interoperability and reusability of such models as well as for the integration of the data needed for model parameterization and validation. Encoded as machine-readable links to knowledge resource terms, semantic annotations describe the computational or biological meaning of what models and data represent. These annotations help researchers find and repurpose models, accelerate model composition, and enable knowledge integration across model repositories and experimental data stores. However, realizing the potential benefits of semantic annotation requires the development of model annotation standards that adhere to a community-based annotation protocol. Without such standards, tool developers must account for a variety of annotation formats and approaches, a situation that can become prohibitively cumbersome and which can defeat the purpose of linking model elements to controlled knowledge resource terms. Currently, no consensus protocol for semantic annotation exists among the larger biological modeling community. Here, we report on the landscape of current semantic annotation practices among the COmputational Modeling in BIology NEtwork (COMBINE) community and provide a set of recommendations for building a consensus approach to semantic annotation.All rights reserved. No reuse allowed without permission.(which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.

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STON: exploring biological pathways using the SBGN standard and graph databases

Cited by 22 publications

References 28 publications

Notions of similarity for systems biology models

Notions of similarity for systems biology models

Reactome graph database: Efficient access to complex pathway data

Harmonizing semantic annotations for computational models in biology

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