BioGraph: a web application and a graph database for querying and analyzing bioinformatics resources

Messina, A.; Fiannaca, Antonino; Paglia, Laura La; Rosa, Massimo La; Urso, Alfonso

doi:10.1186/s12918-018-0616-4

Cited by 30 publications

(21 citation statements)

References 38 publications

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“…Recently, a number of graph-based data integration projects have appeared, including biograkn 34 , Biograph 35 , Bio4j 36 , Bio2RDF 37 , Hetionet 38 . Many of these systems were built to aggregate pathway and genotype/phenotype linkages.…”

Section: Discussionmentioning

confidence: 99%

Exploring Integrative Analysis using the BioMedical Evidence Graph

Struck

Walsh

Buchanan

et al. 2019

Preprint

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Highlights• Data resource connected extremely diverse set of cancer data sets • Graph query engine that can be easily deployed and used on new datasets • Easily installed python client • Server online at bmeg.io AbstractThe analysis of cancer biology data involves extremely heterogeneous datasets including information from RNA sequencing, genome-wide copy number, DNA methylation data reporting on epigenomic regulation, somatic mutations from whole-exome or whole-genome analyses, pathology estimates from imaging sections or subtyping, drug response or other treatment outcomes, and various other clinical and phenotypic measurements. Bringing these different resources into a common framework, with a data model that allows for complex relationships as well as dense vectors of features, will unlock integrative analysis. We introduce a graph database and query engine for discovery and analysis of cancer biology, called the BioMedical Evidence Graph (BMEG). The BMEG is unique from other biological data graphs in that sample level molecular information is connected to reference knowledge bases. It combines gene expression and mutation data, with drug response experiments, pathway information databases and literature derived associations. The construction of the BMEG has resulted in a graph containing over 36M vertices and 29M edges. The BMEG system provides a graph query based API to enable analysis, with client code available for Python, Javascript and R, and a server online at bmeg.io. Using this system we have developed several forms of integrated analysis to demonstrate the utility of the system. The BMEG is an evolving resource dedicated to enabling integrative analysis. We have demonstrated queries on the system that illustrate mutation significance analysis, drug response machine learning, patient level knowledge base queries and pathway level analysis. We have compared the resulting graph to other available integrated graph systems, and demonstrated that it is unique in the scale of the graph and the type of data it makes available. SummaryThe analysis of cancer biology data involves extremely heterogeneous datasets including information. Bringing these different resources into a common framework, with a data model that allows for complex relationships as well as dense vectors of features, will unlock integrative analysis. We introduce a graph database and query engine for discovery and analysis of cancer biology, called the BioMedical Evidence Graph (BMEG). The construction of the BMEG has resulted in a graph containing over 36M vertices and 29M edges. The BMEG system provides a graph query based API to enable analysis, with client code available for Python, Javascript and R, and a server online at bmeg.io. Using this system we have developed several forms of integrated analysis to demonstrate the utility of the system. Publication:ncbi.nlm.nih.gov/pubmed/27269946 1033 Publication:ncbi.nlm.nih.gov/pubmed/27174596 1029 Publication:ncbi.nlm.nih.gov/pubmed/19223544 858 Publication:ncbi.nlm.nih.gov/pubmed/20619739 6...

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Section: Discussionmentioning

confidence: 99%

Exploring Integrative Analysis using the BioMedical Evidence Graph

Struck

Walsh

Buchanan

et al. 2019

Preprint

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“…There are numerous studies in the literature that aimed to integrate the available biomedical data [1][2][3][4][5][6][7][8][9][10] . These studies provided useful tools and methods to the life-sciences research community; however, many of them miss important functionalities that prevent them from becoming widely adopted tools/services (Supplementary Information section 1).…”

Section: Mainmentioning

confidence: 99%

CROssBAR: Comprehensive Resource of Biomedical Relations with Deep Learning Applications and Knowledge Graph Representations

Doğan

Ataş²,

Joshi

et al. 2020

Preprint

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Systemic analysis of available large-scale biological and biomedical data is critical for developing novel and effective treatment approaches against both complex and infectious diseases. Owing to the fact that different sections of the biomedical data is produced by different organizations/institutions using various types of technologies, the data are scattered across individual computational resources, without any explicit relations/connections to each other, which greatly hinders the comprehensive multi-omics-based analysis of data. We aimed to address this issue by constructing a new biological and biomedical data resource, CROssBAR, a comprehensive system that integrates large-scale biomedical data from various resources and store them in a new NoSQL database, enrich these data with deep-learning-based prediction of relations between numerous biomedical entities, rigorously analyse the enriched data to obtain biologically meaningful modules and display them to users via easy-to-interpret, interactive and heterogenous knowledge graph (KG) representations within an open access, user-friendly and online web-service at https://crossbar.kansil.org. As a use-case study, we constructed CROssBAR COVID-19 KGs (available at: https://crossbar.kansil.org/covid_main.php) that incorporate relevant virus and host genes/proteins, interactions, pathways, phenotypes and other diseases, as well as known and completely new predicted drugs/compounds. Our COVID-19 graphs can be utilized for a systems-level evaluation of relevant virus-host protein interactions, mechanisms, phenotypic implications and potential interventions.

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“…We instead use ontologies to find a common ground between the descriptions and terminologies used in different sources. Among a number of integrated databases in the bioinformatics domain that employ graph-based paradigms, we cite: BioGraphDB [10], a resource to query, visualize and analyze biological data belonging to several online available sources (focused on genes, proteins, miRNAs, pathways); Bio4j [12], a platform integrating semantically rich biological data (focused on proteins, functional annotations); ncRNA-DB [3], integrating associations among non-coding RNAs and other functional elements.…”

Section: Related Workmentioning

confidence: 99%

From a Conceptual Model to a Knowledge Graph for Genomic Datasets

Bernasconi

Canakoglu

Ceri

2019

Conceptual Modeling

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Data access at genomic repositories is problematic, as data is described by heterogeneous and hardly comparable metadata. We previously introduced a unified conceptual schema, collected metadata in a single repository and provided classical search methods upon them. We here propose a new paradigm to support semantic search of integrated genomic metadata, based on the Genomic Knowledge Graph, a semantic graph of genomic terms and concepts, which combines the original information provided by each source with curated terminological content from specialized ontologies. Commercial knowledge-assisted search is designed for transparently supporting keyword-based search without explaining inferences; in biology, inference understanding is instead critical. For this reason, we propose a graph-based visual search for data exploration; some expert users can navigate the semantic graph along the conceptual schema, enriched with simple forms of homonyms and term hierarchies, thus understanding the semantic reasoning behind query results.

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BioGraph: a web application and a graph database for querying and analyzing bioinformatics resources

Cited by 30 publications

References 38 publications

Exploring Integrative Analysis using the BioMedical Evidence Graph

Exploring Integrative Analysis using the BioMedical Evidence Graph

CROssBAR: Comprehensive Resource of Biomedical Relations with Deep Learning Applications and Knowledge Graph Representations

From a Conceptual Model to a Knowledge Graph for Genomic Datasets

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