Interoperable genome annotation with GBOL, an extendable infrastructure for functional data mining

Abstract

BackgroundA standard structured format is used by the public sequence databases to present genome annotations. A prerequisite for a direct functional comparison is consistent annotation of the genetic elements with evidence statements. However, the current format provides limited support for data mining, hampering comparative analyses at large scale. Results The provenance of a genome annotation describes the contextual details and derivation history of the process that resulted in the annotation. To enable interoperability of genome annotations, we have developed the Genome Biology Ontology Language (GBOL) and associated infrastructure (GBOL stack). GBOL is provenance aware and thus provides a consistent representation of functional genome annotations linked to the provenance. GBOL is modular in design, extendible and linked to existing ontologies. The GBOL stack of supporting tools enforces consistency within and between the GBOL definitions in the ontology (OWL) and the Shape Expressions (ShEx) language describing the graph structure. Modules have been developed to serialize the linked data (RDF) and to generate a plain text format files. Conclusion The main rationale for applying formalized information models is to improve the exchange of information. GBOL uses and extends current ontologies to provide a formal representation of genomic entities, along with their properties and relations. The deliberate integration of data provenance in the ontology enables review of automatically obtained genome annotations at a large scale. The GBOL stack facilitates consistent usage of the ontology. RDF | Genome | AnnotationCorrespondence: maria.suarezdiez@wur.nl BackgroundAdvances in sequencing technologies have turned genomics into a data-rich scientific discipline in which the total assembled and subsequently annotated sequence data doubles every 30 months (1). To support the growth in data throughput, automated annotation algorithms have become an indispensable supplement to manual annotation (2, 3) and currently, automatic annotations in the UniProt database outnumber manual annotations 100-fold (4).Functional genome comparison has been used to identify diagnostic markers, to develop effective treatments, and to understand genotype-phenotype associations (5-7). The volume and heterogeneity of genome annotation data has created a unique type of big data challenge, namely how to transform computational predicted annotations into actionable knowledge. Tapping into these available resources is only efficiently done by computational means and requires a consistent interlinking of data so that data becomes Findable, Accessible, Interoperable and Reusable (FAIR) (8).The format for sharing of public genome sequence annotation data has been developed and is maintained by the International Nucleotide Sequence Database Collaboration (INSDC) a long-standing foundational initiative that operates between the DDBJ, EMBL-EBI and NCBI public repositories. However, tradeoffs between simplicity, human readability and representational p...

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