Where to search top-K biomedical ontologies?

Oliveira, Daniela; Butt, Anila Sahar; Haller, Armin; Rebholz‐Schuhmann, Dietrich; Sahay, Ratnesh

doi:10.1093/bib/bby015

Cited by 6 publications

(4 citation statements)

References 52 publications

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“…We proposed a method to semantically annotate traits with an automatic process. Semantic enrichment of metadata with appropriate ontologies [ 74 ] has been tackled both with source-independent methods [ 75 , 76 ] and with source-specific ones [ 77 , 78 ]. Several GWAS sources are already working in the direction of homogenizing their values and linking them to existing ontologies.…”

Section: Discussionmentioning

confidence: 99%

Processing genome-wide association studies within a repository of heterogeneous genomic datasets

2023

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Background Genome Wide Association Studies (GWAS) are based on the observation of genome-wide sets of genetic variants – typically single-nucleotide polymorphisms (SNPs) – in different individuals that are associated with phenotypic traits. Research efforts have so far been directed to improving GWAS techniques rather than on making the results of GWAS interoperable with other genomic signals; this is currently hindered by the use of heterogeneous formats and uncoordinated experiment descriptions. Results To practically facilitate integrative use, we propose to include GWAS datasets within the META-BASE repository, exploiting an integration pipeline previously studied for other genomic datasets that includes several heterogeneous data types in the same format, queryable from the same systems. We represent GWAS SNPs and metadata by means of the Genomic Data Model and include metadata within a relational representation by extending the Genomic Conceptual Model with a dedicated view. To further reduce the gap with the descriptions of other signals in the repository of genomic datasets, we perform a semantic annotation of phenotypic traits. Our pipeline is demonstrated using two important data sources, initially organized according to different data models: the NHGRI-EBI GWAS Catalog and FinnGen (University of Helsinki). The integration effort finally allows us to use these datasets within multi-sample processing queries that respond to important biological questions. These are then made usable for multi-omic studies together with, e.g., somatic and reference mutation data, genomic annotations, epigenetic signals. Conclusions As a result of the our work on GWAS datasets, we enable 1) their interoperable use with several other homogenized and processed genomic datasets in the context of the META-BASE repository; 2) their big data processing by means of the GenoMetric Query Language and associated system. Future large-scale tertiary data analysis may extensively benefit from the addition of GWAS results to inform several different downstream analysis workflows.

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Section: Discussionmentioning

confidence: 99%

Processing genome-wide association studies within a repository of heterogeneous genomic datasets

2023

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“…The choice of the ontologies to be used for semantic enrichment, discussed in Section 5.2.1, has been addressed in a number of articles; among them, [29] and [45]. The latter one presents the FAIR principles, which define the characteristics that contemporary data resources, tools and infrastructures should exhibit in order to be Findable, Accessible, Interoperable and Reusable by third parties.…”

Section: Discussionmentioning

confidence: 99%

META-BASE: A Novel Architecture for Large-Scale Genomic Metadata Integration

Bernasconi

Canakoglu

Masseroli

et al. 2022

IEEE/ACM Trans. Comput. Biol. and Bioinf.

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The integration of genomic metadata is, at the same time, an important, difficult, and well-recognized challenge. It is important because a wealth of public data repositories is available to drive biological and clinical research; combining information from various heterogeneous and widely dispersed sources is paramount to a number of biological discoveries. It is difficult because the domain is complex and there is no agreement among the various metadata definitions, which refer to different vocabularies and ontologies. It is well-recognized in the bioinformatics community because, in the common practice, repositories are accessed one-by-one, learning their specific metadata definitions as result of long and tedious efforts, and such practice is error-prone. In this paper, we describe META-BASE, an architecture for integrating metadata extracted from a variety of genomic data sources, based upon a structured transformation process. We present a variety of innovative techniques for data extraction, cleaning, normalization and enrichment. The result is a repository that already integrates several important sources, and a general, open and extensible pipeline that can easily incorporate any number of new data sources.

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“…Past analyses of the pan-ontology landscape have reviewed structural aspects ontologies, but without direct relation to ML. For example, the extent of overlap between ontology projects 30,31 has been quantified, and has been shown to impact how researchers can find ontology terms to annotate a dataset 32 . From a different perspective, the properties of ontology graphs have been shown to vary substantially across projects 33 .…”

Section: Introductionmentioning

confidence: 99%

A pan-ontology view of machine-derived knowledge representations and feedback mechanisms for curation

Konopka

Smedley

2021

Preprint

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Biomedical ontologies are established tools that organize knowledge in specialized research areas. They can also be used to train machine-learning models. However, it is unclear to what extent representations of ontology concepts learned by machine-learning models capture the relationships intended by ontology curators. It is also unclear whether the representations can provide insights to improve the curation process. Here, we investigate ontologies from across the spectrum of biological research and assess the concordance of formal ontology hierarchies with representations based on plain-text definitions. By comparing the internal properties of each ontology, we describe general patterns across the pan-ontology landscape and pinpoint areas with discrepancies in individual domains. We suggest specific mechanisms through which machine-learning approaches can lead to clarifications of ontology definitions. Synchronizing patterns in machine-derived representations with those intended by the ontology curators will likely streamline the use of ontologies in downstream applications.

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Where to search top-K biomedical ontologies?

Abstract: The source code (of seven ranking algorithms), ground truths and experimental results are available at https://github.com/danielapoliveira/bioont-search-benchmark.

Cited by 6 publications

References 52 publications

Processing genome-wide association studies within a repository of heterogeneous genomic datasets

Processing genome-wide association studies within a repository of heterogeneous genomic datasets

META-BASE: A Novel Architecture for Large-Scale Genomic Metadata Integration

A pan-ontology view of machine-derived knowledge representations and feedback mechanisms for curation

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