EDAM: an ontology of bioinformatics operations, types of data and identifiers, topics and formats

Ison, Jon C.; Kalaš, Matúš; Jonassen, Inge; Bolser, Dan; Uludağ, Mahmut; McWilliam, Hamish; Malone, James; López, Rodrigo; Pettifer, Steve; Rice, Peter

doi:10.1093/bioinformatics/btt113

Cited by 250 publications

(196 citation statements)

References 52 publications

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“…We then demonstrate a FAIR Projector, and show how its metadata integrates into the FAIR Accessor. In this example, the Projector modifies the ontological framework of the UniProt data such that the ontological terms used by UniProt are replaced by the terms specified in EDAM-an ontology of bioinformatics operations, datatypes, and formats (Ison et al, 2013). We will demonstrate that this transformation is specified, in a machine-readable way, by the FAIR Triple Descriptor that accompanies each Projector's metadata.…”

Section: Resultsmentioning

confidence: 99%

Interoperability and FAIRness through a novel combination of Web technologies

Wilkinson

Verborgh

Santos³

et al. 2017

PeerJ Computer Science

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Data in the life sciences are extremely diverse and are stored in a broad spectrum of repositories ranging from those designed for particular data types (such as KEGG for pathway data or UniProt for protein data) to those that are general-purpose (such as FigShare, Zenodo, Dataverse or EUDAT). These data have widely different levels of sensitivity and security considerations. For example, clinical observations about genetic mutations in patients are highly sensitive, while observations of species diversity are generally not. The lack of uniformity in data models from one repository to another, and in the richness and availability of metadata descriptions, makes integration and analysis of these data a manual, time-consuming task with no scalability. Here we explore a set of resource-oriented Web design patterns for data discovery, accessibility, transformation, and integration that can be implemented by any general-or specialpurpose repository as a means to assist users in finding and reusing their data holdings. We show that by using off-the-shelf technologies, interoperability can be achieved atthe level of an individual spreadsheet cell. We note that the behaviours of this architecture compare favourably to the desiderata defined by the FAIR Data Principles, and can therefore represent an exemplar implementation of those principles. The proposed interoperability design patterns may be used to improve discovery and integration of both new and legacy data, maximizing the utility of all scholarly outputs.

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Section: Resultsmentioning

confidence: 99%

Interoperability and FAIRness through a novel combination of Web technologies

Wilkinson

Verborgh

Santos³

et al. 2017

PeerJ Computer Science

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show abstract

“…While the OMICtools registry leverages a tailor-made taxonomy that tags each tool and enables researchers finding the most pertinent tool for their data analysis task, the Bio.Tools registry employs the EDAM ontology [16]. Terms from EDAM (a collaboratively and openly maintained ontology) can describe a tool in terms of its topic, operation, data, and format, thus providing a multifaceted characterisation: tools can be grouped by functionality, and compatible data formats.…”

Section: How Distributions Meetmentioning

confidence: 99%

Robust Cross-Platform Workflows: How Technical and Scientific Communities Collaborate to Develop, Test and Share Best Practices for Data Analysis

et al. 2017

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Information integration and workflow technologies for data analysis have always been major fields of investigation in bioinformatics. A range of popular workflow suites are available to support analyses in computational biology. Commercial providers tend to offer prepared applications remote to their clients. However, for most academic environments with local expertise, novel data collection techniques or novel data analysis, it is essential to have all the flexibility of open-source tools and open-source workflow descriptions. Workflows in datadriven science such as computational biology have considerably gained in complexity. New tools or new releases with additional features arrive at an enormous pace, and new reference data or concepts for quality control are emerging. A well-abstracted workflow and the exchange of the same across work groups have an enormous impact on the efficiency of research and the further development of the field. High-throughput sequencing adds to the avalanche of data available in the field; efficient computation and, in particular, parallel execution motivate the transition from traditional scripts and Makefiles to workflows. We here review the extant software development and distribution model with a focus on the role of integration testing and discuss the effect of common workflow language on distributions of open-source scientific software to swiftly and reliably provide the tools demanded for the execution of such formally described workflows. It is contended that, alleviated from technical differences for the execution on local machines, clusters or the cloud, communities also gain the technical means to test workflow-driven interaction across several software packages.

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“…Several projects and workshops have already begun progress to bridge the gap: the BioMoby project defined ontologies for data types and methods used in its services, and it provides a centralized repository for its service discovery [78]; Open Bio* libraries have been developed for the major computing languages i.e. Perl, Python, Ruby, and Java) to maximize bioinformatics web services and to create collaborative compatible data models for common biological objects [79]; the EDAM ontology of bioinformatics operations, types of data and identifiers, topics and formats used by CWL and workflow ROs [80] the DBCLS BioHackathon improves web service interoperability and collaboration between major database centers [77]; and the HTS-CSRS Workshop hosted by GWU and the FDA is a cross-disciplinary endeavor that emphasizes standardization of data storage and collection, communication of this genetic data, and the necessity of reproducibility of these analyses to ensure their potential clinical applications [32]. These are just a few examples of the efforts that combine technologies, ontologies, and standards to enhance computational analysis information.…”

Section: Journal/peer Review Perspectivementioning

confidence: 99%

Enabling Precision Medicine via standard communication of HTS provenance, analysis, and results

Alterovitz

Dean

Goble

et al. 2017

Preprint

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Abstract. Precision medicine can be empowered by a personalized approach to patient care based on the patient's unique genomic sequence. T o be used in precision medicine, genomic findings must be robust, reproducible, and experimental data capture should adhere to FAIR Data Guiding Principles. Moreover, precision medicine requires standardization that extends beyond wet lab procedures to computational methods.Rapidly developing standardization technologies improves communication of genomic sequencing by introducing concepts such as error domain, usability domain, validation kit, and provenance information. T hese advancements allow data provenance to be standardized and ensure interoperability. T hus, a resulting bioinformatics computation instance that includes these advancements can be easily communicated, repeated and compared by scientists, regulators, clinicians and others, allowing a greater range of practical applications.Advancing clinical trials, precision medicine, and regulatory submissions requires an umbrella of standards that not only fuses these elements, but also ensures efficient communication and documentation of genomic analyses. T hrough standardized bundling of HT S studies under an umbrella, regulatory agencies (FDA), academic researchers, and clinicians can expand collaboration to drive innovation in precision medicine with the potential for decreasing the time and cost associated with NGS workflow exchange, including FDA regulatory review submissions.

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EDAM: an ontology of bioinformatics operations, types of data and identifiers, topics and formats

Cited by 250 publications

References 52 publications

Interoperability and FAIRness through a novel combination of Web technologies

Interoperability and FAIRness through a novel combination of Web technologies

Robust Cross-Platform Workflows: How Technical and Scientific Communities Collaborate to Develop, Test and Share Best Practices for Data Analysis

Enabling Precision Medicine via standard communication of HTS provenance, analysis, and results

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