CoV2K model, a comprehensive representation of SARS-CoV-2 knowledge and data interplay

Alfonsi, Tommaso; Khalaf, Ruba Al; Ceri, Stefano; Bernasconi, Anna

doi:10.1038/s41597-022-01348-9

Cited by 13 publications

(10 citation statements)

References 29 publications

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“…The VCM focuses on such structuring function for the purposes of characterizing viral genomic sequences. The model has already being successfully used for designing several search and visualization systems [14][15][16] and has been linked to a Phenotype Data Dictionary [17] and a knowledge base of SARS-CoV-2 mutations' impacts [18,19]. A broader application and adoption of the VCM within the context of virology research (by both life science domain experts and information systems' developers), however, requires that a more ontology-oriented approach is embraced, allowing the unambiguous identification of entities in the context of heterogeneous information.…”

Section: Introductionmentioning

confidence: 99%

Semantic interoperability: ontological unpacking of a viral conceptual model

2022

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Background Genomics and virology are unquestionably important, but complex, domains being investigated by a large number of scientists. The need to facilitate and support work within these domains requires sharing of databases, although it is often difficult to do so because of the different ways in which data is represented across the databases. To foster semantic interoperability, models are needed that provide a deep understanding and interpretation of the concepts in a domain, so that the data can be consistently interpreted among researchers. Results In this research, we propose the use of conceptual models to support semantic interoperability among databases and assess their ontological clarity to support their effective use. This modeling effort is illustrated by its application to the Viral Conceptual Model (VCM) that captures and represents the sequencing of viruses, inspired by the need to understand the genomic aspects of the virus responsible for COVID-19. For achieving semantic clarity on the VCM, we leverage the “ontological unpacking” method, a process of ontological analysis that reveals the ontological foundation of the information that is represented in a conceptual model. This is accomplished by applying the stereotypes of the OntoUML ontology-driven conceptual modeling language.As a result, we propose a new OntoVCM, an ontologically grounded model, based on the initial VCM, but with guaranteed interoperability among the data sources that employ it. Conclusions We propose and illustrate how the unpacking of the Viral Conceptual Model resolves several issues related to semantic interoperability, the importance of which is recognized by the “I” in FAIR principles. The research addresses conceptual uncertainty within the domain of SARS-CoV-2 data and knowledge.The method employed provides the basis for further analyses of complex models currently used in life science applications, but lacking ontological grounding, subsequently hindering the interoperability needed for scientists to progress their research.

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Section: Introductionmentioning

confidence: 99%

Semantic interoperability: ontological unpacking of a viral conceptual model

2022

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“…CoVEffect brings a number of tangible results to the scientific community, which we here describe. Immediate integrated use of our resulting annotated database was made within our CoV2K [ 22 ] system by updating the AA_changes, Variant, and Effect entities. Other data-driven analysis resources developed by our group (such as VirusViz [ 78 ] and ViruClust [ 79 ]) could immediately benefit from the addition of structured tuples connecting mutations and effects.…”

Section: Discussionmentioning

confidence: 99%

“…Effects are chosen from a taxonomy, that is, a controlled vocabulary of terms, including, for example, transmissibility, disease severity, resistance to antiviral drugs, or change in the protein kinetics (flexibility or stability properties). We previously proposed an initial version of this vocabulary [ 22 , 62 ], which has now evolved into a complete list of effects organized by category (“epidemiology,” “immunology,” “viral kinetics and dynamics,” or “diagnosis, prevention, and treatments”). The full list can be found the AdditionalFile1-effects-taxonomy [ 63 ].…”

Section: Methodsmentioning

confidence: 99%

“…Online resources such as CoVariants [ 18 ], European Centre for Disease Prevention and Control [ 19 ], WHO [ 20 ], and Centers for Disease Control and Prevention (CDC) [ 21 ] explain variants’ effects, commenting on how they are reported in the literature. We previously made extensive curation of effects stored in CoV2K [ 22 ], a knowledge base of data and knowledge about SARS-CoV-2; our cumbersome manual curation approach had quickly become unfeasible, prompting us to explore alternative solutions.…”

Section: Related Workmentioning

confidence: 99%

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CoVEffect: interactive system for mining the effects of SARS-CoV-2 mutations and variants based on deep learning

Serna García,

Al Khalaf,

Invernici

et al. 2022

GigaScience

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Background Literature about SARS-CoV-2 widely discusses the effects of variations that have spread in the past 3 years. Such information is dispersed in the texts of several research articles, hindering the possibility of practically integrating it with related datasets (e.g., millions of SARS-CoV-2 sequences available to the community). We aim to fill this gap, by mining literature abstracts to extract—for each variant/mutation—its related effects (in epidemiological, immunological, clinical, or viral kinetics terms) with labeled higher/lower levels in relation to the nonmutated virus. Results The proposed framework comprises (i) the provisioning of abstracts from a COVID-19–related big data corpus (CORD-19) and (ii) the identification of mutation/variant effects in abstracts using a GPT2-based prediction model. The above techniques enable the prediction of mutations/variants with their effects and levels in 2 distinct scenarios: (i) the batch annotation of the most relevant CORD-19 abstracts and (ii) the on-demand annotation of any user-selected CORD-19 abstract through the CoVEffect web application (http://gmql.eu/coveffect), which assists expert users with semiautomated data labeling. On the interface, users can inspect the predictions and correct them; user inputs can then extend the training dataset used by the prediction model. Our prototype model was trained through a carefully designed process, using a minimal and highly diversified pool of samples. Conclusions The CoVEffect interface serves for the assisted annotation of abstracts, allowing the download of curated datasets for further use in data integration or analysis pipelines. The overall framework can be adapted to resolve similar unstructured-to-structured text translation tasks, which are typical of biomedical domains.

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“…For example, the B.1.1 lineage [26] [27] arose in early 2020 which contained a double mutation in Ncap, Arg203Lys and Gly204Arg, [28] which spread throughout Europe and beyond. Further examples include Alpha (B.1.1.7) [29], Delta (B.1.617) [30] and Omicron (B.1.1.529) [31] amongst others [32] [33] [27]. Considerable ongoing sequence surveillance seems certain to identify further new variants [34] [35] [36] [37] [38].…”

Section: Introductionmentioning

confidence: 99%

Efficient overexpression and purification of SARS-CoV-2 Nucleocapsid proteins inEscherichia coli

Brudenell,

Pohare,

Zafred

et al. 2024

Preprint

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The fundamental biology of Severe Acute Respiratory Syndrome coronavirus 2 (SARS-CoV-2) nucleocapsid protein (Ncap), its use in diagnostic assays and its potential application as a vaccine component have received considerable attention since the outbreak of the Covid19 pandemic in late 2019. Here we report the scalable expression and purification of soluble, immunologically active, SARS-CoV-2 Ncap inEscherichia coli. Codon-optimised synthetic genes encoding the original Ncap sequence and four common variants with an N-terminal 6His affinity tag (sequence MHHHHHHG) were cloned into an inducible expression vector carrying a regulated bacteriophage T5 synthetic promoter controlled bylacoperator binding sites. The constructs were used to express Ncap proteins and protocols developed which allow efficient production of purified Ncap with yields of over 200 mg per litre of culture media. These proteins were deployed in ELISA assays to allow comparison of their responses to human sera. Our results suggest that there was no detectable difference between the 6His-tagged and untagged original Ncap proteins but there may be a slight loss of sensitivity of sera to other Ncap isolates.

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CoV2K model, a comprehensive representation of SARS-CoV-2 knowledge and data interplay

Cited by 13 publications

References 29 publications

Semantic interoperability: ontological unpacking of a viral conceptual model

Semantic interoperability: ontological unpacking of a viral conceptual model

CoVEffect: interactive system for mining the effects of SARS-CoV-2 mutations and variants based on deep learning

Efficient overexpression and purification of SARS-CoV-2 Nucleocapsid proteins inEscherichia coli

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