Enforcing Conceptual Modeling to improve the understanding of human genome

Pastor, Óscar; Levin, Ana M.; Casamayor, Juan Carlos; Celma, Matilde; Eraso, Luis E.; Villanueva, María José; Pérez-Alonso, Manuel

doi:10.1109/rcis.2010.5507367

Cited by 21 publications

(11 citation statements)

References 10 publications

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“…Our research group has been able to generate a Conceptual Schema of the Human Genome (CSHG) [24] which has evolved and grown over the years and has advanced the understanding of the human genome.…”

Section: Figure 3: Current Status Of the "Variation View"mentioning

confidence: 99%

“…(5) Bibliography and data bank view: gives the source of the data and contains a series of bibliographic reference documents for anyone wishing to obtain further information [22], [24].…”

Section: Figure 3: Current Status Of the "Variation View"mentioning

confidence: 99%

“…Previous studies [1][2][3] have shown how conceptual models allow us to provide a definition of a domain, so that we can understand the entities involved and their relationship. These studies focused on describing the genome of eukaryotic cells and the interaction between proteins, transcriptome, and other genetic components.…”

Section: Introductionmentioning

confidence: 99%

“…Pastor et. al. describes the Conceptual Schema of the Human Genome (CSHG) [4], [24]. However, this conceptual schema requires to be constantly aligned with the new genomic knowledge and in this paper we extend the aforementioned model to include the specification of haplotypes (which are defined in Section 2).…”

Section: Introductionmentioning

confidence: 99%

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How to deal with Haplotype data: An Extension to the Conceptual Schema of the Human Genome

Román

Pastor

Martínez

et al. 2016

CLEIej

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The goal of this work is to describe the advantages of the application of Conceptual Modeling (CM) in complex domains, such as genomics. Nowadays, the study and comprehension of the human genome is a major challenge due to its high level of complexity. The constant evolution in the genomic domain contributes to the generation of ever larger amounts of new data, which means that if we do not manage it correctly data quality could be compromised (i.e., problems related with heterogeneity and inconsistent data). In this paper, we propose the use of a Conceptual Schema of the Human Genome (CSHG), designed to understand and improve our ontological commitment to the domain and also extend (enrich) this schema with the integration of a novel concept: Haplotypes. Our focus is on improving the understanding of the relationship between genotype and phenotype, since new findings show that this question is more complex than was originally thought. Here we present the first steps in our data management approach with haplotypes (variations, frequencies and populations) and discuss the database evolution to support this data. Each new version in our conceptual schema (CS) introduces changes to the underlying database structure that has essential and practical implications for better understanding and managing the relevant information. A solution based on conceptual models gives a clear definition of the domain with direct implications in the medical field (Precision Medicine), in which Genomic Information Systems (GeIS) play a very important role.

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Section: Figure 3: Current Status Of the "Variation View"mentioning

confidence: 99%

“…(5) Bibliography and data bank view: gives the source of the data and contains a series of bibliographic reference documents for anyone wishing to obtain further information [22], [24].…”

Section: Figure 3: Current Status Of the "Variation View"mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

How to deal with Haplotype data: An Extension to the Conceptual Schema of the Human Genome

Román

Pastor

Martínez

et al. 2016

CLEIej

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“…The rst mechanism obtains some data from the ENSEMBL database [15]. The second mechanism retrieves genetic information from the HGBD database [16] based on the Conceptual Schema of the Human Genome (CSHG) [10].…”

Section: An Integrative Framework For Bioinformaticsmentioning

confidence: 99%

Diagen: A Model-Driven Framework for Integrating Bioinformatic Tools

Villanueva

Valverde

Levin

et al. 2012

Lecture Notes in Business Information Processing

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Abstract. Nowadays, the diagnosis of disease based on genomic information is feasible by searching genetic variations on DNA sequences. However, geneticists struggle with bioinformatic tools that are supposed to simplify DNA sequence analysis. As a universal tool to support every requirement is far from be implemented, geneticists themselves must solve the data exchange among several tools. Due to the fact that there are no standards to support this integration task, it must be managed in every analysis. This paper proposes addressing this integration by means of a model-driven framework. The Diagen framework is a software implementation based on conceptual modeling principles that formalizes data exchange and simplies bioinformatic tool integration. First, we analyze how conceptual modeling can be used to deal with data exchange among tools. And then, as a proof of concept, the presented framework is used to search for variations on the BRCA2 gene using real DNA samples and a set of specic bioinformatic tools.

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Ontology‐Driven Formal Conceptual Data Modeling for Biological Data Analysis

Keet

2013

Biological Knowledge Discovery Handbook

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Biological data modeling serves many purposes, and many approaches exist that are used in this endeavor. The main topics of advanced conceptual data modeling for database and Object-Oriented software development to support biological data analysis are included in Figure 1.1, which extend the traditional 'waterfall' software development methodology as depicted in bold in Figure 1.2. The scope of this chapter is to provide an overview of the ontological and logical aspects of conceptual data modeling tailored to molecular biology and biological knowledge discovery.Many databases and software applications have been and are being developed in bioinformatics, which, following good computing methodologies, are-or should have been-developed in stages, going from requirements analysis ('what should the envisioned software do?') and conceptual analysis ('what data should it be able to manage?') to design-level code and then to the actual implementation. It is well-known that omitting the conceptual analysis stage by going straight to coding or scripting just adds to the pile of one-off (bioinformatics) tools that have more bugs and are much less, or not at all, maintainable and interoperable. Conversely, availing of a proper software development methodology with a representitle, edition.

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Enforcing Conceptual Modeling to improve the understanding of human genome

Cited by 21 publications

References 10 publications

How to deal with Haplotype data: An Extension to the Conceptual Schema of the Human Genome

How to deal with Haplotype data: An Extension to the Conceptual Schema of the Human Genome

Diagen: A Model-Driven Framework for Integrating Bioinformatic Tools

Ontology‐Driven Formal Conceptual Data Modeling for Biological Data Analysis

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