Ana M. Levin

Enforcing Conceptual Modeling to improve the understanding of human genome

It is widely accepted that the use of Conceptual Modeling techniques in modern Software Engineering leads to a more accurate description of the problem domain. The application of these techniques in the context of challenging domains as the human genome is a fascinating task. The relevant biological concepts should be properly addressed through the creation of the corresponding conceptual schema. This schema will improve the description of the global process followed from a DNA sequence to a fully functional protein. Once the conceptual model is established, the corresponding database is created. The database is intended to act as a unified repository of integrated information that will allow biologists to perform efficient recovery tasks.

Model-based engineering applied to the interpretation of the human genome

In modern software engineering it is widely accepted that the use of Conceptual Modeling techniques provides an accurate description of the problem domain. Applying these techniques before developing their associated software representation (implementations) allows for the development of high quality software systems. The application of these ideas to new, challenging domains -as the one provided by the modern Genomics- is a fascinating task. In particular, this chapter shows how the complexity of human genome interpretation can be faced from a pure conceptual modeling perspective to describe and understand it more clearly and precisely. With that, we pretend to show that a conceptual schema of the human genome will allow us to better understand the functional and structural relations that exist between the genes and the DNA translation and transcription processes, intended to explain the protein synthesis. Genome, genes, alleles, genic mutations... all these concepts should be properly specified through the creation of the corresponding Conceptual Schema, and the result of these efforts is presented here. First, an initial conceptual schema is suggested. It includes a first version of the basic genomic notions intended to define those basic concepts that characterize the description of the Human Genome. A set of challenging concepts is detected: they refer to representations that require a more detailed specification. As the knowledge about the domain increases, the model evolution is properly introduced and justified, with the final intention of obtaining a stable, final version for the Conceptual Schema of the Human Genome. During this process, the more critical concepts are outlined, and the final decision adopted to model them adequately is discussed. Having such a Conceptual Schema enables the creation of a corresponding data base. This database could include the required contents needed to exploit bio-genomic information in the structured and precise way historically provided by the Database domains. That strategy is far from the current biological data source ontologies that are heterogeneous, imprecise and too often even inconsistent.

Conceptual modeling of human genome: integration challenges

While Information Systems (IS) principles have been successfully applied to the design, implementation and management of a diverse set of domains, the Bioinformatics domain in general and the Genomic one in particular, often lacks a rigorous IS background, based on elaborating a precise Conceptual Model where the relevant concepts of the domain were properly defined. On the contrary, current genomic data repositories focus on the solution space in the form of diverse, ad-hoc databases that use to be hard to manage, evolve and intercommunicate. Conceptual Modeling as a central strategy is then far from the current biological data source ontologies that are heterogeneous, imprecise and too often even inconsistent when compared among them. To solve this problem, a concrete Conceptual Schema for the Human Genome (CSHG) is introduced in its latest version on this chapter. With a holistic perspective, the CSHG focuses on the different genomic views that must be integrated and emphasizes the value of the approach in order to deal appropriately the challenge of correctly interpreting the human genome.

A Conceptual Modeling Approach To Improve Human Genome Understanding

Information systems cannot be designed nor programmed without prior elicitation of the knowledge they need to know. Representing this knowledge in an explicit form is the main application of a conceptualmodel. By allowing for a minor paradigm shift, one can imagine the human body as an information system; highly complex and built of biological molecules, rather than man-made hardware, but an information system nonetheless. It is this paradigm shift that allows for exciting possibilities. Just as acquiring the source-code of a man-made system allows for post-production modifications and easy software maintenance, the same could very well apply to the human body: essentially, the act of debugging life itself. Acquiring the source-code to the human information system begins with the first step in any information system development: the creation of a comprehensive, correct conceptual model of the human genome.

Facing the Challenges of Genome Information Systems: A Variation Analysis Prototype

In Bioinformatics there is a lack of software tools that fit with the requirements demanded by biologists. For instance, when a DNA sample is sequenced, a lot of work have to be performed manually and several tools are used. The application of Information Systems (IS) principles into the development of bioinformatics tools opens a new interesting research path. One of the most promising approaches is the use of conceptual models in order to precisely define how genomic data is represented into an IS. This work introduces how to build a Genome Information System (GIS) using these principles. As a first step to achieve this goal, a conceptual model to formally describe genomic mutations is presented. In addition, as a proof of concept of this approach, a variation analysis prototype has been implemented using this conceptual model as a development core.

Mutational Data Loading Routines for Human Genome Databases : the BRCA1 Case

The last decades a large amount of research has been done in the genomics domain which has and is generating terabytes, if not exabytes, of information stored globally in a very fragmented way. Different databases use different ways of storing the same data, resulting in undesired redundancy and restrained information transfer. Adding to this, keeping the existing databases consistent and data integrity maintained is mainly left to human intervention which in turn is very costly, both in time and money as well as error prone. Identifying a fixed conceptual dictionary in the form of a conceptual model thus seems crucial. This paper presents an effort to integrate the mutational data from the established genomic data source HGMD into a conceptual model driven database HGDB, thereby providing useful lessons to improve the already existing conceptual model of the human genome.

Gene ontology based automated annotation: why it isn't working

Genomics has seen a great deal of development since the milestone of the sequencing of the human genome by Craig Venter and Francis Collins in 2000. However, it is broadly accepted now that real challenges are lying ahead in actually understanding the meaning of these raw data. Traditionally this process of assigning meaning to biological crude data is being performed by domain specialists and has been known as annotation. As data chaos becomes larger due to rapid advances in sequencing technologies, the interest for automated annotation has equally increased. Current approaches are often based on the Gene Ontology (GO), but often fail to meet the requirements. Determining why and how they fail will prove crucial in finding methods that perform better, and ultimately might very well deliver the promising feat of turning the Human Genome data chaos into actual knowledge.

Using Papers Citations for Selecting the Best Genomic Databases

Selecting the right data is an essential activity in Genomic-related Information Systems. This work aims to analyze if it is possible to select the best genomic databases from a catalog using information about papers citations related to these genomic databases. The motivation for using information about citations has to do with the fact that it is not easy to obtain proper metadata with respect to these databases. Thus, in this work, information related to papers citations is used for measuring three distinct data quality dimensions: believability, timeliness, and relevancy. Believability is evaluated through the inspection of the number of citations. The variation of the number of citations over time is useful for determining the recency of a database and it is related to the timeliness dimension. Regarding to relevancy, the keywords of papers are useful to indicate the main context of application of these databases.

Diagen: A Model-Driven Framework for Integrating Bioinformatic Tools

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 addresses 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 simplifies bioinformatic tool integration. First, we analyze how conceptual modeling can be used to deal with data exchange among tools. Then, the presented framework is used to search for variations on the BRCA2 gene using real DNA samples and a set of specific bioinformatic tools.

Diagen: A model-based bioinformatic tool for genetic analysis

Geneticists that use software tools to carry out their diagnosis claim that current solutions do not fulfill completely their requirements. From an Information System perspective, this issue is a consequence of the lack of formal data descriptions, which has led to genetic repositories full of heterogeneous data and inconsistencies. Simultaneously, the same lack of formalization is perceived in software tools for genetic data analysis. As a solution, we provide a unified view that formalizes genetic concepts through the definition of a Conceptual Schema of the Human Genome (CSHG). In order to demonstrate the benefits of this approach, a Web application for genetic analysis, named Diagen, has been developed applying the aforementioned CSHG: Diagen is a model-based tool since each of its software components is a projection of the CSHG proposed.