Abstract
Genomic information shows some characteristics that make them very difficult to interpret and to exploit. Such data constitute an important factual resource (GenBank, SwissProt, GeneOntology, or Decrypthon…), are heterogeneous, huge in quantity, and geographically distributed. This paper presents Genome3DExplorer, a new modeling and software solution to visualize textual and factual genomic data based on adapted federator description language. The exploration is based on a well-adapted graphical paradigm that automatically helps to build a graph-based representation, and allows biologist to highlight some global topological characteristics of data, which are uneasily visible using traditional exploration tools. Finally, we present results produced by Genome3DExplorer software on various sets of biological data.
Highlights
As in ADN-Viewer (Hérisson, Gros, Férey, Magneau & Gherbi, 2004) and SequenceWord (Rojdestvenski, Pettersson & Modjeska, 2000) our objective is to elaborate new solution in order to explore in virtual environment various kinds of genomic data
These data come from the many databases, such as GenBank (DDBJ/EMBL/Genbank n.d.), SwissProt (SIB/EBI n.d.), or Decrypthon (AFM, GENOMINING, IBM, 2000)
Our objective was to elaborate new solutions in order to explore both biological genomic data. This approach is mainly based on the definition of a genomic data representation language, answering the requirements and specificities of biological databases
Summary
As in ADN-Viewer (Hérisson, Gros, Férey, Magneau & Gherbi, 2004) and SequenceWord (Rojdestvenski, Pettersson & Modjeska, 2000) our objective is to elaborate new solution in order to explore in virtual environment various kinds of genomic data. These data come from the many databases, such as GenBank (DDBJ/EMBL/Genbank n.d.), SwissProt (SIB/EBI n.d.), or Decrypthon (AFM, GENOMINING, IBM, 2000). In order to visualize efficiently biological data, we need to define a common data description language that must accommodate and represent knowledge resulting from structured but heterogeneous databanks. We describe how we used the specific characteristics of the genomic data to find an adapted description format for this kind of data
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