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24, chemin de Borde Rouge –Auzeville – CS52627
31326 Castanet Tolosan CEDEX - France

Dernière mise à jour : Mai 2018

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Data Integration

These activities are conducted jointly with several IRHS team. Their goal is to exploit datasets jointly whereas their were acquired as part of different projects or experiments.

In biology high-throughput techniques and the multiplication of databanks have caused the scale at which data are acquired and shared to increase exponentially in recent years. The field is entering the so called “Big data” era. However, the datasets accumulated by biologists during the course of their experiments are often heterogeneous. Moreover, even if they can be stored and retrieved together through databanks they are usually not bound together because there are discrepancies between the experimental settings. This poses problems to biologists to analyse jointly these disparate datasets in order to acquire new insights into their biological interpretations.

However existing software does not provide complete user-friendly solutions to gather these datasets together, render their size manageable for instance through data summaries and allow scientists to interpret them easily through visual displays. We are exploring new bioinformatic approaches to solve this issue in a generic way, that is to say not tailored for a specific organism or a limited range of experimental data.

We are developing a prototype software using apple and seed datasets. The tool manipulates a large matrix containing all datasets extracted from the research unit database and: (I) normalise datasets so that they can be treated jointly; (ii) group similar samples analysed in similar experimental settings. The grouping process will be performed based on previous knowledge stored in specifically designed ontologies; (iii) represent each group by a representative archetype individual; (iv) summarise data for the archetype individuals; (v) build a visual display of the data summaries which the biologist will be able to navigate to acquire an understanding of the underlying datasets.

See also

Here, other research topics of the team.