Developments in data visualisation pave the way in genetic research
With such vast amounts of data to consider, it can be difficult for scientists to understand the true biological meaning of their research, says Carl-Johan Ivarsson. However, new data visualisation techniques are now making it much easier to uncover new and unexpected results
« Face à de telles masses de données, il peut s’avérer difficile pour les scientifiques de comprendre la véritable signification biologique de leurs recherches », déclare Carl-Johan Ivarsson. Cependant, de nouvelles techniques de visualisation des données facilitent aujourd’hui considérablement le travail, révélant ainsi des résultats nouveaux et inattendus.
Mit einer großen Menge von Daten, die zu berücksichtigen sind, kann es für Wissenschaftler schwierig werden, die biologische Bedeutung ihrer Forschung zu verstehen, sagt Carl-Johan Ivarsson. Neue Datenvisualisierungstechniken vereinfachen die Aufdeckung neuer und unerwarteter Resultate.
A
s recently as 10 years ago, many biologists were still working with glass
slides that revealed a few thousand features of the genes that they were studying, but that number has grown dramatically in recent years, thanks to advances in technology. As such, it has become much more difficult for biologists to identify which genes are being expressed, and to what level.
With such a large volume of data to consider, it is often impossible for these scientists to derive any real biological meaning from their findings with the naked eye alone, which means that sophisticated data algorithms need to be developed in order to interpret this data effectively. As a result, much of the computer software that has been designed
Above Qlucore started as a research project at Lund University, Sweden. Here, Dr Ann-Sofie Albrekt, based at the university’s Department of Immunotechnology, uses tte software.
for use in this area has focussed on being able to handle increasingly vast amounts of data.
Unfortunately, this shift in focus has (unintentionally) pushed scientists and researchers to one side, since a lot of data analysis must now be performed by specialist bioinformaticians and biostatisticians, especially when complicated algorithms are required for the analysis. Tis model has several drawbacks, however, since it is typically the scientist who knows the most about the specific subject area being studied.
Te good news for scientists is that the latest data visualisation techniques and imaging technologies are already making it much easier
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