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Review
. 2014;8 Suppl 2(Suppl 2):I1.
doi: 10.1186/1752-0509-8-S2-I1. Epub 2014 Mar 13.

Data integration in the era of omics: current and future challenges

David Gomez-Cabrero, Imad Abugessaisa, Dieter Maier, Andrew Teschendorff, Matthias Merkenschlager, Andreas Gisel, Esteban Ballestar, Erik Bongcam-Rudloff, Ana Conesa, Jesper Tegnér
Review

Data integration in the era of omics: current and future challenges

David Gomez-Cabrero et al. BMC Syst Biol. 2014.

Abstract

To integrate heterogeneous and large omics data constitutes not only a conceptual challenge but a practical hurdle in the daily analysis of omics data. With the rise of novel omics technologies and through large-scale consortia projects, biological systems are being further investigated at an unprecedented scale generating heterogeneous and often large data sets. These data-sets encourage researchers to develop novel data integration methodologies. In this introduction we review the definition and characterize current efforts on data integration in the life sciences. We have used a web-survey to assess current research projects on data-integration to tap into the views, needs and challenges as currently perceived by parts of the research community.

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Figures

Figure 1
Figure 1
Glycolysis. Description of the ten reactions and their associated enzymes of the metabolic pathway (reproduced from http://en.wikipedia.org/wiki/File:Glycolysis2.svg).
Figure 2
Figure 2
Relevance of integration schemes. (a) Each matrix location (i,j) shows the percentage of survey participants that selected as relevant the integration of data type i and data type j in basic (upper matrix) and clinical (lower matrix) research. (b) shows the percentage of participants that selected as relevant the integration of the same data type for the data types included in the list.
See this image and copyright information in PMC

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