Andreas Magusin

Senior Scientist

Cell & Developmental Biology

Contact details

andreas.magusin@jic.ac.uk

Research interests

The role of High-throughput Informatics is research support in the form of analyses and designs of high-throughput experiments. These techniques are employed in metabolomics, proteomics and transcriptomics. An important part of the analytical aspect is the development of a unified framework for combining the multiple sources of biological information and testing association between them.

High-throughput experiments performed at the John Innes Centre are used to address questions of increasing scientific complexity. As a result, the demand for statistical assessment of the conclusions from the experimental data increases. The benefits of employing optimal experimental designs together with high-throughput technologies are two-fold. Firstly, it ensures that the data obtained are amenable to statistical analysis. Secondly, high-throughput experimentation can be resource intensive, so optimised designs economise on materials without compromising the accuracy of the experiment.

The aim of the Group is to accelerate and further JIC science by enabling scientific investigations of greater complexity. The Group will develop algorithms and statistical methods to originate hypotheses explaining observations in high-throughput experimental data. Such hypotheses are subsequently tested via experiments which are optimally designed with respect to information content and economic restraints. Apart from contributions to the field of Computational Systems Biology; the activities of the Group is expected to contribute significantly to JIC research programmes and scientific clusters.

Recent Publications

Sonmez C., Bäurle I., Magusin A., Dreos R., Laubinger S., Weigel D., Dean C. (2011)
RNA 3' processing functions of Arabidopsis FCA and FPA limit intergenic transcription.
Proceedings of the National Academy of Sciences USA 108 (20) 8508-13
DOI:10.1073/pnas.1105334108

Stokes D., Fraser F., Morgan C., O'Neill C. M., Dreos R., Magusin A., Szalma S., Bancroft I. (2010)
An association transcriptomics approach to the prediction of hybrid performance
Molecular Breeding 26 (1) 91-106
DOI:10.1007/s11032-009-9379-3

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