Approaches to the analysis of proteomics and transcriptomics data based on statistical methodology Statistics randomization bioinformatics oocyte proteome microarray mRNA protein reprogramming aging small molecule drug Randomisierung Bioinformatik Statistik Recent developments in genomics and molecular biology led to the generation of an enormous amount of complex data of different origin. This is demonstrated by a number of published results from microarray experiments in Gene Expression Omnibus. The number was growing in exponential pace over the last decade. The challenge of interpreting these vast amounts of data from different technologies led to the development of new methods in the fields of computational biology and bioinformatics. Researchers often want to represent biological phenomena in the most detailed and comprehensive way. However due to the technological limitations and other factors like limited resources this is not always possible. On one hand more detailed and comprehensive research generates data of high complexity that is very often difficult to approach analytically however giving bioinformatics a chance to draw more precise and deeper conclusions. On the other hand for low complexity tasks the data distribution is known and we can fit a mathematical model. Then to infer from this mathematical model researchers can use well known and standard methodologies. In return for using standard methodologies the biological questions we are answering might not be unveiling the whole complexity of the biological meaning. Nowadays it is a standard that a biological study involves generation of large amounts of data that needs to be analyzed with a statistical inference. Sometimes data challenge researchers ...