Advances in Data Analysis Methods and Tools

Dear Colleagues,

Ever since the first reports of successful application of DNA microarrays appeared in the second half of the 1990s, the technology has matured immensely and gained popularity amongst army of researchers. The density of features has increased dramatically, allowing for manufacturing of whole genome expression arrays or high density SNPs arrays, and was accompanied by massively improved reproducibility of results. This process has been paralleled by dramatic improvements in data analysis methods, which in case of expression arrays, led to confident identification of statistically significant gene/transcript expression changes in complex experiments as well as to demystifying underlying biological processes by linking the resulting gene lists to functional classes, gene networks and biological pathways.

Although recent next generation sequencing technology becomes increasingly popular for applications previously “reserved” for microarrays, e.g., transcriptome profiling, the problems associated with modelling data distribution, data normalisation and subsequent identification of differential expression, make sequencing data analysis challenging and to some extent ambiguous, particularly given small number of replicated samples. Consequently, microarray data analysis seems more robust and better supported, which is particularly important for complex multifactorial experiments.

In this issue we are inviting material about computational methods and tools related to various aspects of microarray data analysis, such as normalisation, statistical analysis, data analysis workflows or functional downstream analysis as applied to wide range of arrays.

Dr. Pawel Herzyk
Guest Editor

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