Open Access Methodology article

SegMine workflows for semantic microarray data analysis in Orange4WS

Vid Podpečan1*, Nada Lavrač12, Igor Mozetič1, Petra Kralj Novak1, Igor Trajkovski3, Laura Langohr4, Kimmo Kulovesi4, Hannu Toivonen4, Marko Petek5, Helena Motaln5 and Kristina Gruden5

Author Affiliations

1 Jožef Stefan Institute, Jamova 39, 1000 Ljubljana, Slovenia

2 University of Nova Gorica, Vipavska 13, 5000 Nova Gorica, Slovenia

3 Ss. Cyril and Methodius University, 1000 Skopje, Macedonia

4 University of Helsinki, P.O. Box 68, FI-00014 Helsinki, Finland

5 National Institute of Biology, Večna pot 111, 1000 Ljubljana, Slovenia

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BMC Bioinformatics 2011, 12:416  doi:10.1186/1471-2105-12-416

Published: 26 October 2011

Abstract

Background

In experimental data analysis, bioinformatics researchers increasingly rely on tools that enable the composition and reuse of scientific workflows. The utility of current bioinformatics workflow environments can be significantly increased by offering advanced data mining services as workflow components. Such services can support, for instance, knowledge discovery from diverse distributed data and knowledge sources (such as GO, KEGG, PubMed, and experimental databases). Specifically, cutting-edge data analysis approaches, such as semantic data mining, link discovery, and visualization, have not yet been made available to researchers investigating complex biological datasets.

Results

We present a new methodology, SegMine, for semantic analysis of microarray data by exploiting general biological knowledge, and a new workflow environment, Orange4WS, with integrated support for web services in which the SegMine methodology is implemented. The SegMine methodology consists of two main steps. First, the semantic subgroup discovery algorithm is used to construct elaborate rules that identify enriched gene sets. Then, a link discovery service is used for the creation and visualization of new biological hypotheses. The utility of SegMine, implemented as a set of workflows in Orange4WS, is demonstrated in two microarray data analysis applications. In the analysis of senescence in human stem cells, the use of SegMine resulted in three novel research hypotheses that could improve understanding of the underlying mechanisms of senescence and identification of candidate marker genes.

Conclusions

Compared to the available data analysis systems, SegMine offers improved hypothesis generation and data interpretation for bioinformatics in an easy-to-use integrated workflow environment.