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<p>Abstract</p> <p>Background</p> <p>Researchers in the field of bioinformatics often face a challenge of combining several ordered lists in a proper and efficient manner. Rank aggregation techniques offer a general and flexible framework that allows one to objectively perform the necessary aggregation. With the rapid growth of high-throughput genomic and proteomic studies, the potential utility of rank aggregation in the context of meta-analysis becomes even more apparent. One of the major strengths of rank-based aggregation is the ability to combine lists coming from different sources and platforms, for example different microarray chips, which may or may not be directly comparable otherwise.</p> <p>Results</p> <p>The <it>RankAggreg </it>package provides two methods for combining the ordered lists: the Cross-Entropy method and the Genetic Algorithm. Two examples of rank aggregation using the package are given in the manuscript: one in the context of clustering based on gene expression, and the other one in the context of meta-analysis of prostate cancer microarray experiments.</p> <p>Conclusion</p> <p>The two examples described in the manuscript clearly show the utility of the <it>RankAggreg </it>package in the current bioinformatics context where ordered lists are routinely produced as a result of modern high-throughput technologies.</p>