Background: Comparative analyses of chromosomal gene orders are successfully used to predict gene clusters in

bacterial and fungal genomes. Present models for detecting sets of co-localized genes in chromosomal sequences

require prior knowledge of gene family assignments of genes in the dataset of interest. These families are often

computationally predicted on the basis of sequence similarity or higher order features of gene products. Errors

introduced in this process amplify in subsequent gene order analyses and thus may deteriorate gene cluster

prediction.

Results: In this work, we present a new dynamic model and efficient computational approaches for gene cluster

prediction suitable in scenarios ranging from traditional gene family-based gene cluster prediction, via multiple

conflicting gene family annotations, to gene family-free analysis, in which gene clusters are predicted solely on the

basis of a pairwise similarity measure of the genes of different genomes. We evaluate our gene family-free model

against a gene family-based model on a dataset of 93 bacterial genomes.

Conclusions: Our model is able to detect gene clusters that would be also detected with well-established gene

family-based approaches. Moreover, we show that it is able to detect conserved regions which are missed by gene

family-based methods due to wrong or deficient gene family assignments.