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Motivation: Cancer is a heterogeneous progressive disease caused by perturbations of the underlying gene regulatory network that can be described by dynamic models. These dynamics are commonly modeled as Boolean networks or as ordinary differential equations. Their inference from data is computationally challenging, and at least partial knowledge of the regulatory network and its kinetic parameters is usually required to construct predictive models. Results: Here, we construct Hopfield networks from static gene-expression data and demonstrate that cancer subtypes can be characterized by different attractors of the Hopfield network. We evaluate the clustering performance of the network and find that it is comparable with traditional methods but offers additional advantages including a dynamic model of the energy landscape and a unification of clustering, feature selection and network inference. We visualize the Hopfield attractor landscape and propose a pruning method to generate sparse networks for feature selection and improved understanding of feature relationships. Availability: Software and datasets are available at http://acb.qfab.org/acb/hclust/ Contact: m.ragan@uq.edu.au Supplementary information: Supplementary data are available at Bioinformatics online.

Motivation: Multiple sequence alignment at the level of whole proteomes requires a high degree of automation, precluding the use of traditional validation methods such as manual curation. Since evolutionary models are too general to describe the history of each residue in a protein family, there is no single algorithm/model combination that can yield a biologically or evolutionarily optimal alignment. We propose a 'shotgun' strategy where many different algorithms are used to align the same family, and the best of these alignments is then chosen with a reliable objective function. We present WOOF, a novel 'word-oriented' objective function that relies on the identification and scoring of conserved amino acid patterns (words) between pairs of sequences. Results: Tests on a subset of reference protein alignments from BAliBASE showed that WOOF tended to rank the (manually curated) reference alignment highest among 1060 alternative (automatically generated) alignments for a majority of protein families. Among the automated alignments, there was a strong positive relationship between the WOOF score and similarity to the reference alignment. The speed of WOOF and its independence from explicit considerations of three-dimensional structure make it an excellent tool for analyzing large numbers of protein families. Availability: On request from the authors. Contact: m.ragan@imb.uq.edu.au

Summary: New high-resolution approaches for mapping ultrastructure of cells in 3D are leading to unprecedented quantities of spatial data. Here we present Illoura, a software tool for the integrated management, analysis and visualization of these data within a semantic context, and illustrate its capability by analysis of spatial relationships in mammalian beta cells. Availability: http://www.visiblecell.com/illoura Contact: m.ragan@uq.edu.au Supplementary information: Supplementary data are available at Bioinformatics online.