MOTIVATION: Microbial genomes undergo evolutionary processes such as gene family expansion and contraction, variable rates and patterns of sequence substitution and lateral genetic transfer. Simulation tools are essential for both the generation of data under different evolutionary models and the validation of analytical methods on such data. However, meaningful investigation of phenomena such as lateral genetic transfer requires the simultaneous consideration of many underlying evolutionary processes. RESULTS: We have developed EvolSimulator, a software package that combines non-stationary sequence and gene family evolution together with models of lateral genetic transfer, within a customizable birth-death model of speciation and extinction. Here, we examine simulated data sets generated with EvolSimulator using existing statistical techniques from the evolutionary literature, showing in detail each component of the simulation strategy. AVAILABILITY: Source code, manual and other information are freely available at www.bioinformatics.org.au/evolsim. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: The multitude of motif detection algorithms developed to date have largely focused on the detection of patterns in primary sequence. Since sequence-dependent DNA structure and flexibility may also play a role in protein-DNA interactions, the simultaneous exploration of sequence- and structure-based hypotheses about the composition of binding sites and the ordering of features in a regulatory region should be considered as well. The consideration of structural features requires the development of new detection tools that can deal with data types other than primary sequence. RESULTS: GANN (available at http://bioinformatics.org.au/gann) is a machine learning tool for the detection of conserved features in DNA. The software suite contains programs to extract different regions of genomic DNA from flat files and convert these sequences to indices that reflect sequence and structural composition or the presence of specific protein binding sites. The machine learning component allows the classification of different types of sequences based on subsamples of these indices, and can identify the best combinations of indices and machine learning architecture for sequence discrimination. Another key feature of GANN is the replicated splitting of data into training and test sets, and the implementation of negative controls. In validation experiments, GANN successfully merged important sequence and structural features to yield good predictive models for synthetic and real regulatory regions. CONCLUSION: GANN is a flexible tool that can search through large sets of sequence and structural feature combinations to identify those that best characterize a set of sequences.