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GANN: Genetic algorithm neural networks for the detection of conserved combinations of features in DNA

Robert G Beiko12* and Robert L Charlebois3

Author Affiliations

1 Institute for Molecular Bioscience, The University of Queensland, Brisbane 4072, Australia

2 Department of Biology, University of Ottawa, Ottawa, ON, K1N 6N5, Canada

3 Genome Atlantic, Department of Biochemistry and Molecular Biology, Dalhousie University, Halifax, NS, B3H 1X5, Canada

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BMC Bioinformatics 2005, 6:36  doi:10.1186/1471-2105-6-36

Published: 22 February 2005



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.


GANN (available at webcite) 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.


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.