Methodology article
Context-driven discovery of gene cassettes in mobile integrons using a computational grammar
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* Corresponding author: Guy Tsafnat guyt@unsw.edu.au
1 Centre for Health Informatics, Univ. of New South Wales, Sydney, NSW 2052, Australia
2 Centre for Infectious Diseases and Microbiology, Univ. of Sydney, Westmead Hospital, Sydney, NSW 2145, Australia
BMC Bioinformatics 2009, 10:281 doi:10.1186/1471-2105-10-281
Published: 8 September 2009Abstract
Background
Gene discovery algorithms typically examine sequence data for low level patterns. A novel method to computationally discover higher order DNA structures is presented, using a context sensitive grammar. The algorithm was applied to the discovery of gene cassettes associated with integrons. The discovery and annotation of antibiotic resistance genes in such cassettes is essential for effective monitoring of antibiotic resistance patterns and formulation of public health antibiotic prescription policies.
Results
We discovered two new putative gene cassettes using the method, from 276 integron features and 978 GenBank sequences. The system achieved κ = 0.972 annotation agreement with an expert gold standard of 300 sequences. In rediscovery experiments, we deleted 789,196 cassette instances over 2030 experiments and correctly relabelled 85.6% (α ≥ 95%, E ≤ 1%, mean sensitivity = 0.86, specificity = 1, F-score = 0.93), with no false positives.
Error analysis demonstrated that for 72,338 missed deletions, two adjacent deleted cassettes were labeled as a single cassette, increasing performance to 94.8% (mean sensitivity = 0.92, specificity = 1, F-score = 0.96).
Conclusion
Using grammars we were able to represent heuristic background knowledge about large and complex structures in DNA. Importantly, we were also able to use the context embedded in the model to discover new putative antibiotic resistance gene cassettes. The method is complementary to existing automatic annotation systems which operate at the sequence level.