Context-driven discovery of gene cassettes in mobile integrons using a computational grammar.
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 kappa = 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% (alpha > or = 95%, E < or = 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).


CONCLUSIONS
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.
Tsafnat
Guy
G
Centre for Health Informatics, Univ. of New South Wales, Sydney, NSW 2052, Australia. guyt@unsw.edu.au
Coiera
Enrico
E
Partridge
Sally R
SR
Schaeffer
Jaron
J
Iredell
Jon R
JR
D019295:Computational Biology / Q000379:methods*; D004247:DNA / Q000737:chemistry*; D004352:Drug Resistance, Microbial; D005796:Genes; D032023:Integrons
D004247:DNA