Computational prediction and characterization of genomic islands: insights into bacterial pathogenicity
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Computational prediction and characterization of genomic islands: insights into bacterial pathogenicity
- 1. Computational prediction and characterization of genomic islands: insights into bacterial pathogenicity Morgan G.I. Langille Department of Molecular Biology & Biochemistry Simon Fraser University http://tinyurl.com/genomic-islands
- 13. IslandPick: Outline Query Genome A Genome B Genome C Genome D Run Mauve Mauve (A & B) Extract unique regions Mauve (A & C) Mauve (A & D) Genome D Putative Genomic Islands BLAST Identify overlapping unique regions
- 14. Selecting Comparative Genomes Run Mauve Mauve (A & B) Extract unique regions Mauve (A & C) Mauve (A & D) Genome D Putative Genomic Islands BLAST Identify overlapping unique regions Genome B Genome C Genome D Comparative Genome Selection (using CVTree distances) Query Genome A
- 19. Example: Pseudomonas Tree Maximum Distance Cutoff = 0.42 Minimum Number of Genomes = 3 0.227 0.256 0.397 0.393 0.411 0.428 0.430 0 0.481 P. fluorescens Pf-5 P. putida KT2440 P. fluorescens PfO-1 P. syringae tomato DC3000 P. syringae phaseolicola 1448A P. syringae syringae B728a P. aeruginosa PAO1 P. aeruginosa PA14 Acinetobacter ADP1 Minimum Distance Cutoff = 0.10
- 20. Predicting Similar Aged GIs GI Insertion Query Genome 1 genome < distance X Query Genome GI Insertion
- 24. Negative Dataset Query Genome 1 genome > distance X GI Insertion Query Genome GI Insertion
- 27. GI Prediction Accuracy Positive Dataset Negative Dataset Predicted Dataset Entire Genome TP FP FN Precision = TP / (TP + FP) Recall = TP / (TP + FN) TN
- 28. GI Prediction Accuracy (Langille, et al.,2008) Tool Average number of nucleotides in GIs per genome (kb) Precision Recall Overall Accuracy SIGI-HMM 233 92 33.0 86 IslandPath/ Dimob 171 86 36 86 PAI IDA 163 68 32 84 Centroid 171 61 28 82 IslandPath/ Dinuc 444 55 53 82 Alien Hunter 1265 38 77 71 Literature* 639 100 87 96
- 35. IslandPick – Manual genome selection
- 41. LES Prophage (Winstanley, Langille, et al., 2008)
- 42. LES Genomic Islands (Winstanley, Langille, et al., 2008)
- 49. Archaea and CRISPRs Prevalence of CRISPRs in Archaea genomes could result in reduced phage genes Archaea Bacteria Genomes containing a CRISPR 90% 40% Proportion of phage genes 0.10% 0.79% Proportion of GIs with a phage gene 5.1% 17.6%
- 53. Acknowledgements Supervisor Dr. Fiona Brinkman Supervisor Committee Dr. Baillie Dr. Pio P. aeruginosa LES Craig Winstanley Roger Levesque Bob Hancock Nick Thomson