1. Individual sequences in large sets of gene sequences may be distinguished efficiently by combinations of shared sub-sequences Mark J Gibbs, John S Armstrong and Adrian J Gibbs BMC Bioinformatics 2005, 6:90 Presented by Miguel Gonzalez

2. Outline Background Background Results Results Discussion Discussion

3. Background Organism identification Organism identification Comparative Gene Sequencing Comparative Gene Sequencing DNA probes DNA probes

4. The Problem! Using contemporary biological research is too time consuming and expensive. Using contemporary biological research is too time consuming and expensive. Usually complex techniques are involved. Usually complex techniques are involved.

5. The Solution Develop a method for identifying sequences that is not extremely specific. Develop a method for identifying sequences that is not extremely specific. Probes can be found that bind to more than target sequence to produce unique binding patterns or fingerprints. Probes can be found that bind to more than target sequence to produce unique binding patterns or fingerprints.

6. Hypothesis To develop a method for identifying sequences efficiently using distinguishing sub-sequences (DSSs). To develop a method for identifying sequences efficiently using distinguishing sub-sequences (DSSs).

7. Strategy The study uses the methods of taxonomy where combinations of characters shared by different members of a target organisms. The study uses the methods of taxonomy where combinations of characters shared by different members of a target organisms. The advantage is that identification requires fewer characters and questions to identify an individual target. The advantage is that identification requires fewer characters and questions to identify an individual target.

8. Strategy The minimum number of characters for this method is defined by the binary logarithm X = log 2 Y, X = # of characters; Y = # of targets The minimum number of characters for this method is defined by the binary logarithm X = log 2 Y, X = # of characters; Y = # of targets Ex. 10 characters could identify a set of 1024 targets. Ex. 10 characters could identify a set of 1024 targets.

9. Testing Hypothesis Three sets of cytochrome oxidase c 1 (CO1) sequences were used: animal, insect, and moth Three sets of cytochrome oxidase c 1 (CO1) sequences were used: animal, insect, and moth CO1-animal had 96 species CO1-animal had 96 species CO1-insect had 92 species CO1-insect had 92 species CO1-moth had 201 species CO1-moth had 201 species

10. Target Sequence ClustalX was performed on the 3 sets of sequences to find a target region within sequences. ClustalX was performed on the 3 sets of sequences to find a target region within sequences. Pools of sub-sequences were created ranging from lengths of 6-31 nucleotides Pools of sub-sequences were created ranging from lengths of 6-31 nucleotides From the sub-sequences, distinguishing sub- sequences were identified From the sub-sequences, distinguishing sub- sequences were identified

11. Results

12. Results

13. Results

14. Discussion A method was produced where sub-sequences are found which, distinguish the gene sequences or groups of gene sequences from which they came from. A method was produced where sub-sequences are found which, distinguish the gene sequences or groups of gene sequences from which they came from. Sequence diversity and sub-sequence length were found to be major factors influencing the number of subsequences available as probe targets. Sequence diversity and sub-sequence length were found to be major factors influencing the number of subsequences available as probe targets.