1. Kyle Tretina with a team led by Dr. Pattle P. Pun in collaboration with Mr. Ross Leung of CUHK Analysis of the Positively Selected and Non-Positively Selected Non-Protein Coding Sequences of Chromosome 16

2. Introduction: Story of Evolutionary History Bacteria < Fish < Primate < Human Story: increasing organismal complexity as evolution proceeds

3. WHY? “But little Mouse, you are not alone, In proving foresight may be in vain: The best laid schemes of mice and men Go often askew, And leave us nothing but grief and pain, For promised joy!” – Robert Burns (1785)

4. Genetics Central Dogma: DNA  RNA  Protein Complexity ~ Number of Genes? Humans ~30,000 Flies ~ 14,000

5. G-Value Paradox

6. Complexity (K) ~ Gene Number (N)? Relationship? proportional: K ~ N polynomial: K ~ Na exponential: K ~ aN factorial: K ~ N! Jean-Michel Claveries: ON/OFF states 2 30,000 / 2 14,000 ≈ 3x10 4816

7. Goal Determine the role of non-coding DNA in gene regulation by looking at the functions of non-coding SNPs that are positively selected or non-positively selected on chromosome 16

8. Definitions SNP: single nucleotide polymorphism Variable between populations Importance likely due to stability of variation Selection: description of phenomena that only organisms best adapted to their environment tend to survive and create progeny Gene-selection algorithm and neutral selection theory (wrench)

9. Methods Overview HapMap Database  Selection Data  List of Chr16 SNPs UCSC Genome Database Mirror  SNP flanking sequence TRANSFAC  related transcription factor data for each SNP flanking sequence PReMod  confirm results

10. HapMap Phase I Data HapMap Project: an international effort to identify and catalog genetic similarities and differences in human beings (Haplotype Maps), also includes: Selection Data  List of Chr16 SNPs ~25,000 non-positively selected ~5,000 positively selected

12. UCSC Genome Browser Genome.UCSC.edu: a website containing several reference sequences and tools for visual and computational analysis Methods: Enter in each from list of RSID’s (SNP Identifiers) Note intersecting sequences Copy/Paste Sequences

15. UCSC Genome Browser Mirror Efficiency ~70seq/hr for 1.5yrs = ~1/3 sequences gathered 2hrs Online Instructions, but Complicated Data Structure Henry Ford: 1.1 million lines source code Many thanks to the Dr. Hayward (Wheaton College CS Faculty)

16. Sequences Collected Graph 1. The distributions of the positively selected SNPs used in the study across human chromosome 16 Graph 2. The distributions of the non-positively selected SNPs used in the study across human chromosome 16

17. TRANSFAC TRANSFAC: a relational database, available via the web as six flat files including various data concerning transcription factors, DNA-binding sites, and target genes Automation at CUHK

20. PReMod PReMod: a new database of genome-wide cis-regulatory module (CRM) predictions for both the human and the mouse genomes. Enter ranges for SNP sequences Look for same pattern as TRANSFAC

22. Analysis MySQL Tables Programmed Scripts: Word Patterns: i.e. keywords, recurring identifiers Unique Entries Progress Statistics Overlap between N+ selected and + selected SNPs

23. Results SNP Selection RS NumbersSequence Gathered Non-Positive25,6226173 (24%) Positive47504750 (100%) Table 1. A summary of the manual SNP flanking sequence gathering from the UCSC Genome Browser

24. Results SNP Selection TotalNo SitesUnique Matches in Other Dataset TRANSFAC Entries to Be Looked Up Non- Positive25,5941,611 (6%)3,218 (13%)20,765 (81%)82 (<1%) Positive33,7702,437 (7%)361 (1.0%)30,972 (92%)10,641(32%)

25. Conclusions Data not all in yet Possible implications: Central Dogma Biology: information flow Quantification Genetic Natural Selection Views of Complexity of Humans Lesson Learned: value of bioinformatics High volume data requires computational analysis, not manual

26. Acknowledgements Many thanks to Dr. Pun, for letting me get involved in this project, for his vision and mentorship. Special thanks to Dr. Hayward, for putting in extra hours unpaid so that a student can follow his dreams of graduate school. Thanks to our collaborators at the Chinese University of Honk Kong – Dr. Tsui and Mr. Leung – for accessing the TRANSFAC database for us, and for being flexible to the demands of our project. The most thanks to God, for blessing me with the opportunity to work hard and learn. I pray that I might always be able to do these two things earnestly and voraciously.