Lecture 27 Special Topics II (Intro to Bioinformatics)

1. Questions
2. Differences between different fields
   1. Biomedical engineering
      1. Treating diseases directly
   2. Bioinformatics
      1. More interdisciplinary
      2. Uses data to solve biology
   3. Computational biology
      1. using cs, math to understand biology
   4. Biotechnology
      1. using biology as technology
3. Bioinformatics
   1. Cross section between stats, cs, biology
   2. Fields in bioinformatics
      1. Translational bioinformatics - techniques for transforming biomedical data to support health
      2. Public Health Informatics - development of methodologies for supporting public health needs
4. Genome
   1. C-Value Paradox: genome size fails to correlate well with apparent complexity
   2. Onions have more base pairs than humans
   3. Human genome project
      1. Launched in 1990, completed in 2003
   4. Bioinformatics: Genomic Analysis
      1. Similarities in sequences: analyze traits and see how they translate to similar traits
      2. Differences: see how they translate to different traits
   5. Conduct a Study
      1. Is this base difference significant for disease?
         1. Take two groups, one health, one has disease
      2. Sickle Cell disease
         1. Found out that one genetic base would lead to sickle cell disease
   6. Applications to Neuroscience
      1. We can use genome from artic ground squirrels for seeing how to defend against stroke
   7. Genomic Analysis for Cancer Treatment and Diagnosis
      1. compare genome to health genome model and compare to other people who have cancer
      2. pinpoint mutations
   8. Sequence Alignment
      1. sequence alignment - way to arranging the dna sequences to identify regions of similarity
   9. Global and Local alignment
      1. Global approach compares one who sequence with other entire sequences
      2. Local alignment uses a subset of a sequence and attempts to align it to subset of other sequences
   10. BLAST: Basic Local Alignment Search Tool
       1. Uses a seed and extend algorithm
       2. its heuristic, just trial and error
       3. BLAST sacrifices accuracy for time, fast but not super accurate
   11. Understanding Evolution
       1. phylogenic trees allow us to visualize evolution
5. Public Health Informatics
   1. captures, manages, and analyzes information to improve population-level health outcomes
   2. Lead poisoning research project - can use data to prevent lead poisoning by zip codes