1. Bioinformatics Brad Windle bwindle@vcu.edu Ph# 628-1956 Web Site: http://www.people.vcu.edu/~bwindle/Courseshttp://www.people.vcu.edu/~bwindle/Courses Click on Link to MEDC 310 course Or http://www.phc.vcu.edu/310/

2. Profiling

3. The term "bioinformatics" is about 15 years old. It covers a variety of data analyses that include: DNA and protein sequence analysis Biological analysis of drugs, can overlap with chemoinformatics Genetics Taxonomy Clinical data statistics Genomic and proteomic research Bioinformatics is sometimes equated to the term "data mining", which is commonly used in e-business and internet data handling.

4. Chemoinformatics Chemoinformatics has a special challenge in that a structure of a compound or drug needs to be quantified. Specific structures are characterized by molecular descriptors useful in Quantitative Structure Activity Relationship (QSAR) modeling. QSAR tells you what about the structure of a drug that makes it do what it does. Much of this information has implications on what a drug will do in a cell. However, the complexity of a cell makes the reality of what a drug does in the cell deviate significantly from what is anticipated based on chemistry and enzymatic assays. This stresses the need for characterizing drugs based on more biological data.

5. Analogies for looking for patterns Looking at patterns in images

6. A mixture of many patterns We need to identify individual patterns

7. There are methods for extracting the patterns from the data

9. There is also noise tht obscures the patterns

10. One method for identifying object patterns of interest amidst the noise

11. Another method for identifying different object patterns of interest amidst the noise

12. This is what was actually buried in the noise

13. Questions?

14. Philosophy of Science Reductionist Approach (Reductionism) VS Systems Approach (Systemism)

15. Reductionist

16. Systems Approach

18. How Does a Cell, or Person Respond to Therapy or a Drug? Treat 10 people suffering from Disease A with Drug X. 2 people suffer adverse reactions 3 exhibit good recovery from disease 2 exhibit modest recovery from disease 3 exhibit no sign of recovery from disease

19. What Factors Cause in Differences Between People? Genes and their sequence Health-wise Disease Health-related Traits Response to Drugs

20. What Are the Differences in Genes? Single nucleotide polymorphisms (SNPs) SerSerIleAsnGlyGlnLeuArgPro AGTTCTATAAATGGCCAGCTTAGACCT TCAAGATATTTACCGGTCGAATCTGGA SerSerIleHisGlyGlnIleArgPro AGTTCTATACATGGCCAGATTAGACCA TCAAGATATGTACCGGTCTAATCTGGT

21. How does a difference in a gene affect drug response? Transport of the drug Metabolism of the drug Interaction with the drug target

22. 5 Million SNPs Let’s say there are 10 SNPs that contribute to response to Drug X Combinatorial approach to identifying SNPs that correlate with drug response All combinations = 10 60 Narrow SNPs down to those within genes to 100,000 Combinations = 10 43

23. Traveling Salesman Problem

24. SNPs thus far described were inherited, affecting the quality of proteins What about differences between people that are somatic? What about quantitative differences in proteins?

25. Differences in Protein Expression and Gene Expression 20,0000 genes - Genomics 100,000 proteins - Proteomics

27. In genomics and proteomics research, the data is extensive and the patterns complex. The emphasis shifts from asking specific questions or testing hypotheses to trying to filter out the most significant observation the data offers. Bioinformatics and Data Mining in general use two forms of learning: Supervised learning is the process of learning by example: Use example patterns with known characteristics to learn and predict characteristics for the unknown This is essentially the modeling process Unsupervised learning and Supervised learning

28. Unsupervised learning is the learning by observation and exploratory data analysis is a general form Let the data reveal prominent patterns and associations, you don’t look for specific patterns Exploratory data analysis is used when there is no hypothesis to test, or when there is no specific pattern expected. This type of analysis shows the most significant pattern or trends within the data; it does not imply biologically or statistical significant. Cluster analysis is a popular form of exploratory data analysis.

29. Cluster analysis sorts whatever is being analyzed into clusters with the greatest similarities in trend or pattern. It is a form of non-descriptive statistics and exploratory data analysis. A dendrogram or tree diagram is used to present the results. Below is an example of a dendrogram for bacterial species of Escherichia.

30. New technology= lots of data

31. Microarray Technology DNA Microarray Cell 1’s mRNA Cell 2’s mRNA

32. Pseudo-colored MicroarraySpots

33. The total intensity for each spot is summed and the values plotted on a scatterplot. A scatterplot of 2000 points is shown. Each point respresents a gene.

34. Cluster analysis methods The most straightforward methods involve calculating the Euclidean (Euclid) distance between two points, for all combinations of points. Pythagorean Theorem

35. If we perform cluster analysis on the 2000 points, we can see that we have one giant cluster with a handful of outliers.

37. Adding Dimensions to Cluster Analysis

38. The distance calculation would be: Thus, while we can't visualize more than three dimensions, the computer can perform cluster analysis on as many dimensions imaginable or as processing time allows.

39. Pearson Correlation Coefficient

41. Two-fold Cluster Analysis Gene expression analysis in drug development can involve a large number of genes and a large number of drugs. It is not only important to identify what genes cluster together, but also what drugs cluster. This is done by two-fold cluster analysis. The genes are arranged and clustered as well as the drugs. The drugs that illicit similar gene expression patterns will cluster. Both clusters can be viewed in a single 2-D dendrogram.

42. Questions?

43. Cluster Tree of cell lines

44. Classifying Cancer Using supervised learning, models have been developed Classifying different subsets of cancers that the pathologist can’t Predicting response to therapy and patient prognosis

45. Any kind of data can be explored

46. Cell response profile Monks et al. Anti-Cancer Drug Design 12:553 (1997)

47. Drug clusters correspond to drug targets or mechanisms of action not necessarily drug structure. Scherf et al, nature genetics 24:236 (2000)

52. Exploratory Tools allows us to focus on what most relevant based on the data And developed relevant hypotheses For example Geldanamycin is cytotoxic through inhibition of microtubules

53. The End Any Questions?