Data Mining in DNA: Using the SUBDUE Knowledge Discovery System to Find Potential Gene Regulatory Sequences

1. Data Mining in DNA: Using the SUBDUE Knowledge Discovery System to Find Potential Gene Regulatory Sequences by Ronald K. Maglothin

2. Committee Members • Dr. Lawrence B. Holder, Supervisor • Dr. Diane J. Cook • Dr. Lynn L. Peterson

3. Outline • DNA Sequence Domain • SUBDUE Knowledge Discovery System • Experiments with Unsupervised SUBDUE • Experiments with Supervised SUBDUE • Conclusion and Future Work

4. DNA Structure • All cells use DNA to store their genetic information. • A DNA molecule is composed of two linear strands coiled in a double helix. • Each strand is made of the bases adenine (A), thymine (T), cytosine (C), and guanine (G), joined in a linear sequence.

5. DNA Sequence • These four bases constitute a four-letter alphabet that cells use to store genetic information. • Molecular biologists can break up a DNA molecule and determine its base sequence, which can be stored as a character string in a computer: TTCAGCCGATATCCTGGTCAGATTCTCT AAGTCGGCTATAGGACCAGTCTAAGAGA

6. Genes • A gene is a DNA sequence that encodes instructions for building a protein. • Gene expression is the process of using a gene to make a protein: transcription translation DNA RNA Protein gene transcript product

7. Gene Regulation • Primary mechanism is to control the rate of DNA transcription: • Faster transcription more protein • Slower transcription less protein • Transcription rate is controlled by transcription factors, which are proteins which bind to specific DNA sequences.

8. Human Genome Project • A U.S.-led, worldwide effort to determine the complete DNA sequence for humans, as well as several other organisms. • These sequences will be used to study: • mechanisms of disease • growth and development • evolutionary relationships

9. A Genome is a LOT of Data • Raw sequence (text) • Human (2005): 3 x 10 9 base pairs • Yeast (finished): 1.2 x 107 base pairs • Annotated sequence (Relational DB) • Links to 3D structures of protein products, other genes in family, known transcription factors, journal references, and other databases.

10. A Rich Domain for Knowledge Discovery • Most of the sequences (and genes) have unknown function. • Efficient algorithms are needed to: • identify important patterns • identify and classify possible genes • infer relationships between genes • predict protein structure

11. The SUBDUE Knowledge Discovery System • Input: A graph G • Output: A list of substructures that compress G well • Uses a computationally-constrained beam search and inexact graph match

12. What is a substructure? • A definition subgraph and a list of subgraph instances : Input Graph next next next next next next A T A C A T G 1 2 3 4 5 6 7 Substructure Definition Instances next A T next A T 1 2 next A T 5 6

13. MDL Heuristic • SUBDUE uses the Minimum Description Length Principle to evaluate substructures. • Description Length of a graph is the number of bits needed to send the graph’s adjacency matrix to a remote computer. • Goal is to minimize DL(S) + DL(G|S).

14. SUBDUE Parameters • Iterations: Graph is compressed using the best substructure, discovery is restarted • Threshold: Controls how much two subgraphs can differ to be considered similar • Beam Width: The number of substructures in the expansion list

15. Graph Representations • Simple linear next next next next A C A T G Downstream edges 4 3 3 2 2 2 1 1 1 1 A C A T G

16. Graph Representations 5 • Start vertex 4 3 2 next next next next 1 A C A T G Start Backbone next next next next base base base base base name name name name name A C A T G

17. Graph Representations • Backbone-star * star star star star star next next next next base base base base base name name name name name A C A T G

18. Unsupervised SUBDUE • Input: An entire yeast chromosome next next next next A C A T G Heuristic: • Results: Not good; patterns with two to three bases

19. Polynomial Heuristic

20. Unsupervised SUBDUE -Discussion • Random noise is not a meaningful kind of pattern variation in DNA. • Unsupervised SUBDUE finds DNA patterns that are hard to evaluate and that are not focused on any target concept. • We need to give SUBDUE more targeted input data and to modify the system to use it effectively.

21. Supervised SUBDUE • Give SUBDUE two graphs: a graph of positive instances of a target concept, and a graph of negative instances. • SUBDUE discovers substructures in the positive graph, finds instances in the negative graph, and bases the overall heuristic value on the values in both graphs.

22. New Data Sets • Clusters of coexpressed yeast genes compiled by Brazma et al., from expression data generated by DeRisi et al. • The expression level of each gene in a cluster changed at the same time and by a similar degree during the experiment; perhaps some genes in a cluster are regulated by similar mechanisms?

23. New Data Sets • Positive examples: • 300-bp upstream windows (both strands) for all genes in a given cluster • Negative examples: • 300-bp upstream windows for genes not in the cluster, OR • 300-bp windows randomly selected from the complete genome (probably not involved in gene regulation)

24. Supervised Heuristic • Based on the substructure’s values in the positive and negative graphs Numerator set to 1 when no negative instances

25. Compression Ratio • Normalize the graph values by using the inverse of the graph compression

26. Negative Graph Value • When there are no negative instances, setting numerator to 1 actually penalizes such substructures. • Using 2 x DL(G-) in this situation gave better results.

27. Ratio Heuristic Results

28. Concept DL Heuristic • Based on the size of a message containing the compressed positive graph, plus the errors (negative instances).

29. Concept DL Heuristic Results Relative graph size affected results

30. Backbone Representation next next next next base base base base base • “Base” vertices allowed don’t-care positions, but heuristic had to be changed to accommodate them. • Overlap became very important. name name name name name A C A T G

31. DL Equations

32. Negative Graph Value • Using 2 x DL(G-) for no negative instances favored such substructures too strongly.

33. Compression Difference Heuristic • Use subtraction with the compression values instead of division.

34. Results Cluster cr4.111101.77

35. Results Cluster c2_4.2222200.39

36. Results of Brazma et al. Cluster c2_4.2222200.39

37. Brazma Heuristic • Based on number of positive and negative instances

38. SUBDUE Using Brazma Heuristic Cluster c2_4.2222200.39

39. Conclusion • SUBDUE can be used to discover likely transcription factor binding sites. • Patterns found by SUBDUE are different from those found by string-based algorithms, due to the graph representation, beam search, and different search heuristic.

40. Conclusion • Patterns found by unsupervised SUBDUE in DNA are difficult to evaluate. • Using supervised SUBDUE can greatly focus the search on the target concept. • Choosing the right graph representation and heuristic are critical to success.

41. Future Work • Further refinement of the supervised MDL heuristic. • Application of graph grammar theory to SUBDUE’s search. • Close collaboration with molecular biologists to select data sets and evaluate results.