1. MICHAEL MORRA CSE 4939W Detection of Transcription Factor Binding Sites

2. Background DNA is comprised of a combination of 4 chemical bases Adenine – A Thymine – T Guanine – G Cytosine - C Image from : http://www.genetest.org/page5.html

3. Background (Continued) Each individual organism has a unique DNA sequence The DNA sequence contains information which can be used by a cell to construct proteins Each set of instructions within this sequence is called a gene Image from: http://www.buzzle.com/articles/point-mutations.html

4. Transcription Factors To regulate the expression of genes, proteins known as transcription factors are used Each transcription factor binds to the DNA sequence, turning a gene on or off Image from: http://www.cs.uiuc.edu/homes/sinhas/work.html

5. Binding Sites The portions of the DNA where the transcription factors are able to bind are known as binding sites A single transcription factor’s binding sites may vary

6. Introduction The detection of binding sites is important to understanding the regulatory network of an organism As binding sites can vary considerably, searching for them within a DNA sequence is tedious

7. Project Implement a method used to accurately and precisely discover the locations of transcription factor binding sites within a DNA sequence.

8. Data 4 species (Human, Mouse, Fruit Fly & Yeast) Human  26 Transcription Factors, 300 binding sites Mouse  12 Transcription Factors, 98 binding sites Fruit Fly  6 Transcription factors, 51 binding sites Yeast  8 Transcription Factors, 75 binding sites

9. Multiple Sequence Alignment To be able to analyze the data effectively, each transcription factor’s binding sites need to be aligned http://www.ebi.ac.uk/Tools/clustalw2/index.html >s1 GACTTTTCGCT >s2 CGATTTTCTCG >s3 GCATTTTCCCA >s4 AGAGAAAACCC >s5 GAATAACCCAAGAGAAA >s6 ACAGAAAAATC >s7 CGAGAAAATCG >s8 TGGTTTTCCCG >s9 GGGTTTCTCCC

10. Scoring Berg and von Hippel method l = length of the sequence to be scored j = position in the sequence n j = number of times a base occurs at position j in the alignment t j = base at position j in the sequence to be scored n j (0) = most common base at position j

11. Scoring Example ACTCA n 1 (0) = 3 n 2 (0) = 2 n 3 (0) = 2 n 4 (0) = 2 n 5 (0) = 2 n 1 (A) = 3 n 2 (C) = 1 n 3 (T) = 2 n 4 (C) = 1 n 5 (A) = 2 Score = log(1) + log(1.5/2.5) + log(1) + log(1.5/2.5) + log(1) = -0.443697499

12. Leave One Out Cross Validation To determine the effectiveness of the algorithm, a cross validation technique is used This technique involves leaving one binding site out when the multiple sequence alignment is performed, and then scoring that left out sequence If the algorithm is effective, the left out sequence should score higher than the majority of other binding sites within that species. (>80-90%)

13. Implementation C++  Input  Multiple Sequence Alignment of a transcription factor’s binding sites  All binding sites of a species  Output  Scores  Results of Leave One Out Cross Validation

14. Desired Functionality Deal with cases where the sequence to be scored is longer or shorter than the multiple sequence alignment  Slide the sequence over the alignment and take the highest scoring portion

15. Timeline Oct 4 th – Oct 18 th  Create multiple sequence alignments for all transcription factors Oct 18 th – Nov 15 th  Implement scoring algorithm in C++ Nov 15 th – Nov 29 th  Implement leave one out methods Nov 29 th – Dec 6 th  Tweaks and Improvements

16. Questions? Image from: http://www.ideacenter.org/contentmgr/showdetails.php/id/954