MICHAEL MORRA CSE 4939W Detection of Transcription Factor Binding Sites

Project Recap Implement a method used to accurately and precisely discover the locations of transcription factor binding sites within a DNA sequence. 4 species (Human, Mouse, Fruit Fly & Yeast)  52 Transcription Factors, 524 binding sites Image from:

Multiple Sequence Alignment To be able to analyze the data effectively, each transcription factor’s binding sites need to be aligned ClustalW2 >s1 GACTTTTCGCT >s2 CGATTTTCTCG >s3 GCATTTTCCCA >s4 AGAGAAAACCC >s5 GAATAACCCAAGAGAAA >s6 ACAGAAAAATC >s7 CGAGAAAATCG >s8 TGGTTTTCCCG >s9 GGGTTTCTCCC

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

Implementation Microsoft Visual Studio - C++  Input  Multiple Sequence Alignment of a transcription factor’s binding sites (.txt file)  All binding sites of a species (.txt file)  Output  Scores  Results of Leave One Out Cross Validation Testing and Efficiency purposes

Implementation Scoring Algorithm  Input: Alignment  Function: Create the scoring matrix Leave One Out Cross Validation  Input: Alignment and Binding Sites  Function: Test the effectiveness of the scoring matrix

Functionality Sequence to be scored is shorter than the alignment  Slide the sequence over the alignment and take the highest scoring portion Sequence to be scored is longer than the alignment  Slide the alignment over the sequence and take the highest scoring portion

Testing Scoring Algorithm/LOOCV Unit testing will be done on each function and critical portions of code as they are implemented Once it is determined that the code is functioning correctly and all formulas are providing correct results, implementation can continue

Testing Overall Performance 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%)

Progress Alignments  Complete Scoring Algorithm  Mostly Complete Leave One Out Cross Validation  Partially Complete

Remaining Schedule Nov 15 th – Nov 19th  Finish implementation and testing of scoring algorithm Nov 20 th – 29 th  Finish implementation of leave one out algorithm  Begin testing of entire program’s effectiveness Nov 30 th – Dec 6 th  Complete testing  Tweak program to run more effectively/accurately

Questions?