Team:Stanford/Model
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Our DNA detection system called for the need to develop a computer algorithm that could find matching PAM sequences in given DNA sequences. This tool would highly increase the recognition patterns of sgRNA's and enable us to develop sgRNA and PAM sequences that would bind to our DNA sequences of interest. CHOP-CHOP as we like to call it, its a program that utilizes a highly efficient algorithm that enables the user to input a specific sequence of DNA as well as specific PAM sequences and return to the user locations in the DNA sequence were the Cas9 proteins could potentially bind and enable recognition. Taking our program a step further, we decided to include that ability to select random DNA sequences and test the probability that a set of PAM sequences would be present and enable recognition by our system. Finally, we implemented a further characteristic that would enable users to run different lengths of DNA sequences and see the prevalence of the PAM sequences among different lengths of DNA sequences.
Simulations conducted in CHOP-CHOP appeared to resemble PAM-identification processes done by our team-members. This suggested that the program implemented correctly and efficiently. Running the simulations multiple times appeared to suggest that PAM sequences prevalence tend to saturate at around 30% of the length of the DNA sequence. Although unclear why this is the case, we will continue to fine tune CHOP-CHOP and discover if these results are truly life-applicable or a simple computer bug.
Our original code can be found here.