Our Team developed a web tool that offers an improved user experience that streamlines the gRNA design process. The web-tool helps in detecting PAM sites and designing gRNAs for large DNA sequences for a range of CRISPR reactions involving different Cas Proteins in an efficient manner. As a final result, the web-app produces gRNA sequences targeting multiple PAM sites detected in the DNA sequence that can be readily ordered for different CRISPR experiments. Since iGEM promotes open science practices, all the codes are available on Github, which makes it easier for future iGEM teams who wish to build upon our work and embed it in new workflows

Our project involves the detection of the E7 oncogene of the Human Papillomavirus 16. We are using the trans cleavage property of the CRISPR/Cas12a reaction to detect the E7 gene. The gRNAs involved in the reaction are usually 20-24 nucleotide long sequences that bind to specific sites present on the target DNA. The part of the gRNA sequence that is complementary to the target sequence is known as a spacer. In order for Cas proteins to function, it also requires a specific protospacer adjacent motif (PAM) that varies depending on the type of Cas protein used. In the case of our project, our target E7 oncogene is 298 nucleotides long which may have multiple PAM sites resulting in the possibility of multiple suitable gRNAs for detection. The detection of multiple PAM sites in such a large DNA sequence was very cumbersome and time-consuming, and we realized there is a need for a web-based system which could automate this whole process so that the first model could be made bioinformatically. 

Our webtool tool provides an effective computational method to identify unique targets for gRNAs of the CRISPR/Cas system and design specific gRNAs for the same based on the Cas protein used. 

Our workflow as mentioned below is effortless and straightforward:

The Aho-Corasick algorithm can be used to efficiently search for multiple patterns in a large blob of text, making it a really useful algorithm in data science and many other areas. 
This algorithm is helpful to find all occurrences of all given sets of keywords. It is a kind of Dictionary-matching algorithm. It uses a tree structure using all keywords. After making the tree, it tries to convert the tree as an automaton to do the searching in linear time. Our web app is required to search for PAM sequence patterns in a large string of target DNA in an efficient manner hence we decided to use this algorithm for the same.