Summary
Our project's primary objective revolves around the swift and accurate detection of pathogens carried by mosquitoes. What sets our project apart is its capability to seamlessly transmit this crucial pathogen information directly to researchers and epidemiologists in real-time. To tackle this ambitious goal, we harnessed the remarkable potential of the SHERLOCK CRISPR Cas13 system.
Our approach is elegantly simple yet highly effective. We employ guideRNA sequences that are specifically tailored to target the viral RNA of pathogens present in the mosquitoes. These guideRNA sequences serve as the navigators for the Cas13 enzyme, which is tasked with identifying and binding to the pathogen's RNA. When this binding event occurs, it triggers a cascade of enzymatic reactions that ultimately produce a fluorescent signal, thanks to the sensor molecule BIOTIN-FAM. This fluorescence serves as the readout, providing us with the valuable information needed to detect and identify the presence of these harmful pathogens.
However, one of the primary challenges we grapple with in this endeavor is the inherent mutability of viral RNA. Viruses are known for their rapid mutation rates, which can pose difficulties in accurately targeting and detecting them.
Initially, we undertook the labor-intensive process of manually designing guideRNA and target sequences, using the available online tools. While this approach did yield some success, it quickly became evident that it was a time-consuming and tedious task. It required meticulous precision and a substantial investment of effort.
Recognizing the need for a more efficient and accessible solution, we embarked on the idea of developing specialized software. This software, once realized, will provide researchers and students with a user-friendly platform to generate consensus guideRNA sequences swiftly and accurately. By simplifying and automating this critical step, we aim to empower a broader community of scientists and budding researchers to contribute to the advancement of pathogen detection and virology research.
To be more precise, the software will generate a reverse DNA primer tailored specifically to the target sequence. When coupled with the forward DNA primer designed exclusively for the enzyme employed in the SHERLOCK experiment and utilized in conjunction with RT-PCR, we yield a guide RNA, as elucidated in the Wet Lab section.
In essence, our project represents the convergence of cutting-edge technology, molecular biology, and software development. We are driven by a commitment to make pathogen detection more accessible and efficient, all while addressing the ever-present challenge of viral RNA mutability. Our vision is to facilitate greater understanding and control of mosquito-borne diseases, ultimately contributing to global health efforts.
Our primary objective was to develop software that can be easily reused by researchers, regardless of the target sequence they are working on. We are committed to simplifying complex processes to ensure that using our software is not only functional but also an enjoyable experience. Our goal is to make the life of the researcher easier by automating and speeding up tasks that would have otherwise taken a lot of time.
Key Features
1/ Custom Consensus Sequence Calculation:
Enables the calculation of consensus sequences from multiple sequence alignments. This can be valuable in bioinformatics and molecular biology for identifying common nucleotides or amino acids in aligned sequences. Moreover, this consensus sequence can be used in the creation of Guide RNA.
3/ Scalability:
Adaptable for different numbers of clusters and desired consensus sequences, enhancing flexibility to accommodate various research requirements. Furthermore, the code works with multiple alignment files independently of the number of sequences in each file.
5/ Extensibility:
Depending on research requirements, users can extend and integrate the software with additional bioinformatics tools and pipelines for more comprehensive analyses.
2/ Clustering Capabilities:
Utilizes k-means clustering, a powerful unsupervised machine learning technique, to cluster sequences, helping group similar sequences together, aiding various biological and genetic analyses.
4/ User Interaction:
Prompts user input, making the software interactive and adaptable to specific research requirements. Moreover, some plots are interactive, allowing the user greater freedom.
Software Development
1) Understanding Phase:
This was the first step where the team thought about the problem we were trying to solve. In this case, it was about dealing with the quick changes in viral RNA and the need for a constant supply of guideRNA sequences for a system called SHERLOCK.
2) Defining Phase:
In this stage, the team carefully detailed what the users needed from the software. They set the rules for BLAST searches and sequence alignment, and decided how to measure the analysis of subsequences.
3) Ideation Phase:
This stage involved translating the gathered requirements into a structured plan. The software's architecture was designed to efficiently handle sequence input, analysis, and alignment while ensuring flexibility through user-defined parameters. This included running BLAST searches, applying user filters, aligning sequences, and analyzing subsequences. The team also thought about how to give users the option to choose the best consensus sequences and run additional BLAST searches, making the analysis process more flexible
4) Prototyping and Testing Phases:
The final stages involved turning the design into actual code and testing it thoroughly with users. The goal was to create a strong, user-friendly tool that could efficiently handle sequence input, analysis, alignment, identification of consensus sequences, and additional searches. The tool was also designed to let users customize results to meet their specific goals. In addition, the software was built using appropriate programming languages and libraries, such as Python and BioPython, to handle biological data efficiently. As code development progressed, rigorous testing was conducted. This included unit testing to ensure individual components functioned correctly, integration testing to verify the interactions between different modules.This is the phase that took the most time during the development.
In summary, the software development process was a meticulously orchestrated journey that began with a comprehensive requirement analysis, delving deep into the intricacies of user needs. This was followed by a design phase, where the blueprint of the application was crafted, laying the foundation for its success. Subsequently, the coding and testing phases came into play, breathing life into the design and rigorously evaluating its functionality to deliver valuable results for the experiments.
