After the commencement of the project, we established contact with the experimental team, known as "iGEM-USTC", to understand the background related to synthetic biology.

In the course of the discussion, we learned that performing omics analysis in synthetic biology is a time-consuming and laborious task. One of the reasons is that the models used for omics analysis are not centralized and are not beginner-friendly.

Therefore, we felt that we should make our contribution in this field.

Based on the idea of the intersection of computer and biology, we try to integrate the big data model of commonly used omics analysis. At the same time, under the guidance of the laboratory teachers, we have constructed a full-process optimized single-cell analysis to facilitate transcriptomics-based synthetic biology analysis.

Our contributions are as follows:

Mo-BASE Platform Overview

Our platform, Mo-BASE, serves as a collaborative hub for sharing resources, literature, and educational materials related to bioinformatics and synthetic biology. By offering a range of user-friendly applications, we aim to empower researchers and students alike in their scientific endeavors.

scRNA-seq Workflow Development

We are focused on creating an optimized single-cell RNA sequencing (scRNA-seq) analysis workflow, which integrates cutting-edge techniques for data acquisition, quality control, dimensionality reduction, clustering, and gene annotation. This workflow will be developed into an intuitive app that guides users through each step, making complex analysis accessible to all.

Medical Image Segmentation Application

Leveraging the SAM-Med-2D/3D model, we will implement a web-based application for medical image segmentation. Users can upload their 2D or 3D medical images and receive accurate segmentation results in real time, facilitating their downstream analysis and clinical research.

Enhancing Educational Resources

Mo-BASE will provide a wealth of educational materials, including tutorials on deep learning, literature review strategies, and guides on statistical processing workflows. This ensures that users can quickly gain the knowledge needed to leverage our tools effectively, enhancing their research capabilities.

Fostering Community and Collaboration

A vibrant academic community is integral to the Mo-BASE platform. Users will have opportunities to engage in discussions, share insights, and collaborate on projects, fostering a culture of innovation and support within the field of synthetic biology and bioinformatics.

Making Science Accessible

Our commitment to education and accessibility means that Mo-BASE is designed for both newcomers and seasoned researchers. By simplifying complex bioinformatics concepts and providing user-friendly applications, we aim to make synthetic biology and data analysis more approachable for a diverse audience.