Team:UESTC-Software/Awards
Gold Medal
Integrated Human Practices
Our human practices were carried out strictly in accordance with the software lifecycle. We have created a spiral research model for the early and middle stages of project development. Major research activities included exchanges with scholars, professors, as well as visits to law firms and biotechnology companies.
In spiral three-round survey, new requirements or suggestions for improvement were received. We enhanced the project through continuous excavation and problem solving step by step. Spiral research did not affect the process of software implementation while continuing to retrospect. We could also keep pace with the updating requirements in time.
After satisfying all the requirements, the test version was handed over to the users to collect feedback. Through the modifications and improvements, the complete final product was obtained. Our activities are helpful in the project promotion, click Human Practicesfor more details.
Model
We established models in the screening BLAST results and the EC prediction functions, which play an important role in our project.
To get more accurate data, we derived a formula to filtrate origin blast results. Considering that the data accuracy directly impact the credibility of our project, Poisson distribution and Normal distribution were used to fit the frequency histogram of formula results. Each coefficient was modified in our formula until it performs well enough. The testing result showed that our new filtrating model has a significant better performance than last year.
Our enzymatic function prediction uses machine learning and constructs multiple binary classifiers. It combines three independent predictors based on subsequences, sequence similarities and amino acid physicochemical features. It can provide probabilistic enzymatic function by predicting the EC number.Click Model for more details.
Demonstrate
We demonstrated and promoted the BioMaster 2.0 to iGEMers in Giant Jamboree. A short guide video was recorded to prove the efficiency of BioMaster, and you can also experience it directly from www.biomaster-uestc.cn. In the early testing of BioMaster, there are many sincere feedback form synthetic biologists and iGEM teams. We also got contacts with official iGEM Registry about technical details on copyright issues. To ensure the safety and legitimacy of data, we consulted lawyers on intellectual property issues. Currently, BioMaster 2.0 is open to the public, providing downloads of databases and docker images.Click Demonstratefor more details.
Sliver Medal
Validated Contribution
We carefully designed our model and test method to achieve the expectations. The final model results and a large number of sample tests proved the effectiveness of the project. The comparison results showed that BioMaster 2.0 had a significant improvement compared with BioMaster 1.0. BioMaster is open to synthetic biologists and the public, serving the synthetic biology community and being recognized by our partners. It was clearly documented on the wiki about how we achieved our goal in Design, Model. Click Contributionfor more details. We recorded the
validation in the late half of the page.
Collaboration
Our project not only aimed at individual users such as iGEMers and synthetic biologists, but also assisted in the design of various synthetic biology softwares. Therefore, in addition to inviting many users to try out our software products and collecting feedbacks, we have collaborated with Tongji-Software and USTC-Software to complete their software products. Our team and UESTC-China jointly hosted the fifth iGEM Exchange in Southwest China. Click
Collaborationfor more details.
Human Practice
To ensure BioMaster is user-friendly enough, we communicated to many scholars, professors and engineers for their opinions on our project. Questionnaires were also released to gather information about users' requirements. 2019 Southwestern iGEM Exchange Conference of China was held by us as a platform for iGEMers to exchange learning. We also teamed up with Sichuan Technology and Science Museum to hold an large science popularization "The Adventure with SynBio". Click Human
Practicesfor more details.
Bronze Medal
Register For IGEM And Attend
We have signed up for the collegiate category as in software track team.
Certainly, we had a great summer for our joint effort and are going to attend the Giant Jamboree.
Deliverables
We have completed Wiki, poster, judging form and prepared a wonderful
presentation for the final competition.
Attribution
Many people have supported our project and offered help. We documented every
effort they made for us. Click
Attributionsfor more details.
Contribution
We have released a BioMaster 2.0 database which integrates iGEM registry and 11 traditional biological research databases. It provides synthetic biologists with more analytical information, more friendly retrieval methods, clearer data pages and long-term sustainable database services. When it comes to the promotion of synthetic biology and iGEM, we launched a board game BioME and a brochure of synthetic biology which is suitable for junior and senior high school students. Click Contributionfor more details.
Special Awards
Best Integrated Human Practices
We carried out human practices in strict accordance with the software lifecycle to
guide our project. Software requirements can be constantly updated with possible backtracking during
development. Therefore, in the design and implementation phase, we used the spiral model to conduct research and
promote the project in parallel with the demand research and coding. In the spiral research, we understand the
software requirements of different groups through questionnaires, interviews with professors, visits to
biological companies and so on. Each round of investigation received new requirements or suggestions for the
project, and gradually improved the project through continuous exploration and problem solving. The test version
obtained after meeting all the requirements was submitted to users for experience again. Then the revised
comments were collected and perfected, and the final version was obtained. Such a model is not only suitable for
our software development, but also for others to implement their project.
Best Education & Public Engagement
Synthetic biology, as an interdisciplinary subject, involves many terms that are
difficult to understand. To make it accessible to the public in a simpler way, we designed two products to aid
understanding. Board game BioME aims to make the public understand the core idea of the bottom-up construction
of synthetic biology, and the "building blocks" approach to the game is more effective in enhancing the interest
of the public with the fun of the game. The popular science brochures are assisted by text, vivid pictures and
well-designed cartoons to help people understand. When organizing activities, we had different positions for
different groups and adapted different methods to increase people’s engagement in synthetic biology and iGEM,
such as interesting science museum activities for kids, science lectures for junior and senior high school
students and experience sharing with Pre-iGEMers. Our brochures and BioME have been well received by the public
and we will consider commercializing and promoting them to more people.
Best Model
We spent a lot of time and energy to construct our models, which played an
important role in both parts of our project. To get more accurate data, we derive a formula to filtrate
origin blast results. For the data accuracy directly impact the credibility of our project, we use Poisson
distribution and normal distribution to fit the frequency histogram of formula results and modify each
coefficient in our formula until it performs well enough. The testing result shows that our new filtrating
model has a significant better performance than last year, and the model can be generalized in big data
processing for iGEM parts to help iGEMers. Additionally, our enzymatic function prediction tool use machine
learning and constructs multiple binary classifiers. It combines three independent predictors based on
subsequences, sequence similarities, and nucleotide physicochemical features to predict probabilistic EC number,
which can provide synthetic biologists with more help on enzymes.