Difference between revisions of "Team:SJTU-Software/project"

As we know, there are more than 20,000 biobricks in iGEM official standard database and the number of biobrick keeps increasing every year. Based on the fact that sequencing technology gave birth to bioinformatics, we assumed that with the explosive increase of biobricks, it will be harder for synthetic biologists to manually find good biobricks which meets the requirements when they are trying to create new devices with existing parts. This issue will inevitably lead to the birth of softwares and databases especially related to synthetic biology and these intelligent tools will further promote the rapid development of synthetic biology. So we integrated biobricks data before September from the iGEM official standard database and then developed a visual online device-designing system for synthetic biology researchers.
Meanwhile, in order to facilitate the researchers to look for better biobricks, we combined the search function and scoring system from the 2014 SJTU Software’s EasyBBK with our own system. In this way, users can find biobricks in line with their requirements more quickly.

Introduction
Our software, BASE, has four functions: search, recommendation, evaluation and upload. Via search function, users can search for parts or devices using IDs or features as keywords. In recommendation interface, users can draw their devices. They can also give some keywords when drag an icon to the chain to get a list of parts which fit the require and other parts best. When using evaluation, users firstly enter a device that they designed, then our software can give advice for each part to improve their devices.Finally, users can upload their device to the IGEM part registry and BASE’s database. For the first three functions, we develop a set of scoring system to evaluate the effectiveness and ease of use of the parts and devices.

Method
We get the data of parts from IGEM part registry. A total of 14971 bio-bricks are recorded in the database. Then we divide them into two groups, parts and devices, according to whether the biobrick has subparts. Among them, ??? are parts and ??? are devices. For each bio-brick, there’re four different websites: http://parts.igem.org/cgi/xml/part.cgi?part=BBa_??? http://parts.igem.org/cgi/partsdb/part_info.cgi?part_name=BBa_??? http://parts.igem.org/partsdb/get_part.cgi?part=BBa_???  http://parts.igem.org/Part:BBa_???:Experience When collecting data, we simply replace the ??? with the bricks’ ID. We then extract information from the websites. The information include Part_status, Sample_status, Part_results, Uses, DNA_status, Qualitative_experience, Group_favorite, Star_rating, Del, Groups, Number_comments, Ave_rating. And we take most of the above factors into account when scoring bio-bricks. As for optimizing the weight of these factors, we firstly analyze the distribution of value of the factors to choose the factors that can distinguish the parts most effectively. Then we select 40 parts and 40 devices as the training sets. Finally we get the weight by combining results of several methods.

Results
1.scores for different values of factors To build a scoring system, we start at giving scores to the values of these factors. With the help of wet lab researchers, we rank the values of discrete type according to their effect on researches, and choose a relatively good method to transform successive values into values between 0 and 1. For discrete values, we have a scoring table as below.

And the optimized weight of the factors are shown in the table below.
Table2:

2.devices scoring method with relationship between parts This method is mainly used in the evaluation function. For a device which is just designed by users, the score we get through the first method actually mean nothing, as there’re no information for the device on the registry. So we need to develop a new evaluation system based on its composing parts and relationships between the parts. When evaluating the relationships between parts, we take several factors into consideration, such as the frequency and the average score when the parts are used together and so on. Firstly the weight of the two aspects is confirmed. The default ratio is 65% for the parts and 35% for the relationships. In the first aspect, the weight of different types is dynamic. It’s influenced by the number and type of the parts. However it still shows the different significance of the parts. But in the second aspect, all relationships share the same weight. Then the scoring begins. Given that wet lab researchers care more about the outcome of a device, we search for functional coding parts in the device, and optimize it in the first place. After the user locks the functional parts, we start to optimize other parts. The order is decided according to their type and location in the device. Since there’re two scoring system for devices, the weight in the second one is adjusted to make the scores made by different method close so that scores for new devices can have the comparability with those already in the database. 3.Adding parts one by one This method is mainly used in recommendation function. It’s similar to the second one, but it only cares about the new adding part and relationships when doing the recommendations. The weight’s also adjusted to fit the other two method.

Reference

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