DNA rapid detection equipment, since the advent of second-generation sequencing technology, has always been the focus of research and clinical fields. With the technological development of the entire industry, second-generation sequencing has also driven the entire genetic research industry chain. In the second-generation sequencing industry chain, the upstream is used for testing, the midstream for analysis, and the downstream is used for applications. As the price of sequencing continues to fall, the bioinformatics analysis of midstream sequencing data has become the biggest bottleneck in improving efficiency.

　　Traditional sequencing data analysis relies on the performance of the local server. It is foreseeable that the ever-decreasing sequencing price will bring about the generation of more massive sequencing data, and the huge amount of sequencing data will undoubtedly extend the time to obtain sequencing analysis results. At present, a possible better solution is to do it through cloud computing. The advantage of cloud computing lies in the ability to process big data through distributed computing, which greatly improves computing efficiency and reduces costs.

　　At present, Seven Bridge, a foreign cloud computing platform, is relatively mature, and can quickly analyze second-generation sequencing data. The disadvantage is that as a typical pipeline analysis, the requirements for users are relatively high, and there will be some obstacles to the use of domestic user groups. Among the domestic cloud computing platforms, GCBI will release a new whole-genome sequencing analysis platform at the end of February 2016. Although no specific information has been released yet, it hopes to reflect the high efficiency and high availability of basic functions.

　　Next, let's look at the changes and progress that the sequencing cloud computing platform may bring in different fields.

　　Research field

　　Scientific researchers have always been an important user group of sequencing. Due to the continuous reduction of sequencing costs and the selection of more sequencing service providers, it is foreseeable that the yield and scale of sequencing data will increase significantly. And this part of the data must be analyzed. Before there was a large-scale data analysis platform, the efficiency of analysis was limited by the scale of the local server. The larger the amount of data, the longer the analysis time. The sequencing cloud computing platform is expected to break through this bottleneck. The analysis time of 100 samples and 1,000 samples is only similar to the analysis time of 1 sample, which will greatly reduce the time cost of users. It is expected that with the emergence of data analysis platform, the period of scientific research will be greatly shortened.

　　Clinical application field

　　In the traditional diagnosis and treatment mode, clinicians need various examination data and physical examinations to diagnose patients. Once molecular-level testing is carried out in the clinic, the cloud computing platform can collect and quickly analyze the clinical data and molecular testing data of the same disease, and provide corresponding auxiliary diagnosis references for specific patients, and even provide corresponding medication plans. With reasonable application by clinicians, the entire diagnosis process will become faster and more accurate. If the development of diseases in the future evolves into classification based on changes at the molecular level, cloud computing platforms such as GCBI will be more clinically helpful.

　　Personal health

　　With the application of sequencing technology in the medical field, there are already many testing services for personal health on the market. Testing methods include personal whole-genome sequencing, customized gene chips, and so on. The analysis and interpretation of these data will become more and more common as the cost of testing decreases. When everyone will do such a test, the cloud computing platform is expected to provide a feasible solution for the rapid interpretation of this part of the data. Individual users will get their own results reports more quickly.

　　Cooperation model

　　In view of the powerful functions of the bioinformatics cloud computing platform, it is expected to build various cooperation modes between the platform and scientific research units, clinical researchers and even enterprises. The cooperation between scientific research units and cloud platforms can speed up the output of scientific research results. The cloud platform can help scientific research units to transform and apply results; clinical researchers can use the cloud platform to assist in diagnosis, and the cloud platform continuously optimizes the diagnosis model through the input of clinical data ;Enterprises can promote their own products through the cloud platform, and the cloud platform can also provide users with more diverse supplier choices.

　　It is foreseeable that the powerful capabilities of the bio-information cloud computing platform will not only be reflected in its computing power, but also in clinical applications, cooperative transformation and other aspects can show its potential. Let us wait and see the development of cloud computing platforms.