Research On The Framework Of Automated Processing For Differential Expression Analysis Of Transcriptome Based On The Docker

The differential expression analysis is one of the core objectives of transcriptome research,which is of great significance to reveal the gene function and regulation rules.However,the analysis is a multi-step iteration and time-consuming computationally intensive process.There are complex data dependencies between softwares,and input and output formats are not the same.In the traditional way,the complex installation and use of softwares,tedious manual operation and difficult migration of analysis environment are the key problems to be solved.In view of the above problems,the Docker container technology,an open source cloud project,is applied in the field of biological information for the first time and an efficient and automated method for transcriptome differential expression analysis is proposed.The specific work is as follows:(1)This thesis summarizes the research status of automated transcriptome differential expression analysis at home and abroad,and proposed to Docker container technology applied in the field of biological information bold ideas,integrates discrete processing steps to form the best practice for transcriptome differential expression analysis,and then it is embedded and integrated in Docker container and then the combination of multi-script interaction and Web services are adopted to achieve efficient automation of transcriptome differential expression analysis(2)Secondly,this thesis takes the Kubernetes cluster with 6 nodes as the experimental environment,the transcriptome differential expression analysis is automatically processed by the above method and the traditional method for sample analysis and testing,and the accuracy and processing efficiency are verified in many aspects.Experimental results show that compared with the traditional method,the analysis time of this method is reduced by about 69.3%,and the efficiency is improved by about twice,providing more efficient technical support for researchers.(3)In the end,Based on the above methods,a DEA-container framework for automated analysis of transcriptome differences was formed in this thesis,it provides friendly user access interface and the function of automatically generating reports to users,it provides a single data input and output endpointm.Therefore,users only need to upload and submit the analysis sequence without any other operation.After the back-end server processes the analysis,the analysis result file is fed back to the user through the Web interface and provides the user download result file function.In turn,it provides a basis for reliability for further research by biologists.