Bioinformatics high-performance information management system and data processing method

Abstract

The invention relates to a bioinformatics high-performance information management system and a data processing method. The bioinformatics high-performance information management system includes a server module; the server module is electrically connected to an information acquisition module and a cloud computing module through an Internet network; the server module is connected to a budgeting module; a sample allocation unit, a sample detection unit, a device management unit and a marking unit are arranged in the information acquisition module; the marking unit is connected to the sample allocation unit; the sample allocation unit is connected to the sample detection unit; operation software is arranged in the server module; the operation software includes an man-machine interactive interface; a data updating unit is arranged in the man-machine interactive interface; a user management unit is arranged on one side of the data updating unit; a synchronization zone unit is arranged on oneside of the user management unit; an induction classification unit is arranged on one side of the synchronization zone unit; and a preview unit is arranged under the induction classification unit. The bioinformatics high-performance information management system and the data processing method can achieve classification management and computing of bioinformatics big data, and can improve the working efficiency.

Description

technical field [0001] The invention relates to a bioinformatics high-performance information management system and a data processing method, belonging to the technical field of computer network software. Background technique [0002] With the rapid development of experimental technology in the field of life sciences, the level of automation and intelligence of scientific research instruments is increasing day by day, and the data output capability has developed by leaps and bounds. Higher requirements and usage standards are put forward in terms of processing speed and data accuracy of analysis projects, and the information produced by biological laboratory research has shown explosive growth. In traditional biological laboratories, due to the variety of data types and inconsistent formats, It is difficult to save data, and it is inconvenient to exchange, query, analyze, and maintain, which seriously hinders the information exchange and data comparison between researchers. ...

AI Technical Summary

Problems solved by technology

[0002] With the rapid development of experimental technology in the field of life sciences, the level of automation and intelligence of scientific research instruments is increasing day by day, and the data output capability has developed by leaps and bounds. Higher requirements and usage standards are put forward in terms of processing speed and data accuracy of analysis projects, and the information produced by biological laboratory research has shown explosive growth. In traditional biological laboratories, due to the variety of data types and inconsistent formats, It is difficult to save data, and it is inconvenient to exchange, query, analyze, and maintain, which seriously hinders the information exchange and data comparison between researchers. In the sequencing and high-performance computing links in bioinformatics, professional sequencing laboratories need to carry out Processing a large number of gene sequencing orders, arranging sequencing experiments, and processing high-speed sequencing results in a timely manner. At present, the growing demand for sequencing and high-speed output data are very difficult to manage using only computer file systems, and the progress of various projects is not uniform. It is difficult to deal with, and it is difficult to grasp the overall progress from a macro perspective. Sequencing experiments generally use spreadsheets and paper documents to save databases. However, these large amounts of scattered data are difficult to manage centrally, and users face challenges in data retrieval and progress query. Therefore, it is necessary to establish a standardized The software management system can not only save relevant experimental data, but also integrate indexing, display, integration, analysis, and packaging into one, and manage different experimental samples and experimental results.

Images