KR101494864B1 - Proteomics service system using cloud computing and the method for the same - Google Patents

Abstract

The present invention provides a system for providing a proteomics service based on a cloud computing network that provides a proteomics service in response to a request from a client, the system comprising: a workflow definition module for inputting a sequence of one or more programs to be performed by a user; An input / output information conversion module for converting information of sample data in an input file and an output file format according to the at least one program; And a job script generation module for generating a script that can be submitted to the job scheduler module for each program in order to execute the one or more programs.

Description

TECHNICAL FIELD [0001] The present invention relates to a system for providing a proteomics service based on a cloud computing network,

The present invention relates to a system for providing a proteomics service based on a cloud computing network and a method for providing the service.

In the case of computational problems in computation of bio-field and mechanical engineering, one or more application programs are sequentially executed to obtain the final result.

Currently, many of these programs process a single program for processing interlocked operations, process the output manually, and then work on the next program.

Proteomics-related tasks involve manual tasks that process a large number of files into various programs, from obtaining experimental results to obtaining the results of the analysis. These tasks involve input file conversion for various programs with different input file formats, interworking of programs to be executed step by step, and the like.

SUMMARY OF THE INVENTION The present invention has been made in order to solve the above problems, and it is an object of the present invention to eliminate a manual operation occurring in an interlocking operation and automate a complicated operation process.

Due to the nature of biotechnology research, the proteomics method is characterized by the use of an incomplete database to select a pool of possible outcomes and then to re-enter the next step in performing the biotechnology research method,

The present invention needs to provide a UI capable of simultaneously analyzing various search algorithms, comparing and analyzing results, and selecting desired analysis results,

Because of the diversity of biotechnology research areas that use proteomics services, biotechnology researchers can make various workflows based on sample data,

Various search algorithms and database usage are automatically updated on the service,

When biotechnology users perform the analysis they want, it can be solved in the portal service without investing in their own servers.

A system for providing a proteomics service based on a cloud computing network that provides a proteomics service in response to a request from a client according to the present invention includes a workflow definition module for inputting a sequence of one or more programs to be performed by a user; An input / output information conversion module for converting information of sample data in an input file and an output file format according to the at least one program; And a job script generation module for generating a script that can be submitted to the job scheduler module for each program in order to execute the one or more programs.

According to the present invention, the execution scheduler module selects cloud members according to the script, converts the sample data into a format supported by the cloud members, and transmits the sample data and the search start command.

According to the present invention, the workflow definition module is specified in a graphical user interface (GUI).

According to the present invention, the program is present on a predefined catalog or can be newly registered.

According to another aspect of the present invention, there is provided an integrated server for transmitting and storing search results from the cloud members according to a search start command of the execution scheduler module.

According to the present invention, an analysis result is generated by analyzing a search result stored in the integration server, and a report is generated and restored.

According to the present invention, the generated analysis result and report are accessible on a user interface (UI).

According to the present invention, a user login module for user application and authentication on the user interface (UI) is included.

According to the present invention, an upload module for uploading various kinds of sample data on the user interface (UI) is included.

According to the present invention, the program is characterized by being a proteomics database search algorithm OMSSA or X! Tandem.

According to the present invention, a cloud infrastructure interworking module for enabling a virtual machine (VM) image to which the proteomics database search algorithm is applied can be driven.

According to the present invention, the execution scheduler module manages the task distribution and resources between the VMs, implements the performance monitoring and task time prediction algorithms of the VMs, and implements a task redistribution algorithm between the VMs to reduce latency .

A method for providing a proteomics service based on a cloud computing network providing a proteomics service according to the present invention includes defining a sequence of one or more programs to be executed by a client in order to provide a proteomics service in response to a request from a client; An input / output information conversion step of converting information of sample data into an input file and an output file format according to one or more programs; And a job script generation step of generating a script that can be submitted to the execution scheduler module for each program in order to execute one or more programs.

The execution scheduler module according to the present invention selects cloud members according to a script and converts the sample data into a format supported by cloud members, and transmits the sample data and the search start command.

The workflow definition step according to the present invention is characterized in that it is specified in a graphical user interface (GUI).

The program according to the present invention is present on a predefined catalog or can be newly registered.

According to another aspect of the present invention, there is provided a computer readable recording medium storing a program for causing a computer to execute the steps of: receiving a search start command from an execution scheduler module;

According to the present invention, an analysis result generation step of analyzing a search result stored in the integration server, generating an analysis result, and generating and restoring a report is provided.

According to the present invention, the generated analysis result and report are accessible on a user interface (UI).

A method of providing a proteomics service based on a cloud computing network that provides a proteomics service according to the present invention includes a user login step for user application and authentication on a user interface (UI) on the web after accessing a service providing portal on the web, And an upload step of uploading various kinds of the sample data on a user interface (UI).

The program according to the present invention is characterized by being a proteomics database search algorithm OMSSA or X! Tandem.

According to the present invention, a virtual machine (VM) image to which a proteomics database search algorithm is applied can be driven by a cloud infrastructure interworking module.

The execution scheduler module according to the present invention is characterized in that it manages work distribution and resources among VMs, implements VM performance monitoring and work time predicting algorithms, and implements a work redistribution algorithm between the VMs to reduce waiting time.

Proteomics programs such as OMSSA and X! Tandem, which are based on open software, are ported to the cloud computing infrastructure to enable work in the cloud infrastructure without restrictions on computing resources. This provides a variety of algorithms, a fast processing speed of a single search, a high throughput for a plurality of searches, and scalability due to an increase in users, without the need to purchase expensive IT resources .

In addition, to increase accessibility of non-IT proteomics researchers, a workflow engine that submits work through a portal providing a convenient UI and eliminates simple and repetitive tasks is provided, and various cloud service farms are scheduled to be linked, It is possible to check the user submitted work quickly in the portal.

In addition, the present invention can select various search algorithms through an easy UI, has no difficulty in using various commands according to different data formats, and can be used only by logging in without installing a search algorithm.

In addition, the present invention can use various search algorithms through a consistent workflow, and can compare and analyze results through various search algorithms.

Further, the present invention is automatically updated when a search algorithm is changed in a cloud environment, and is automatically updated even when DNA information is changed.

In addition, the present invention provides a cloud provisioning according to a variable resource usage by shortening a search time by performing a conventional disadvantage that a search time is long in a single server environment under a cloud environment.

In addition, the present invention improves the viewer speed through the console or ActiveX, even though the size of the search result data is more than tens MB, and the data result on the web can be seen quickly.

1 is a conceptual diagram of a system for providing a proteomics service based on a cloud computing network according to the present invention.
FIG. 2 shows a structure of a cloud computing network-based proteomics service and a location of a cloud infrastructure interworking module according to the present invention.
3 is a structure of an interlocking module program.
4 shows a design result of executing a workflow module in a system 1 for providing a proteomics service based on a cloud computing network according to the present invention.
5 shows an analysis procedure using X! Tandem.
6 is a screen in which X! Tandem is performed in the virtual server.
7 shows the usage status of X! Tandem resources in the virtual server.
Figure 8 shows the transformed data using Tandem2XML
Fig. 9 shows a setting screen for peptide analysis.
Fig. 10 is a pentide analysis completed screen.
Figure 11 shows the analyzed peptide information.
12 is a protein analysis setting screen.
13 is a screen for setting a threshold value in the analyzed protein result.
Fig. 14 is a screen showing the final analysis result of the protein.
15 is a flowchart illustrating a method of providing a proteomics service based on a cloud computing network for providing a proteomics service according to the present invention.

Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings. First, it should be noted that the same components or parts among the drawings denote the same reference numerals whenever possible. In the following description of the present invention, a detailed description of known functions and configurations incorporated herein will be omitted so as not to obscure the subject matter of the present invention.

1 is a conceptual diagram of a system for providing a proteomics service based on a cloud computing network according to the present invention.

1, a system 1 for providing a proteomics service based on a cloud computing network is a system 1 for providing a proteomics service based on a cloud computing network that provides a proteomics service in response to a request from a client 100, A user such as a university researcher, a biotechnology research institute, a pharmaceutical / R & D company accesses the portal to use the proteomics service providing system provided on the web via the client 100 and accesses the user login module 200 on a user interface (UI) And an upload module 300 for uploading various types of sample data to be analyzed through the web, through a user application and a user authentication procedure.

In order to shorten the uploading time, various compression techniques can be applied. The uploaded sample data is stored and managed in the database of the integration server 2, and sample data is distributed to the cloud member 3 as 1 to n virtual servers .

In the state where the sample data is uploaded, the user inputs a sequence of one or more programs to be performed in the workflow definition module 400 on the user interface (UI).

A graphical user interface (GUI) is preferably used for user input convenience in the workflow definition module 400.

Here, the program refers to a proteomic database search algorithm, and the proteomic database search algorithm includes OMSSA or X! Tandem, and one or several search algorithms can be selected according to the search method.

The order of the programs to be executed in the workflow definition module 400 is specified, and the programs may be present in a predefined catalog or newly registered. The program information includes an input / output file format of the programs.

The input / output information conversion module 500 converts the format of the input / output file into a file format recognizable by each program, since the input / output file format differs from one program to another.

The execution scheduler module 700 selects a plurality of cloud members 3 which are previously registered calculation resources in accordance with the program order specified in the workflow definition module 400 and transmits the sample data to the selected cloud member 3, After transmitting the sample data, the search start command is transmitted. In order to transmit the search start command, job scripts, which are a set of instructions, are generated for each program stored in the plurality of cloud members 3, and the job scripts are generated by the job scripts generating module 600.

That is, the script generated by the execution script generation module 600 is transmitted to the execution scheduler module 700, the execution scheduler module 700 selects the cloud member 3 according to the script, and transmits the sample data to perform the search .

In the execution script generation module 600, a final single script is generated by merging the scripts generated for each program.

Each cloud member (3) has a different search algorithm, and it is easy to compare and analyze the results through various search algorithms.

The results retrieved from the cloud members 3 are transmitted to the integration server 2 and stored in the database DB on the integration server 2. [

The search result stored in the integration server 2 is analyzed by using an analysis tool to generate an analysis result. Various reports are generated according to a user's request and transmitted to a mail designated by the user, or an integrated server 2).

Here, a tool such as a TPP (trans proteomic pipeline) can be used as an analysis tool, and protein analysis can be performed using the search result by this analysis tool.

The generated analysis results and reports are accessible on a user interface (UI).

FIG. 2 shows a structure of a cloud computing network-based proteomics service and a location of a cloud infrastructure interworking module according to the present invention.

2, a system 1 for providing a proteomics service based on a cloud computing network according to the present invention is implemented by implanting a proteomics search algorithm into the cloud members 3 and packaging the same, Image registration is supported.

The system 100 for providing a proteomics service based on a cloud computing network includes a cloud infrastructure interworking module 800 for allowing VM images to which the proteomics search algorithm is implanted to be dynamically driven in the system 100 for providing a proteomics service based on the cloud computing network, May be further included.

In the cloud computing network-based proteomics service providing system 1 including the cloud infrastructure interworking module 800, the execution scheduler module 700 functions to distribute tasks and manage resources among VMs, A work time predicting algorithm is implemented, and a work redistribution algorithm between the VMs can be implemented to shorten the waiting time.

The cloud infrastructure interworking module 800 may be used by an external developer or an enterprise to provide various services provided to the cloud computing network-based proteomics service providing system 1, for example, a dynamic VM image management service, a storage service, billing service and an open API for VM image automatic provision service according to a system 1 for providing a proteomics service based on an integrated cloud computing network.

Open API (application programming interface) is a RESTful web service (Web Service) format, so it is easy to integrate with a framework or service that supports HTTP protocol without affecting the terminal. Basically, it controls and manages virtualized server and storage With the ability to use multiple computing resources.

This service is made accessible from an external network, which makes it logically restrictive to access other services internally.

3 is a structure of an interlocking module program.

The Queue shown in FIG. 3 is a module for using JMS, and provides a function of transmitting or receiving data by using the commercial version of ActiveMQ, FUSE Message Broker.

JSON is a module for handling JSON data and array. WADL is a module for WADL generator for documenting. REST Client is a client module for requesting RESTful Web Service to each service. HTTP Service is API Server is an HTTP service module to serve as a web server. Memcached controller is a controller module for using memcached for authentication and Product Concerned is an Open API module related to Billing, Authorization, User, Product and bundle. The module is externally exposed to the external network as a structure that can access the form of Restful Web service using the HTTP protocol.

4 shows a design result of executing a workflow module in a system 1 for providing a proteomics service based on a cloud computing network according to the present invention.

4, the user uploads the sample data after the user logs in through the Web UI, designates the data format and the search algorithm through the workflow definition module 400, and in the cloud platform, the execution scheduler module 700 , It is possible to obtain a quick and accurate search result in cooperation with the cloud members 3 which are virtual servers.

The search result is stored in a database (DB) on the integrated server (2), and the analysis result and report analyzing the stored search result can be confirmed through the Web UI.

If the search algorithm is X! Tandem in the system 1 for providing the proteomics service based on the cloud computing network according to the present invention, the service providing procedure is as follows.

X! Tandem is a software used for protein identification. Protein identification is performed by matching the peptide sequence with the Tandem mass spectrum obtained according to the proteomics method. The output of the XML file lt; / RTI >

The Trans Proteomic Pipeline (TPP) is a solution for peptides and protein analysis with matched results using X! Tandem.

5 shows an analysis procedure using X! Tandem.

The sample data analyzed using the XCalibur mass spectrometer is converted into mzXML data using the ReadW program, the mzXML data is used for matching in X! Tandem, and the matched result is converted into XML data again using Tandem2XML . The converted XML data can be analyzed by protein analysis after peptides are analyzed using TPP to obtain excel data and follow the analysis flow as shown in FIG.

Sample  Data mzXML  Convert to format

The XCalibur sample data is converted into mzXML data through ReadW.exe. Since the sample data format conforms to the manufacturer's format, it can be converted into mzXML data only by using the API provided by the manufacturer. The api provided by the mass spectrometer manufacturer Because it is distributed as a dll, you have to convert the data in Windows environment.

Tandem  How to search

The converted mzXML data is matched with human fasta data in Linux environment.

Human fasta data is in ascii format and should be converted to binary format for fast searching.

The search is performed with X! Tandem using binary data of human type and mzXML data. The default_input.xml file specifying the search condition, the taxonomy.xml file specifying the database, and the XML file specifying the search method And executes X! Tandem using the specified file.

That is, the input / output conversion module 500 consequently converts the format of the input file and the formats of the output file into unified XML or similar form. The file converted to the unified format is automatically re-processed in the format specified by the user.

The input / output file format of the newly registered program is registered by the user in the form of a script designated by the system.

FIG. 6 is a view showing X! Tandem performed in a virtual server, and FIG. 7 is a view showing usage of X! Tandem resources in a virtual server.

As shown in FIG. 7, when X! Tandem is executed, one CPU core occupies 100%, and the memory takes 185M or more. Since the memory usage and the execution time are proportionally increased according to the size of the input data, if a job executed in a single core is performed in a plurality of virtual servers, the execution time can be shortened and the result can be quickly fed back. Based proteomics service providing system 1, the result of shortening the execution time is obtained by using the cloud members 3, which are a plurality of virtual servers.

Figure 8 shows the transformed data using Tandem2XML

Tandem  Results XML  Convert to format

In order to analyze the X! Tandem results, we need to convert them back to XML data. Tandem2XML is a program that performs this conversion. It outputs standard output and can be saved in ascii format.

Fig. 9 shows a setting screen for peptide analysis.

Trans Proteomic Pipeline Using Peptides  analysis

The converted XML file using Tandem2XML can analyze peptides using the xinteract program, and Fig. 9 shows a screen where an XML file can be selected and various parameters can be set.

Fig. 10 is a pentide analysis completed screen.

When Peptide analysis is executed, it is possible to observe the results after a certain period of time has elapsed, and it is possible to obtain the batch processed data by performing on the web.

Figure 11 shows the analyzed peptide information.

The analyzed peptides information can be seen through the web as shown in FIG.

FIG. 12 is a protein analysis setting screen, FIG. 13 is a screen for setting a threshold value from the analyzed protein results, and FIG. 14 is a screen showing a final analysis result of the protein.

Trans Proteomic Pipeline Using Protein  Analyze and save results

After the peptide analysis, the XML data is subjected to the protein analysis process shown in FIGS. 12 and 13 to obtain the final result. In this step, various threshold values can be set according to the sample data, and a step of adjusting the threshold values is carried out while checking the result values.

15 is a flowchart illustrating a method of providing a proteomics service based on a cloud computing network for providing a proteomics service according to the present invention.

As shown in FIG. 15, a method of providing a proteomics service based on a cloud computing network providing a proteomics service according to the present invention includes providing a proteomics service in response to a request from a client, A workflow defining step (S100) of defining a sequence of programs; An input / output information conversion step (S200) of converting the sample data into an input file and an output file format according to one or more programs; And a job script generating step S300 for generating a script that can be submitted to the job scheduler 700 for each of the programs in order to execute one or more programs.

The execution scheduler module 700 according to the present invention is characterized by selecting the cloud members 3 according to a script and converting the data into a format supported by the cloud members 3 and transmitting the sample data and the search start command .

The workflow definition step S100 according to the present invention is characterized in that it is specified in a graphical user interface (GUI).

The program according to the present invention is present on a predefined catalog or can be newly registered.

According to the present invention, a storage step (S400) for transmitting and storing the search result from the cloud members (3) to the integration server (2) according to a search start command of the execution scheduler module .

According to the present invention, an analysis result generation step (S500) of analyzing a search result stored in the integration server (2), generating an analysis result, and generating and restoring a report is provided.

According to the present invention, the generated analysis result and report are accessible on a user interface (UI).

A method for providing a proteomics service based on a cloud computing network providing a proteomics service according to the present invention includes a user login step (S10) for user application and authentication on a user interface (UI) on the web after accessing a service providing portal on the web And an upload step (S20) for uploading various kinds of the sample data on the user interface (UI).

The program according to the present invention is characterized by being a proteomics database search algorithm OMSSA or X! Tandem.

According to the present invention, a virtual machine (VM) image to which a proteomics database search algorithm is applied can be driven by the cloud infrastructure interworking module 800.

The execution scheduler module 700 according to the present invention is characterized in that it performs task distribution and resource management functions among VMs, implements VM performance monitoring and work time predicting algorithms, and implements a task redistribution algorithm between VMs .

While the present invention has been particularly shown and described with reference to specific embodiments thereof, it will be understood by those skilled in the art that various changes, modifications, and variations can be made therein without departing from the spirit and scope of the invention as defined by the appended claims. Anyone with the knowledge of this will know easily.

1: Proteomics service providing system 2: Integrated server
3: Cloud member 100: Client
200: user login module 300: upload module
400: workflow definition module 500: input / output information conversion module
600: Execution Script Generation Module 700: Execution Scheduler Module
800: Cloud infrastructure interworking module
S10: user login step S20: upload step
S100: Workflow definition step S200: Input / output information conversion step
S300: Creation of job scripts
S400: storing step S500: analysis result generating step

Claims (24)

1. A system for providing a proteomics service based on a cloud computing network in response to a request from a client,
A workflow definition module for inputting a sequence of one or more programs to be performed by a user;
An input / output information conversion module for converting information of sample data in an input file and an output file format according to the at least one program; And
And a job script generation module for generating a script that can be submitted to a job scheduler module for each program to execute the one or more programs,
Wherein the execution scheduler module selects cloud members according to the script and converts the sample data in a format supported by the cloud members to transmit the sample data and the search start command. system. delete The method according to claim 1,
Wherein the workflow definition module is specified in a graphical user interface (GUI).
The method of claim 3,
Wherein the program is present on a predefined catalog or can be newly registered. ≪ Desc / Clms Page number 13 > 5. The method of claim 4,
And an integration server for transmitting and storing the search result from the cloud members according to a search start command of the execution scheduler module. 6. The method of claim 5,
Analyzing search results stored in the integration server to generate analysis results, and generating and re-storing the generated reports; and a system for providing the proteomics service based on the cloud computing network. The method according to claim 6,
Wherein the generated analysis results and reports are accessible on a user interface (UI). 8. The method of claim 7,
And a user login module for user application and authentication on the user interface (UI). The method of claim 8, wherein
And an upload module for uploading various types of sample data on the user interface (UI). 10. The method of claim 9,
Wherein the program is a proteomics database search algorithm OMSSA or X! Tandem. 11. The method of claim 10,
And a cloud infrastructure interworking module for enabling a VM (virtual machine) image to which the proteomics database search algorithm is applied to be driven. 12. The method of claim 11,
The execution scheduler module manages work distribution and resources among the VMs,
Implementing the VM performance monitoring and work time prediction algorithm,
And a work redistribution algorithm between the VMs is implemented to reduce the waiting time.
A method for providing a proteomics service based on a cloud computing network in response to a request from a client,
A workflow definition step of defining an order of one or more programs to be executed by the client;
An input / output information conversion step of converting information of sample data in an input file and an output file format according to the at least one program; And
And generating job scripts that can be submitted to an execution scheduler for each of the programs to execute the one or more programs,
Wherein the execution scheduler module selects cloud members according to the script and converts the sample data in a format supported by the cloud members to transmit the sample data and the search start command. Way. delete 14. The method of claim 13,
Wherein the workflow definition step is specified in a graphical user interface (GUI). 16. The method of claim 15,
Wherein the program is present on a predefined catalog or can be newly registered. ≪ Desc / Clms Page number 20 > 17. The method of claim 16,
And storing the result of the search in the cloud members according to a search start command of the execution scheduler module to the integration server and storing the result. 18. The method of claim 17,
Generating an analysis result by analyzing a search result stored in the integration server, and generating an analysis result and re-storing the report; and generating an analysis result based on the analysis result. 19. The method of claim 18,
Wherein the generated analysis results and reports are accessible on a user interface (UI). 20. The method of claim 19,
And a user login step for user application and authentication on the user interface (UI). The method of claim 20, wherein
And uploading various types of the sample data on the user interface (UI). 22. The method of claim 21,
Wherein the program is a proteomics database search algorithm OMSSA or X! Tandem. 23. The method of claim 22,
Wherein a virtual machine (VM) image to which the proteomics database search algorithm is applied is driven by a cloud infrastructure interworking module. 24. The method of claim 23,
The execution scheduler module manages work distribution and resources among the VMs,
Implementing the VM performance monitoring and work time prediction algorithm,
And implementing a work redistribution algorithm between the VMs to reduce the latency time.

Images