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WO2013035904A1 - Système et procédé de traitement de pipeline d'analyse d'informations biométriques - Google Patents

Système et procédé de traitement de pipeline d'analyse d'informations biométriques Download PDF

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Publication number
WO2013035904A1
WO2013035904A1 PCT/KR2011/006686 KR2011006686W WO2013035904A1 WO 2013035904 A1 WO2013035904 A1 WO 2013035904A1 KR 2011006686 W KR2011006686 W KR 2011006686W WO 2013035904 A1 WO2013035904 A1 WO 2013035904A1
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Prior art keywords
analysis
information
image
pipeline
task
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Korean (ko)
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한영만
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Korea Institute of Science and Technology KIST
Korea Institute of Science and Technology Information KISTI
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Korea Institute of Science and Technology KIST
Korea Institute of Science and Technology Information KISTI
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Priority to PCT/KR2011/006686 priority Critical patent/WO2013035904A1/fr
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    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B50/00—ICT programming tools or database systems specially adapted for bioinformatics
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B50/00—ICT programming tools or database systems specially adapted for bioinformatics
    • G16B50/30—Data warehousing; Computing architectures

Definitions

  • the present invention relates to a bioinformation analysis pipeline processing system capable of generating a bioinformation analysis pipeline and processing the same according to a bioinformation analysis method desired by a user, and a processing method thereof.
  • bioinformatics can give potentially meaningful answers to questions such as what parts of DNA control the various chemistries of life, what are the functions of new proteins, and can predict the structure of new proteins? have.
  • the fields of biology in which bioinformatics is currently applied include all fields of molecular biology, such as genomics, transcriptome, proteomics, metabolism, and pharmacogenomics.
  • Cloud computing services related to conventional bioinformation analysis are limited to providing a specific analysis tool or analysis pipeline or data storage device such as Amazon EC2 service. Therefore, there is a problem that each user cannot supply the desired analysis pipeline smoothly, and there is a problem that the user is difficult to use because the information on the analysis pipeline stored in each cloud service device is insufficient. In addition, there is a problem that can not fundamentally address the concern about the security of important research data distributed in each cloud service device.
  • the present invention is to solve the above problems, the technical problem to be achieved by the present invention in the life information analysis pipeline processing system and method, without suffering the above-mentioned problems, life information analysis pipeline in the cloud computing base To make it workable.
  • the life information analysis pipeline processing system is a cloud for installing a virtual machine (VM) image including an analysis task for analyzing the life information data on the cloud service device Resource supply apparatus; Receive meta information about the life analysis pipeline and VM image from the client, extract Identifier (ID) information of the VM image mapped with the meta information, and perform the analysis operation sequentially according to the execution position of the analysis operation in the VM image.
  • a server that directs and executes an analysis pipeline in synchronization with a cloud resource supply device and synchronizes job status and results; And a database that stores meta information and ID information of a mapped VM image.
  • the client writes meta information about the pipeline and the VM image, and instructs the server to generate an object model.
  • the VM image includes at least one analysis task for analyzing the life information, is installed in the computing service device included in the cloud service device analysis work It characterized in that to perform.
  • the pipeline in the life information analysis pipeline processing system converts at least one or more analysis tasks for analyzing life information, links for linking each analysis task, and the format of input / output data. It characterized in that it comprises a data conversion script.
  • life information in the life information analysis pipeline processing system is characterized in that it comprises at least one of sequence data, sequence alignment data, protein structure or molecular interaction network.
  • the meta information represents a connection relationship between analysis tasks included in the pipeline and a VM image executing the analysis task.
  • the meta information may be selected from among the structure information of the pipeline, input / output data conversion script information, execution location information of an analysis operation in a VM image, or VM resource allocation information. It characterized in that it comprises at least one.
  • the server extracts meta information from a database and generates a VM package by extracting a VM image mapped thereto through a cloud resource supply device. It is done.
  • the server receives the VM package from the client, and stores the meta information contained therein in a database, the cloud resource supply device cloud the VM image Instructing the service device to install.
  • the life information analysis pipeline processing method comprises the steps of installing a virtual machine (VM) image including an analysis operation for analyzing life information data on the cloud service device ; Receiving meta information about the life information analysis pipeline and the VM image; Extracting Identifier (ID) information of the VM image mapped with the meta information; Instructing an analysis task sequentially according to an execution position of the analysis task in the VM image; Running an analysis pipeline and synchronizing the work state and outputs; And storing the meta information and the ID information of the mapped VM image.
  • VM virtual machine
  • ID Identifier
  • life information analysis pipeline processing method further includes the step of instructing to create the meta-information about the pipeline and the VM image and generate the object model.
  • the VM image at least one analysis task for analyzing the life information, is installed in the computing service device included in the cloud service device analysis operation It characterized in that to perform.
  • the pipeline converts at least one or more analysis tasks for analyzing life information, links for linking each analysis task, and a format of input / output data. It characterized in that it comprises a data conversion script.
  • the life information includes at least one of sequence data, sequence alignment data, protein structure, or molecular interaction network.
  • the meta information represents a connection relationship between analysis tasks included in the pipeline and a VM image executing the analysis task.
  • the meta information may be selected from among the structure information of the pipeline, input / output data conversion script information, execution location information of an analysis operation in a VM image, or VM resource allocation information. It characterized in that it comprises at least one.
  • life information analysis pipeline processing method further comprises the step of extracting the meta information and the VM image to generate a VM package.
  • the life information analysis pipeline processing method further includes the step of receiving a VM package, storing meta information contained therein, and instructing to install the VM image on the cloud service device.
  • backup and reinstallation to a cloud computing environment may be facilitated.
  • FIG. 1 is a diagram illustrating a life information analysis tool object-oriented class diagram according to an embodiment of the present invention.
  • FIG. 2 is a diagram illustrating a class diagram of a life information analysis pipeline object model according to an embodiment of the present invention.
  • FIG. 3 is a diagram illustrating a general life information analysis pipeline model according to an embodiment of the present invention.
  • FIG. 4 is a diagram illustrating a life information analysis pipeline processing system according to an embodiment of the present invention.
  • FIG. 5 is a flowchart illustrating a life information analysis pipeline processing method according to an embodiment of the present invention.
  • FIG. 6 is a flowchart illustrating a VM package export method in the life information analysis pipeline processing method according to an embodiment of the present invention.
  • FIG. 7 is a flowchart illustrating a VM package importing method of the life information analysis pipeline processing method according to an embodiment of the present invention.
  • the present invention addresses the bioinformation analysis pipeline that integrates and analyzes various and heterogeneous bioinformation.
  • the bioinformation pipeline includes a plurality of analysis tools that receive bioinformation data, perform analysis to output the result data, links linking the plurality of analysis tools, and / or result data output from each analysis tool.
  • Data conversion scripts are included to convert the life information data types to corresponding analysis tools. Since the above analysis tool refers to an entity executing a task of analyzing life information data, the following analysis tool and analysis work may be used in the same sense.
  • bioinformatics tools vary in their purpose and practice, they need to be effectively integrated.
  • One thing to consider to effectively integrate bioinformation analysis tools is that there are many ways to implement even analytical tools that do the same.
  • an analysis tool that obtains a single sequence of information from a genetic database may have various execution methods such as command line execution, scripting, and remote invocation methods such as web services, XML-RPC, and REST. Therefore, to integrate effective bioinformatics analysis tools, a flexible integration schema must be implemented considering these various execution methods.
  • life information analysis requires a large amount of computing, so it cannot be analyzed with a personal computer, and it is appropriate to use a super computer or a cloud service device capable of distributed computing. Therefore, the present invention proposes a device and method for analyzing life information using a cloud service device.
  • FIG. 1 is a diagram illustrating a life information analysis tool object-oriented class diagram according to an embodiment of the present invention.
  • the present invention is a subclass of the analysis tool (BioTool, 1010) class for each execution method of the analysis tool, command line based execution method (CommandLineBioTool, 1011), web service execution method (WebServicesBioTool, 1012), script-based execution method (ScriptBioTool). 1013) is defined.
  • ConandLineBioTool 1011
  • WebServicesBioTool web service execution method
  • ScriptBioTool script-based execution method
  • the analysis tools run in different ways, so integrating them requires scalability and flexibility.
  • the present invention may generate an analysis tool using XML (eXtensible Markup Language).
  • XML eXtensible Markup Language
  • XML has advantages such as portability, reusability, extensibility, and efficient data exchange, and thus is suitable for integrating various life information analysis tools in the present invention to form a life information analysis pipeline.
  • the life information analysis pipeline according to the present invention is used as an input of another analysis tool in which a plurality of analysis tools are interconnected by a link and the output of one analysis tool is linked. Therefore, semantic-based hierarchical data schema can be defined in order to be freely linked between analysis tools having heterogeneous input / output data formats.
  • OWL Ontology Web Language
  • the input and output data of most bioinformatics analysis tools are in the form of strings, semantically diverse and can be classified hierarchically. Therefore, OWL can be used to define the input and output data format of the bioinformation analysis tool.
  • Input and output data formats may include strings, numbers, Boolean values, as well as bioinformation data such as sequence data, sequence alignment data, protein structures, molecular interaction networks, and the like. Whether or not the two analysis tools can be linked to I / O is determined by determining whether the form class for the output data is a subclass of the form class for the input data in the OWL data schema.
  • the analysis tool is XML and the input / output data is OWL.
  • the flexibility and extensibility of the input / output data linkage between the analysis tools can be obtained for each analysis tool.
  • One life information analysis pipeline 2010 may include a plurality of analysis tool execution tasks 2020 in a lower layer.
  • Each analysis tool execution task 2020 may include a plurality of input data 2030 and output data 2040 in a lower layer.
  • each of the input data 2030 and the output data 2040 includes a plurality of links 2050 in a lower layer, and the links 2050 correspond one-to-one with the input / output data conversion script 2060.
  • Each analysis tool execution task 2020 above is executed in each analysis tool and may be referred to as an analysis tool below.
  • the life information analysis pipeline 2010 may include a plurality of analysis tools 2020 that perform analysis work therein, and each analysis tool may include various types of data as input / output data 2030 and 2040. have.
  • each link 2050 linking the analysis tools 2020 a plurality of links 2050 may be linked to one analysis tool, and each link 2050 converts the data when the data formats are different between the analysis tools. May involve data conversion script 2060.
  • One bioinformatics analysis pipeline is represented as a directed acyclic graph with the main task as the peak of the BioTask corresponding to the unit analysis task that processes the specific task using the input data, and the link that links the input / output between the BioTasks. Can be.
  • FIG. 3 is a diagram illustrating a general life information analysis pipeline model according to an embodiment of the present invention.
  • BioTask 1 3010 is the initial performance analysis task and corresponds to the highest vertex.
  • BioTask 1 (3010) has BioTask 2 (3020), BioTask 3 (3030), BioTask 4 (3040), BioTask 5 (3050) as child tasks, and BioTask 1 (3010) becomes parent task to them.
  • a task executes in parallel when all of its parent tasks are terminated and its output is set appropriately for the input of that task. That is, in the case of BioTask 6 (3060), both BioTask 3 (3030) and BioTask 4 (3040), which are parent tasks, are terminated, and their output values are transmitted to BioTask 6 (3060) for execution.
  • BioTask 3 (3030) or BioTask 4 (3040) is not suitable as the input data of BioTask 6 (3060)
  • the data conversion script accompanying the link is converted into a suitable input data format and the BioTask 6 (3060) is used. ) Is entered.
  • the present invention proposes a life information analysis pipeline system using a cloud service device.
  • FIG. 4 is a diagram illustrating a life information analysis pipeline processing system according to an embodiment of the present invention.
  • the life information analysis pipeline processing system may include a client 4010, a server 4020, a cloud service device 4030, and / or a database 4040.
  • the client 4010 may include a pipeline designer 4011, an execution status monitor 4012, and / or a data uploader 4013.
  • the pipeline execution server 4020 includes a request processing unit 4021, a pipeline VM package handler 4022, a DB object accessor 4023, a pipeline execution scheduler 4024, a job execution unit 4025, and a cloud resource supply device. 4026, object-relation mapper (ORM) engine 4027, and / or pipeline VM package 4028.
  • the cloud resource supply device 4026 and / or the ORM engine 4027 may exist independently of the pipeline execution server 4020.
  • the cloud service device 4030 may include a storage service device 4031 and / or a computing service device 4032.
  • the life information analysis pipeline processing system may generate a pipeline VM package and use the same to provide analysis of life information in a cloud service device.
  • the client 4010 may generate a pipeline model for analyzing life information to be analyzed.
  • the pipeline designer 4011 may create meta information of the pipeline used for life information analysis, generate an object model of the server 4020, and store it in the database 4040.
  • the meta information may indicate a connection relationship between the analysis tasks included in the pipeline and the VM image that executes the analysis tasks.
  • the meta information may include information necessary for the server 4020 to execute a pipeline for analyzing life information, and may include information required for pipeline computing using the cloud service device 4030.
  • the meta information may include at least one of pipeline structure information, input / output data conversion script information, execution location information of each analysis tool in the VM, or VM resource allocation information.
  • the client may monitor the execution status of the pipeline at the server 4020 using the execution status monitor 4012.
  • the created meta information can be delivered to the server by the data uploader 4013.
  • the server 4020 may receive a request of the client 4010 through the request processor 4021.
  • the connection between the client 4010 and the server 4020 may be based on web services or COMET.
  • the pipeline VM package handler 4022 may extract ID information of a VM image mapped to specific pipeline meta information and store the ID information in the database 4040.
  • the pipeline VM package handler 4022 is involved in the pipeline VM package 4028 import / export operation, which is described again below.
  • DB object accessor 4023 may be used to import specific data from database 4040 to server 4020.
  • the operation of storing the database 4040 is performed through the ORM engine 4027 connected to the server 4020.
  • OR object-relation
  • object-oriented data models expressed in XML can be mapped to relational databases.
  • the components of the pipeline processing system can be implemented by referring to the object-oriented data model of pipeline meta information, and as a result, it is possible to construct the entire system object-oriented.
  • Meta information of a specific pipeline stored above and ID information of a VM image mapped thereto may be used to utilize the cloud service device 4030 when analyzing the corresponding pipeline.
  • the pipeline execution scheduler 4024 When executing life information analysis, the pipeline execution scheduler 4024 requests the cloud resource provider 4026 to create a VM image mapped to the pipeline as a VM instance, and when the VM instance is created, the pipeline meta information
  • Each defined analysis tool can be used to direct sequential analysis tasks.
  • the task executor 4025 may receive such an instruction and generate an analysis tool execution task in a corresponding VM in synchronization with the cloud resource supply device 4026 and synchronize the task state and the result.
  • the above-mentioned analysis work and the result storage are performed by using the cloud service device 4030.
  • the analysis operation is performed by the computing service device 4032 including the VM instance, which may include a VM image manager and a VM instance activated and scheduled to run in memory.
  • Data generated by the analysis operation is made by the storage service device 4031.
  • the storage service device 4031 may include a common data manager, a big data manager, and a distributed file manager.
  • the user using the present invention through the life information analysis work using the cloud service device has the advantage that can quickly and accurately analyze complex and vast life information through large-scale distributed computing.
  • life information analysis pipeline processing system may provide import / export of the life information pipeline VM package 4028.
  • the client 4010 may request the export of the specific pipeline VM package 4028 through the request processor 4021.
  • the pipeline VM package handler 4022 of the server 4020 may extract meta information of the pipeline from the database 4040 and generate the XML.
  • the DB object accessor 4023 is used.
  • the pipeline VM package handler 4022 may generate a VM package 4028 by taking a VM image mapped through the cloud resource supply device 4026 and merging it with meta information.
  • the client 4010 may download the generated pipeline VM package image and export it to a specific local storage space.
  • the user can export the VM package 4028 image of the life information analysis pipeline designed by the user from the server and save it on the personal computer, which can be analyzed again using any other server anytime, anywhere. can do.
  • the client 4010 may upload a specific pipeline VM package image to the server 4020 using the data uploader 4013.
  • the pipeline VM package handler 4022 of the server 4020 extracts the pipeline meta information from the uploaded VM package image and stores it in the database 4040, and the VM image is a cloud service through the cloud resource provider 4026.
  • the computing service device 4032 may be installed in the device 4030.
  • FIG. 5 is a flowchart illustrating a life information analysis pipeline processing method according to an embodiment of the present invention.
  • the cloud resource supply device 4026 assigns an ID to the VM image and installs the VM image in the cloud service device in operation S5010. Each VM image is installed in a computing service device 4032 in a cloud service device 4030.
  • the client 4010 writes meta information of the pipeline through the program designer 4011 and generates an object model of the server 4020 (S5020).
  • the meta information may include at least one of pipeline structure information, input / output data conversion script information, execution location information of each analysis tool in the VM, or VM resource allocation information.
  • the pipeline VM package handler 4022 extracts meta information of a pipeline to be analyzed and ID information of a VM image mapped thereto (S5030).
  • the ID information of the VM image may be used to execute the VM image including the analysis tool when the analysis tools in the pipeline are to be executed. This is because it is necessary to access a specific computing service device 4032 in the cloud service device 4030 and execute a VM image corresponding to a desired pipeline.
  • the server stores the object model and meta information in the database 4040 (S5040).
  • the operation of storing the database 4040 is performed through the ORM engine 4027 connected to the server 4020.
  • OR object-relation
  • object-oriented data models expressed in XML can be mapped to relational databases.
  • the storing step (S5040) is not an essential component in the embodiment of the present invention, it may be excluded according to the embodiment.
  • the pipeline execution scheduler 4024 may request the cloud resource provider 4026 to create a VM instance for the VM image mapped to the pipeline.
  • the analysis operation is instructed sequentially using the execution position in each analysis tool VM defined in the pipeline meta information (S5050).
  • the job executor 4025 executes an analysis tool in the VM in association with the cloud resource supply device 4026 and synchronizes the job status and the result (S5060).
  • FIG. 6 is a flowchart illustrating a VM package export method in the life information analysis pipeline processing method according to an embodiment of the present invention.
  • the client 4010 requests the export of a specific pipeline VM package (S6010).
  • the pipeline VM package handler 4022 included in the server 4020 may extract meta information of the corresponding pipeline from a database and generate the XML.
  • the VM image to be mapped is extracted using the cloud resource supply apparatus (S6020).
  • the pipeline VM package handler 4022 generates the VM package image by merging the extracted meta information and the VM image (S6030).
  • the client 4010 downloads the generated pipeline VM package image and exports it to a specific local storage space (S6040).
  • the user can export the VM package image of the life information analysis pipeline designed by the user from the server and save it on the personal computer. . How to import back to the server is described below.
  • FIG. 7 is a flowchart illustrating a VM package importing method of the life information analysis pipeline processing method according to an embodiment of the present invention.
  • the client 4010 uploads a specific pipeline VM package image to the server 4020 (S7010).
  • the client 4010 may use the data uploader 4013.
  • the pipeline VM package handler extracts the VM image and pipeline meta information from the uploaded VM package image (S7020).
  • the pipeline VM package handler 4022 stores the extracted meta information in the database 4040 (S7030).
  • the storage of meta information is via object-relational mapping via the ORM engine 4027.
  • the VM image is installed in the cloud service device 4030 through the cloud resource supply device 4026 (S7040). At this time, the installed place is the computing service device 4032.
  • the present invention enables the user to secure his / her life information analysis pipeline model through the pipeline VM package import / export operation described above, and execute the analysis task at any time through any server.
  • the present invention may be applied, in whole or in part, to a life information analysis pipeline processing system.

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Abstract

La présente invention porte sur un système de traitement de pipeline d'analyse d'informations biométriques apte à générer un pipeline d'analyse d'informations biométriques conformément à un procédé d'analyse d'informations biométriques souhaité par un utilisateur et de traiter le pipeline d'analyse d'informations biométriques généré, et sur un procédé de traitement correspondant. Un système de traitement de pipeline d'analyse d'informations biométriques selon un mode de réalisation de la présente invention comprend : un fournisseur de ressources en nuage pour installer une image de machine virtuelle (VM), qui comprend une tâche d'analyse pour analyser des données d'informations biométriques, dans un dispositif de service en nuage ; un serveur pour recevoir des méta-informations sur un pipeline d'analyse d'informations biométriques et l'image VM en provenance d'un client, extraire des informations d'identification (ID) de l'image VM qui est mappée aux méta-informations, diriger séquentiellement la tâche d'analyse conformément à une position d'exécution de la tâche d'analyse d'image VM, exécuter un pipeline d'analyse en association avec le fournisseur de ressources en nuage, et synchroniser un état de tâche avec un résultat ; et une base de données pour stocker les méta-informations et les informations ID mappées de l'image VM.
PCT/KR2011/006686 2011-09-08 2011-09-08 Système et procédé de traitement de pipeline d'analyse d'informations biométriques Ceased WO2013035904A1 (fr)

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