Development of Bio-Cloud Service for Genomic Analysis Based on Virtual

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1 Development of Bio-Cloud Service for Genomic Analysis Based on Virtual Infrastructure 1 Jung-Ho Um, 2 Sang Bae Park, 3 Hoon Choi, 4 Hanmin Jung 1, First Author Korea Institute of Science and Technology Information, jhum@kisti.re.kr 2, Korea Institute of Science and Technology Information, plucky@kisti.re.kr *3, Corresponding Author Korea Institute of Science and Technology Information, choid@kisti.re.kr 4, Korea Institute of Science and Technology Information, jhm@kisti.re.kr Abstract Recently the importance of genomic data analysis is growing to realize the personalized treatment of human cancers. The next generation sequencing (NGS) technique is a cost-effective way to obtain such data sets for cancer data analysis. Because NGS produces data sets of lots of short reads, more computing resources are required to analyze those sets of data. To solve this issue, cloud computing can be considered as a prominent solution in order to elastically manage the requirement of computing resources. In this paper, we propose a bio-cloud service for large-scale NGS data analysis based on virtualized computing infrastructure. It has been developed by collaboration of KISTI and KOBIC to enhance the productivity of KOBIC s genomic data re-sequencing study. Keywords: Next-Generation Sequencing, Cloud Computing, Genomic Analysis, Virtual Cluster Management, NGS Analysis Pipeline 1. Introduction In general, genome sequence data analysis is an essential part of studying the personalized medical treatment of human cancers [1]. Nowadays, the next-generation sequencing (NGS) technique is prevalent in the field of genome analysis study because it is possible for biologists to save the cost and time for generating genome data. Hence, most bioinformatics research groups use the NGS technique to analyze genome sequences [2, 3]. The Korean Bioinformation Center (KOBIC), which is the primary research institute for bioinformatics research on human cancers, constructed a sequence data analysis system which consists of 100 physical computing servers and one storage server. However, the system has a few issues due to the direct use of physical computing resources. First, as the workload increases, latency increases; therefore, job execution time is severely delayed. Second, it is very difficult to enhance the efficiency of computing resources because even a bioinformatics application consists of sequential processing and parallel processing components. Therefore, bioinformatics applications tend to under-utilize physical computing resources. Finally, the existing system only stores the output data from executing applications in a storage server. This means that data management is handled by each user who executes applications. Since the system manages data not from a user s viewpoint but from an application s viewpoint, it requires additional efforts by users to manage their data. These problems cause serious inefficiency in dealing with large-scale data analysis like RNA and DNA re-sequencing. To enhance the productivity of such analysis, we developed the Bio-Cloud system, which is based on virtualized computing resources and is specific to KOBIC s requirements. In this paper, we describe the Bio-Cloud system which can elastically allocate virtualized computing resources to bioinformatics applications on demand. We exploit Xen and OpenNebula to construct a virtual infrastructure management system for Bio-Cloud. 2. Proposed Bio-Cloud System We designed the Bio-Cloud system by analyzing the above problems and developing solutions to resolve them. To reduce job execution time, we constructed a virtual infrastructure management system that can dynamically allocate a virtual cluster to a user s job on demand. To improve the utilization of computing resources, a virtual cluster is allocated on the basis of CPU usage frequency, memory usage Advances in information Sciences and Service Sciences(AISS) Volume4, Number13, July 2012 doi: /AISS.vol4.issue

2 patterns, and characteristics of applications including sequential and parallel processing components. Data management functions are provided to support NGS analysis pipelines and make it easy for cloud users to handle their data. Figure 1. Bio-Cloud system architecture Figure 1 shows the overall system architecture of Bio-Cloud. The system consists of a virtual infrastructure, resource management server and web interface to receive from and respond to KOBIC s requests. Users and their scenarios in our system are described as follows. - End-User: End users execute and monitor their tasks, and upload/download their data by using an App Server provided by the Bio App. manager. - Bio App. Manager (SaaS Provider): The Bio App Manager (KOBIC personnel in our project) is primarily responsible for submitting the request of a virtual cluster to the Resource Manager by using a web interface. The capacity of a virtual cluster provided by the Resource Manager is based on end user s requests and the status of virtual infrastructure. The Bio App Manager is also responsible for maintaining pipelines of the NGS application. In addition, the Bio App Manager manually or automatically installs bioinformatics applications on the virtual cluster provided by the Resource Manager. - Resource Manager: The Resource Manager manages and monitors the physical and virtual infrastructure for the Bio-Cloud system. We designed a virtual infrastructure management system to provide a virtual cluster (or virtual machine) to bioinformatics applications. Figure 2 shows components of the virtual infrastructure management system. 298

3 Figure 2. Virtual Infrastructure Management System - Virtual Infrastructure: The Virtual Infrastructure is consideredd as a pool of virtual clusters, virtual machines, and storage servers. The diverse capacity (i.e., the number of vcpu s, the size of virtual memory, the number of virtual machines for a virtual cluster, etc.) of virtual clusters and virtual machines is created by SaaS Provider s requests. The virtual resources in the Virtual Infrastructure are managed by the Computing Resource Management Server and Data Resource Management Server - Computing Resource Management Server: The Computing Resource Management Server manages virtual computing resources, such as virtual clusters and virtual machines. To allocate a virtual cluster to a job and execute it on the virtual cluster, the Computing Resource Management Server provides a Virtual Cluster Management module, which provides functions such as allocation, release, and monitoring of a virtual cluster. Virtual Cluster Management calculates the number of virtual CPU s, the size of the memory, and the number of virtual machines in a virtual cluster. Based on the calculation, the Virtual Cluster Management module creates a virtual cluster or machine from the Virtual Infrastructure. In addition, it also manages the execution of a job on a virtual cluster. - Data Resource Management Server: The Dataa Resource Management Server manages input/output and temporary data required to execute a job on a virtual cluster. It provides end users with data management functions to handle their data according to NGS analysis pipelines. Figure 3 shows an example of an NGS analysis pipeline frequently used in DNA and RNA re-sequencing. The pipeline consistss of BWA[4], BowTie[5] ], SSAHA[6] ], SamTools[ [7] and GATK[8]. The data requirement for each step of the pipeline is shown in Table

4 Figure 3. An example NGS analysis pipeline Table 1. The data size generated by the example NGS analysis pipeline file name ref.fa gen.fq snp.rod input file size(gigabyte) file name gen.sam outpu file size(gigabyte) gen.addrg.sam gen.bam gen.sorted.bam gen.snpcalls.vcf file name ref.fa.amb ref.fa.ann ref.fa.bwt ref.fa.pac ref.fa.rbwt size(gigabyte) index file file name ref.fa.rpac ref.fa.rsa ref.fa.sa gen.sai ref.fa.fai gen.snpcalls.vcf.idx size(gigabyte) Total 2, The Data Resource Management Serverr must store and maintainn the whole data set of 2 TB produced from the above analysis pipeline, which is reusable to NGS researchers. To do this, it annotates the metadata shown in Table 2 to the data products. It also creates a folder for each step of a 300

5 pipeline and stores data in the folder. As a result, the data product for a pipeline consists of a hierarchy of folders, and the folders hold data from each step with the metadata. Field Name UserID Storage Server Address Path Filename Datasize Creation date Modified date Application type pipelineid Table 2. Metadata table unique user identifier address of storage server stored data folder path to store user s data file name of data size of data creation date of data last modification date of data application type if data is output data pipeline Id for genome sequencing The Data Resource Management Server supports Project folders, File folders and Script folders to make the management of data products easy. The folders store output data from applications, end user s input data, and user defined scripts, respectively. Each user owns and manages these folders in a storage server like NFS [9]. Data updating consists of three steps as shown in Figure Meta-data related to the update request is stored to the meta-data table. 2. User submits the update request to storage server. 3. Updated data is transferred to storage server. Figure 4. Data management flow The storage server contains a Data Management module and a Data Transfer module to handle user s requests. The Data Management module maintains input/output data and provides creation, deletion, rename and copy of folders and data. In addition, it maintains a metadata table for efficient data management. The Data Transfer module uploads and downloads data between clients and a storage server. 3. Implementation The Bio-Cloud designed in the previous section is implemented by the use of Xen and OpenNebula. They are well-suited to the construction of a virtual infrastructure management system for bioinformatics applications. The user environment of Bio-Cloud is similar to KOBIC s current system to reduce customer s adaptation efforts. That is, we use the same system components, such as job scheduler, operating system of a virtual machine, web server, etc. 301

6 - Virtual Cluster Management module: We extended OpenNebula [10] to develop this module. OpenNebula supports Open Cloud API (OCA) to manage virtual infrastructure. OCA provides creation, deletion, and monitoring of virtual machines, virtual network, host, physical cluster, user and image. OCA does not provide functions for managing virtual clusters. We exploited OCA to add the functions for virtual cluster management to OpenNebula. The added functions are creation, deletion, and monitoring of a virtual cluster (Table 3). In addition, KOBIC uses Sun Grid Engine (SGE) [11] to execute bioinformatics applications because SGE is an open-source program of high stability with consistent updates. We also adapt SGE for a job scheduler in Bio-Cloud and execute KOBIC s jobs on virtual clusters. Table 3. Virtual Cluster Management APIs Allocate : allocation of virtual cluster Client client connect to a client String virtual machine template to allocate virtual cluster description int numvm the number of virtual machines for virtual cluster OneResponse communication acknowledgement finalizecluster : deletion of virtual cluster OneResponse communication acknowledgement savecluster : saving a virtual machine image in virtual cluster int vmid[] storing virtual machine identifier int disked storing disk identifier int imageid storing image identifier OneResponse communication acknowledgement getclusterinfo : monitoring virtual cluster VCInfo store virtual cluster information - Data Transfer module: KOBIC s current system uses innods [12] for transferring data. However, innods costs license fee, which is a major obstacle to deployment of Bio-Cloud. Instead, the opensource code Rapident [13] is used to implement the Data Transfer module. Data Transfer includes upload/download of data and folders (Table 4). 302

7 Upload : Upload data String srcpath String destpath boolean Download : Download data String srcpath String destpath boolean Table 4. APIs for data transfer path of user s data source path to store data to server success or failure of upload (true/false) stored data path on the server path to download data success or failure of download (true/false) - Data Management module: KOBIC users want to manage data for their own purposes. This module provides data management functions from a user s viewpoint. They include creation, deletion, rename and copy of folders and files (Table 5). Table 5. Data Management APIs create : creation of folder or file String path path including the folder(or file) name to create boolean success or failure of creation (true/false) delete : deletion of folder or file String path path including the folder(or file) name to delete boolean success or failure of delete(true/false) rename : rename folder or file String srcpath source path including the folder(or file) name to rename String destpath destination path to rename boolean success or failure of rename(true/false) copy : rename folder or file String srcpath source path including the folder(or file) name to copy String destpath destination path to copy boolean success or failure of copy(true/false) 303

8 4. Software as a Service on Bio-Cloud: NEUMA Web Portal NEUMA [14] web portal service was developed on the top of the Bio-Cloud system described in sections 2 and 3. NEUMA is the fastest RNA re-sequencing application for quantifying the volume of genomic expression data. The NEUMA analysis pipeline comprises the NGS applications shown in Table 6. steps Table 6. NEUMA pipeline function 1 make bowtie index file of Reference sequence by bowtie(bowtie [5]) 2 make mapping statistics of each RNA-Seq 3 find length distribution 4 build indexed table for transcriptome model 5 build suffix array table for transcriptome model 6 print suffix array table for transcriptome model 7 build gu, iu table for transcriptome model 8 gene and isoform quantification 9 merge Bio-Cloud provides the NEUMA web portal with the following features. First of all, it is responsible for allocating virtual clusters to NEUMA application services. The virtual clusters consist of vcpu, the memory and storage volume on the Bio App Manager s request (Figure 5). Figure 5. Request of virtual clusters Second, Bio-Cloud provides a feature that can summarize the requests of virtual clusters from users and monitor the current status of virtual clusters allocated to users (Figure 7). 304

9 Figure 6. Summary of requested virtual clusters Figure 7. Monitoring allocated virtual clusters Third, Bio-Cloud executes NEUMA application jobs through Sun Grid Engine (SGE), and monitors their status executed on virtual clusters (Figure 8). Figure 8. Monitoring user s job status 305

10 Finally, Bio-Cloud provides a data management feature for creating, deleting, and renaming files and folders. It also provides editing, uploading, and downloading of data files (Figure 9). 5. Related works Cloud computing services for bioinformatics have been studied by many research and industry organizations. CloudBurst[15] is a read-mapping application for DNA and RNA re-sequencing running on Amazon EC2. It drastically reduces the execution time for re-sequencing by the use of the MapReduce programming framework and runtime system. CloudBLAST[16] also parallelized BLAST[17] with MapReduce on the public cloud Amazon EC2. OBIWEE [18] has proposed an open-source on a private cloud. Kim [19] has also presented a private cloud computing environment for parallel processing of bioinformatics applications consisting of multiple tasks. These studies focus on the efficient processing of bioinformatics applications on public and private cloud environments. But all of them do not support analysis pipelines essential to analyze genomic data. computing environment to support workflows among bioinformatics applications The Bio-Cloud system we have proposed in this paper deals with analysis pipelines and data management issues for NGS analysis on a private cloud. 6. Conclusionss Figure 9. Data management interface In this paper, we have proposed and developed Bio-Cloud service for genomic data analysis, which can dynamically allocate a virtual cluster depending on a user s job. The advantages of Bio-Cloud may be summarized as follows. First, it reduces user s waiting time and job execution time by creating a virtual cluster suited for users jobs. Second, it allocates a virtual cluster to a job based on the characteristics of bioinformatics applications (sequential or parallel processing) and the number of vcpus or the size of memory. Finally, since each user s data is separately stored and maintained according to his/her own analysis viewpoint, a user s efforts to manage data is drastically reduced. Bio-Cloud is used to deploy NEUMA, a web tool for KOBIC s RNA re-sequencing [20]. Bio-Cloud and NEUMA web portal service are very useful for smalll labs that do not operate computing systems with high enough performance to handle their research. We are still working on the development of an adaptive scheduler for the Virtual Cluster Management in Bio-Cloud. The scheduler dynamically allocates a virtual cluster to a cluster of physical hosts based on the application s characteristics to 306

11 improve performance and scalability. We are also developing a data provenance management module for bioinformatics data management. 10. References [1] A. H. Chen, and M. C. Lee, Novel Approaches for the Prediction of Cancer Classification, In Proceedings of IJACT, Vol. 3, No. 3, pp , [2] H. Liu, Z. Lu, L Guo, Q. Wu, Q. Ge, and J. Lu, Next generation sequencing, an effective method for genomic profiling of circulating mirna, In Proceedings of JCIT, Vol. 6, No. 12, pp , [3] M. Xiong, Z. Zhao, J. Arnold, and F. Yu, Next-Generation Sequencing, In Proceedings of Journal of Biomedicine and Biotechnology, [4] [5] [6] [7] [8] [9] R. Sandberg, D. Goldberg, S. Kleiman, D. Walsh, and B. Lyon, Design and Implementation or the Sun Network Filesystem, In Proceedings of USENIX, [10] [11] [12] [13] [14] [15] M. Schatz, CloudBurst: Highly Sensitive Short Read Mapping with MapReduce, In Proceedings of BioInformatics, [16] A. Matsunaga, M. Tsugawa, and J. Fortes, CloudBLAST: Combining MapReduce and Virtualization on Distributed Resources for Bioinformatics Applications, In Proceedings of 4th IEEE International Conference on escience, [17] [18] F. Moreews, J. Piat, and O. Sallou, OBIWEE : an open source bioinformatics cloud environment, In Proceedings of 12th Annual Bioinformatics Open Source Conference, [19] T. K. Kim, B. K. Hou, and W. S. Cho, Private Cloud Computing Techniques for Inter-processing Bioinformatics Tools, In Proceedings of Convergence and Hybrid Information Technology [20] S. Lee, C. H. Seo, B. Lim, J. O. Yang, J. Oh, M. Kim, S. Lee, B. Lee, C. Kang, and S. Lee, Accurate quantification of transcriptome from RNA-Seq data by effective length normalization, In Proceedings of Nucleic Acids Res.,