Bioinformatician in an NHS Clinical Genomics Unit - Report Example

Bioinformatician in an NHS clinical genomics unit Number; Lecturer; Table of contents 2 Introduction 4 Problem analysis 5 Scope 6 Adoption of cloud computing. 6 Cloud computing models of service 7 Strategies for adopting cloud computing 9 Strategy 1: Hybrid Cloud 9 Infrastructure layer of Hybrid Cloud 9 Application layer of Hybrid Cloud 10 Strategy 2: Public Cloud 11 Infrastructure layer for Public Cloud 11 Application Layer for Public Cloud 12 Recommendation 13 References 16 Appendix A: 18 A1: Story Board for the Public Strategy 18 A2: Free Bioinformatics Software 18 A3: Adoption and Deployment Issues 19 Abstract The cloud computing technology provides a new platform through which advanced software can be developed and used in many industries. It offers computing and software infrastructure and services through the network. The technology has become so reliable in many institutions that it has gained potential for use in genome research experiments. These experiments involve large datasets that utilize highly demanding algorithms that need reliable computational resources. This paper elaborates how cloud computing can be applied to address the problems and challenges that are associated with the next generation DNA sequencing.The paper is structured into three sections that identify the problems that are mainly faced in an NHS clinical genomics unit and how these problems can be addressed by employing cloud computing technologies. The common problem associated with this field is the huge amount of data that is as a result of the next and third generation sequencing techniques. Genome sequencing creates a huge amount of data on a daily basis that will require additional resources for storage and analysis hence resulting in additional expenses that may not be possible for small organizations. Cloud computing offers a convenient way of virtually computing, storing and analysing these set of data without the need of installing the required resources on site. This report also provides a description of the cloud computing technology, its features, and types of strategies and how they can be applied in DNA sequencing. Two strategies are proposed in this report for adoption of cloud computing in DNA sequencing; Hybrid cloud and the Public cloud strategies. The report will provide a comprehensive elaboration with the aid of tables and pictures or certificates of the use of cloud computing of bioinformatics and its application in DNA sequencing projects. Introduction Sanger technology which was among the first generation sequencing technologies generated fairly large amounts of data. However, with the next generation sequencing technologies (NGS), these amounts of data increased tremendously. The Bieplan indicates that the huge amounts of data prove difficult to compute, analyse and store. This complexity affects many data centres and genome units in hospitals. Essentially, the major problems faced by the health care units and data centres is complexity, computational problems and storage difficulties that is associated with the next generation sequencing. It is the small organization and enterprises that feel the weight of these problems due to lack of enough resources to install the required storage and computational resources. These can be addressed by using cloud computing technology. Cloud computing can be used for bioinformatics in the health sector (Schatz, &Salzberg, 2010). This paper basically aims to explore the possibilities of using the cloud computing technology in DNA sequencing projects in the health sector. Cloud computing is one of the recent advancement in the field computing that is based on the use and consumption of computing resources. Basically, the technology involves the deployment of groups of remote servers and software networks that will accord users the ability to centrally store data and subsequent access through online platforms (Thakur &Bandopadhyay, 2013). The technology has gained application in most industries where huge amounts of data are generated hence requiring adequate storage facilities. Problem analysis DNA sequencing is mainly associated with large comparative genome studies that generate huge volumes of data. The tools and data that is used and generated in these studies is ever increasing hence requiring more storage space and computing infrastructure (Rosenthal, Mork, Stanford, Koester &Reynolds 2010). This increase in data requirements also means the capacity for computing and storing the data must be increased. Therefore, local infrastructure and technology may not have the capacity to handle the increasing amount of data requirements. According to Thakur &Bandopadhyay (2013), the third and next generation sequencing techniques have the characteristic of flooding data caused by techniques such as nanopore, pyrosequencing and non optical Ion chip sequencing. The data flooding results in too much analysis that must be carried out in a smooth and convenient way. Thakur &Bandopadhyay (2013), also opine that bioinformatics has also been affected by the same problem of huge amounts of data produced and analysed. Some of the specific problems that we face as an organization associated with next generation sequencing include; NGS generates gigabytes and even terabytes of datasets that require more sequencing. These data generated consumes a lot of computational and storage resources which is not available in the organization NGS sequencing results in differential of raw sequencing data into the regular DNA genomes which are non compatible with the next generation sequencing platforms. Reintegrating and reformatting these data sets to meet the broad standards can be time and resource consuming. Inherent software often encounters problems while displaying and conveying the datasets to different users. Additionally, there is lack of specialist staff in sequencing who can create and develop reliable software to solve computational problems. The organization faces challenges in terms of insufficient resources that can be able to accommodate the large datasets. We also lack expertise to manage the complex computational resources and network infrastructure. Consequently, time is prime to adopt the use of virtual technologies such as cloud computing technology in the biological sphere to adequately address the data storage, analysis and management of research data. This is aimed at complementing research in the field of DNA sequencing in the health sector (Rosenthal et al 2010). Scope This report elaborates the strategies that can be used in the adoption of cloud computing for bioinformatics particularly in DNA sequencing unit of the NHS organization. The main focus of this report is to explain what is cloud computing, its main features and the strategies that can be used to adopt it in the organization. The report will mainly propose strategies through which cloud computing can be adopted for management and analysis of the next generation sequencing data in the bioinformatics unit of the organization. Adoption of cloud computing. The problems identified for the next generation sequencing can be effectively addressed by adoption of cloud computing technologies in the organization. Mell&Grance (2009) defines Cloud computing as a computing technology that relies mainly on sharing of resources in order to achieve economies of scale and coherence. This technology can be likened to a utility over a network. The concept behind this technology is the shared services and converged infrastructure. Often simply referred to as the cloud, cloud computing also focuses more on the maximization of the shared resources (Bateman & Wood 2009). According to Marston, Bandyopadhyay, Zhang, &Ghalsasi (2011), cloud computing technology delivers on-demand resources needed for computing including simple and complicated appliances to huge data centres. These resources are provided over the internet on a pay for use basis. In our case, cloud computing can be effectively used to address the storage challenges faced by the organization as a result of Next Generation sequencing. Cloud computing will be used to store the huge amounts of datasets generated from cloud computing. It also provides analytical and computational services that could otherwise cost the organization a great deal. Cloud computing models of service Infrastructure as a service (IaaS): this involves renting cloud infrastructure such as the servers, computers, networks and other infrastructure. Providers of the IaaS offer physical or virtual infrastructure and other additional resources depending on the user requirements (Lenk, Klems, Nimis, Tai&Sandholm 2009). Software as a Service (SaaS): Cloud users are given access to application software and a database. The infrastructure and platforms that run the applications are managed by cloud providers who manage these services (Lenk et al 2009). The provision is either pay per use or on subscription basis. Since there are numerous cloud users, the cloud applications can serve more than one cloud users through virtualization a strategy called multitenant (Rimal, Choi &Lumb 2009). Platform as a Service (PaaS): The PaaS model involves provision of a cloud computing platform through provision of operating system, database, programming language execution environment and the web servers (Rimal, Choi &Lumb 2009). The cloud users can develop and run the applications and software solutions on the cloud platform and save the complexity and costs associated with purchasing and managing the software and hardware layers (Lenk et al 2009). Cloud computing can be deployed in several ways including; ● Private cloud: here the cloud infrastructure is built, managed and accessed internally (inhouse) for a single organization. It is not shared with any other external entities (Lenk et al 2009). ● Public cloud: in this strategy, the cloud infrastructure is available for multiple users through the internet. It is essentially available to the public but with enforcement of privileges for privacy (Lenk et al 2009). ● Hybrid cloud: this strategy is a combination of several other strategies provided by several providers. It allows clients to experience more benefits born from a mixture of different clouds (Lenk et al 2009). ● Community cloud: cloud computing strategy that is used by a particular group of users with similar interests and needs (Lenk et al 2009). Strategies for adopting cloud computing There are two major strategies that we can use to deploy or adopt the cloud computing services for next generation sequencing in the organization. Strategy 1: Hybrid Cloud As the name suggests, this is a composition of two or more clouds that operate as distinct clouds but are bound together, therefore offering the benefits of multiple deployment strategies. According to Buyya, Yeo, Venugopal, Broberg, &Brandic (2009), hybrid cloud can be defined as a cloud computing service that includes a combination of the private and public cloud services from single or different service provide Infrastructure layer of Hybrid Cloud This deployment strategy allows the cloud user to expand the capacity and the capability of the cloud service through integration, aggregation and customization with other cloud services (Marston, Bandyopadhyay, Zhang&Ghalsasi 2011). DNA sequencing can use this strategy to store sensitive data in an in-house or private cloud application and interconnect the application to an intelligence application on a public cloud as a software service. The hybrid cloud computing can be implemented using the Amazon Web Service (AWS) that provides Amazon Compute Cloud (EC2) and the virtual private cloud system (VPC). The EC2 public cloud provides a public platform through which interaction with clients, basic and non confidential communication, complex computation, visualization and storage of data can be affected. The VPC (Virtual Private Cloud) provides a private platform for the storage and preservation of sensitive information in private location. These set of infrastructure offers a perfect strategy for management of NGS datasets. The NGS confidential and sensitive data sets can be stored through the VPC away from internet access while communication and interaction is achieved through the EC2. Data integrity is maintained through encryption and communicated via an encrypted VPN (Marston et al 2011). On the other hand, solve Bio can be used as a cloud based infrastructure to host collated, curated and versioned data for Next Generation Sequencing. Solve Bio offers an infrastructural platform where data can be stored and used in a secure environment. It is equipped with the requisite audit trails, access controls and encryption technology. The pricing is similar to AWS where customers pay for services per use. This offers great flexibility and scalability to the clients. Additionally, the client is given data free of charge and only pay for use of infrastructure (Marston et al 2011). Application layer of Hybrid Cloud In the context of this strategy, complex computing and analysis of the numerous DNA sequencing data generated by the NGS can be handled by cloud Biolinux. This application is compatible with the AWS infrastructure from Amazon and Solve Bio platforms. Most importantly, BioLinux application is a virtual machine that can be accessed publicly by installation on the EC2, VPC and Solve Bio clouds. It has a complete Graphical User Interface for both technical and non technical users for interaction, uploading tools and other applications into the cloud. These uploaded applications are used for genome assembly, sequence analysis, alignment, annotation, molecular modelling, visualization and gene expression (Marston et al 2011). Generally, AWS, SolveBio and BioLinux offer convenience, reliability and confidentiality. Through this strategy the data is secured through the VPC and solve Bio allowing NHS to keep sensitive NGS data private but use the EC2 to do other things such as communication. It is one of the most flexible and scalable strategies for deploying cloud computing for bioinformatics in the organization, with unlimited storage space and very fast deployment and affordability (Marston et al 2011). Strategy 2: Public Cloud Unfortunately, the teacher strongly disagree about the Public Cloud, he prefer to choose the private could than public because the NHS has high secured and they need something with a high security to avoid access the data so Could you change it to the private cloud, please? Really appreciate ur time and help Tim, Ok I will change it and get back to you tomorrow. This is a strategy where deployment of service is done through a network that is open for the public. These services are often free and are provided by many cloud providers over the internet. This is common for services that require minimal resources (Rimal, Choi &Lumb 2009). Infrastructure layer for Public Cloud To implement a public cloud based Next generation DNA Sequencing, NHS must use infrastructure that is provided by the cloud providers. The best known providers for the infrastructure as a service include the Amazons’ AWS, Solve Bio and Gene Stack (Peng, Lei, Zhang, &Li 2009). Amazons AWS cloud infrastructure includes the EC2 service that is also used for the hybrid cloud service. The cost range for Amazon AWS service is between $0.065 - $0.260 on an hourly-basis. Efficiency of data analysis is further enhanced through the combination of public cloud and IaaS service. EC2 and Solve Bio offer public infrastructure platforms for analyzing large data, computation and storage of genome data and information. Application Layer for Public Cloud To add on to the infrastructure, public cloud computing and storage also requires applications to particularly aide in next generation Gene Sequencing. The best application that can be used is the DNA Nexus application (Buyya et al 2009). This application is used for the computation of the next generation sequence data and creates an interface with the storage infrastructure for access to stored information and transfer of these data to the respective cloud providers (Buyya et al 2009). Public cloud deployment strategy is cost effective; it offers the largest benefit from economies of scale due to the great assimilation of resources. Centralized management and operation of resources is shared across the subsequent cloud services. Additionally, some services are free to clients with the provider relying on advertisements for their revenue (Grossman 2009).    Ultimate scalability; the resources on a public cloud are available on demand basis hence the infrastructure and applications can be provided for different fluctuating needs (Grossman 2009). Reliability; service provision is almost guaranteed in public cloud service due to the large pool of resources reinforced by data redundancy. Therefore, the failure of one machine or infrastructure cannot hinder provision of service (Grossman 2009). Flexibility: with the availability of several service providers of the public cloud service, one has the capability of accessing data from any device that has internet connection (Grossman 2009). Simplicity; public cloud computing is relatively simple hence can be used by many people including the staff handling the next generation sequence data analysis (Grossman 2009). On the other hand public cloud strategy lacks security for sensitive and confidential data. Data is vulnerable to access by the public since they stored and access through public infrastructure. You may not know how where data is stored, if it is safe or not (Ramgovind, & Smith 2010). Another disadvantage is the issue of reliability that was occasioned by the two day outage in the Amazon cloud. This cloud results in unavailability of access that could lead to inefficiencies in NHS institutions (Ramgovind, & Smith 2010). Recommendation Cloud computing provides the perfect solution to the challenges and problems that are faced by the next generation DNA sequencing. Through cloud computing several aspects of Next Generation Sequencing can be addressed, but most importantly the issue of limited resources and capital required for storage and computation infrastructure is reduced (T &Morgens 2008). As seen from the analysis, data and information generated from Next generation DNA sequencing is voluminous, therefore, the organization must invest a lot of capital in infrastructure that is used for computation and storage of these data. Many institutions handling DNA sequencing are faced with this challenge that can be alleviated through the use of cloud computing (T &Morgens 2008). The strategies for adopting the cloud computing services offer great benefits to DNAsequencing and bioinformatics as a whole. The cost of implementing cloud computing service for DNA sequencing are low thereforereducing the costs that the organization will incur if we were install in-house infrastructure for management of computing and storage infrastructure. Regardless of the strategy adopted for implementation of cloud computing for next generation sequencing, the overall cost is drastically reduced. The cheapest of the strategies is the public cloud service. Public cloud is a suitable strategy for our organization due to a limited budget. This is because public cloud services often come at lower costs with some offering the service for free and gaining revenue through advertisements. Therefore, I recommend the public cloud adoption strategy. Public cloud computing offers great reliability for storage and computation of DNA sequencing data. This is due to the fact that the cloud offers continuous availability and access to data stored in the cloud. It is a common practice for all cloud computing strategies that data must be backed up through redundancy. Hence, in case of any failure, there is another copy of data available for the client. With increased demands and the need for next generation sequencing data cloud computing based systems such as GeneSifter can easily adapt in real time to meet these needs. They have great flexibility to increase the computational speeds and storage capacities to meet the demand. Users have immediate access to the data and information hence increasing their output in the long run (Rimal&Rumb 2009). Data can also be accessed from any location with an internet enabled mobile device. This is achieved through public cloud strategies where access and retrieval are not too restricted. However, security issues may arise from public cloud deployment strategies where the confidential data and information is vulnerable to unauthorized access. The hybrid cloud deployment strategy offers a more secure implementation strategy for Next Generation Sequencing. Hybrid cloud service combines both private and public cloud services. The private cloud service accords cloud users a high degree of confidentiality and security of the data stored in the cloud. Additionally, it allows more sensitive and confidential data to be stored locally. In conclusion, the best strategy to employ in the adoption of cloud computing for DNA sequencing depends on the company size and the level of datasets generated. As a Bioinformatician the best strategy will be one that can save the company money and at the same time maintain effectiveness of NGS. References Armbrust, M., Fox, A., Griffith, R., Joseph, A. D., Katz, R., Konwinski, A.,&Zaharia, M. (2010). A view of cloud computing. Communications of the ACM, 53(4), 50-58. Bajo, J., Zato, C., de la Prieta, F., de Luis, A., & Tapia, D. (2010). Cloud computing in bioinformatics. In Distributed Computing and Artificial Intelligence (pp. 147-155). Springer Berlin Heidelberg. Bateman, A., & Wood, M. (2009). Cloud computing. Bioinformatics, 25(12), 1475-1475. Bell G., Hey T., Szalay A., (2009). Computer science. Beyond the data deluge. Science 323, 1297-1298. Buyya, R., Yeo, C. S., Venugopal, S., Broberg, J., &Brandic, I. (2009). 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Cloud computing—The business perspective. Decision Support Systems, 51(1), 176-189. Mell, P., &Grance, T. (2009). The NIST definition of cloud computing. National Institute of Standards and Technology, 53(6), 50. Miller, M. (2008). Cloud computing: Web-based applications that change the way you work and collaborate online. Que publishing. Peng, J., Zhang, X., Lei, Z., Zhang, B., Zhang, W., & Li, Q. (2009, December). Comparison of several cloud computing platforms. In Information Science and Engineering (ISISE), 2009 Second International Symposium on (pp. 23-27). IEEE. Ramgovind, S., Eloff, M. M., & Smith, E. (2010, August). The management of security in cloud computing. In Information Security for South Africa (ISSA), 2010 (pp. 1-7). IEEE. Rosenthal, A., Mork, P., Li, M. H., Stanford, J., Koester, D., & Reynolds, P. (2010). Cloud computing: a new business paradigm for biomedical information sharing. Journal of biomedical informatics, 43(2), 342-353. Rimal, B. P., Choi, E., &Lumb, I. (2009, August). A taxonomy and survey of cloud computing systems. In INC, IMS and IDC, 2009. NCM09. Fifth International Joint Conference on (pp. 44-51). Ieee. Schatz, M. C., Langmead, B., &Salzberg, S. L. (2010). Cloud computing and the DNA data race. Nature biotechnology, 28(7), 691-693. Strickland, J. (2008). How cloud computing works. Howstuffworks. Thakur, R. S., &Bandopadhyay, R. (2013). Role of cloud computing in bioinformatics research for handling the huge biological data. In Biology of useful plants and microbes (pp. 321-329). Tograph, b., &Morgens, y. R. (2008). Cloud computing. Communications of the ACM, 51(7). Appendix A: Appendix A shows a storyboard of adopted strategy, available bioinformatics software for testing purposes and the possible issues and concerns for adoption of cloud computing. A1: Story Board for the Public Strategy A2: Free Bioinformatics Software The table below presents a list and brief description of free bioinformatics software available for testing before adoption of the intended software. Software Description of functions Platform License AMPHORA Used for Metagenomic analysis The software can run on the Linux Platform GNU general Public license StadenPackage Software used for assembly of genome sequence, editing and analysis. It is mainly made of gap4, gap5 and spin. Mac Linux windows BSD GENtle This software is equivalent to Vector NTI , the tool is used to analyze and edit DNA sequence files. Linux GPL Galaxy Galaxy software is one of the best tools for handling scientific workflow that is common with bioinformatics. It is also a suitable tool for data integration. Unix Linux Academic Free License GenePattern This system is also used for handling scientific workflows and also provides access to more than 150 genomic analysis tools Windows Linux MIT License A3: Adoption and Deployment Issues Deployment and adoption of cloud computing comes with a share of concerns. Some of these issues and concerns include; Availability of data: For timely research and effectiveness of company researches, there must be availability of health data and information. Research has been done to determine the capacity, availability and the capability of the health care data and information. The results show overloading and huge data and information held by cloud vendors that often result in crushes and failures. There are also occasional periods of unavailability of the same data that often results in service delays. The huge volumes of data to processed and analysed affects performance that can result in ineffective communication between the parties. Therefore, it is important to consider several factors such as reliability, reputation, high capacity and good quality structures when selecting a cloud provider. This may be costly but it is worth every penny. No Appropriate Standardization: No appropriate standards have been established for cloud computing to date. This complicates the process of evaluating a cloud provider. It is upon the company to install and use standardized technology as a guarantee for continuous operation of applications in the company. This will include use of cloud providers with universally used toolkits that can allow easy migration should there be need for such measures. Read More