Abstract
BackgroundExplosive growth of next-generation sequencing data has resulted in ultra-large-scale data sets and ensuing computational problems. Cloud computing provides an on-demand and scalable environment for large-scale data analysis. Using a MapReduce framework, data and workload can be distributed via a network to computers in the cloud to substantially reduce computational latency. Hadoop/MapReduce has been successfully adopted in bioinformatics for genome assembly, mapping reads to genomes, and finding single nucleotide polymorphisms. Major cloud providers offer Hadoop cloud services to their users. However, it remains technically challenging to deploy a Hadoop cloud for those who prefer to run MapReduce programs in a cluster without built-in Hadoop/MapReduce.ResultsWe present CloudDOE, a platform-independent software package implemented in Java. CloudDOE encapsulates technical details behind a user-friendly graphical interface, thus liberating scientists from having to perform complicated operational procedures. Users are guided through the user interface to deploy a Hadoop cloud within in-house computing environments and to run applications specifically targeted for bioinformatics, including CloudBurst, CloudBrush, and CloudRS. One may also use CloudDOE on top of a public cloud. CloudDOE consists of three wizards, i.e., Deploy, Operate, and Extend wizards. Deploy wizard is designed to aid the system administrator to deploy a Hadoop cloud. It installs Java runtime environment version 1.6 and Hadoop version 0.20.203, and initiates the service automatically. Operate wizard allows the user to run a MapReduce application on the dashboard list. To extend the dashboard list, the administrator may install a new MapReduce application using Extend wizard.ConclusionsCloudDOE is a user-friendly tool for deploying a Hadoop cloud. Its smart wizards substantially reduce the complexity and costs of deployment, execution, enhancement, and management. Interested users may collaborate to improve the source code of CloudDOE to further incorporate more MapReduce bioinformatics tools into CloudDOE and support next-generation big data open source tools, e.g., Hadoop BigTop and Spark. Availability: CloudDOE is distributed under Apache License 2.0 and is freely available at http://clouddoe.iis.sinica.edu.tw/.
Highlights
Progress in computer science and technology has vastly promoted the development of genetic research in the past few decades
The explosive growth of Next-generation sequencing (NGS) data has already resulted in ultra-large-scale datasets and various computational problems for conventional NGS tools; for instance, insufficient computation resources and undesirably long execution times [1]
CloudDOE provides straightforward and user-friendly graphical interfaces, and covers most of the complicated, technical, and difficult command-line operations a user may encounter in traditional approaches (Figure 2)
Summary
Progress in computer science and technology has vastly promoted the development of genetic research in the past few decades. Next-generation sequencing (NGS) is a notable technology for genetics and computational biology research. The explosive growth of NGS data has already resulted in ultra-large-scale datasets and various computational problems for conventional NGS tools; for instance, insufficient computation resources and undesirably long execution times [1]. To overcome the issues associated with processing of large-scale data, MapReduce [2] and its Java implementation, Hadoop [3], were introduced. MapReduce is a framework that processes huge datasets in parallel by utilizing a large number of computers simultaneously, in which the computing resources can be allocated dynamically. Explosive growth of next-generation sequencing data has resulted in ultra-large-scale data sets and ensuing computational problems. Cloud computing provides an on-demand and scalable environment for large-scale data analysis. It remains technically challenging to deploy a Hadoop cloud for those who prefer to run MapReduce programs in a cluster without built-in Hadoop/MapReduce
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