Next-generation sequencing (NGS) technologies produce huge amounts of data. These sequencing data unavoidably are accompanied by the occurrence of sequencing errors which constitutes one of the major problems of further analyses. Error correction is indeed one of the critical steps to the success of NGS applications such as de novo genome assembly and DNA resequencing as illustrated in literature. However, it requires computing time and memory space heavily. To design an algorithm to improve data quality by efficiently utilizing on-demand computing resources in the cloud is a challenge for biologists and computer scientists. In this study, we present an error-correction algorithm, called the CloudRS algorithm, for correcting errors in NGS data. The CloudRS algorithm aims at emulating the notion of error correction algorithm of ALLPATHS-LG on the Hadoop/ MapReduce framework. It is conservative in correcting sequencing errors to avoid introducing false decisions, e.g., when dealing with reads from repetitive regions. We also illustrate several probabilistic measures we introduce into CloudRS to make the algorithm more efficient without sacrificing its effectiveness. Running time of using up to 80 instances each with 8 computing units shows satisfactory speedup. Experiments of comparing with other error correction programs show that CloudRS algorithm performs lower false positive rate for most evaluation benchmarks and higher sensitivity on genome S. cerevisiae. We demonstrate that CloudRS algorithm provides significant improvements in the quality of the resulting contigs on benchmarks of NGS de novo assembly. Availability: The source code of CloudRS is freely available at https://github.com/ice91/ReadStackCorrector.

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/.

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