CloudDOE: A User-Friendly Tool for Deploying Hadoop Clouds and Analyzing High-Throughput Sequencing Data with MapReduce

@inproceedings{Chung2014CloudDOEAU,
  title={CloudDOE: A User-Friendly Tool for Deploying Hadoop Clouds and Analyzing High-Throughput Sequencing Data with MapReduce},
  author={Wei-Chun Chung and Chien-Chih Chen and Jan-Ming Ho and Chung-Yen Lin and Wen-Lian Hsu and Yu-Chun Wang and D. T. Lee and Feipei Lai and Chih-Wei Huang and Yu-jung Chang},
  booktitle={PloS one},
  year={2014}
}
BACKGROUND 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. 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… CONTINUE READING
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