CloudLCA: finding the lowest common ancestor in metagenome analysis using cloud computing

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• 作者：Guoguang Zhao (1) (4)
  Dechao Bu (1) (4)
  Changning Liu (1)
  Jing Li (1)
  Jian Yang (3)
  Zhiyong Liu (1)
  Yi Zhao (1)
  Runsheng Chen (1) (2)
  
• 关键词：CloudLCA ; metagenome analysis ; cloud computing
• 刊名：Protein & Cell
• 出版年：2012
• 出版时间：February 2012
• 年：2012
• 卷：3
• 期：2
• 页码：148-152
• 全文大小：530KB
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• 作者单位：Guoguang Zhao (1) (4)
  Dechao Bu (1) (4)
  Changning Liu (1)
  Jing Li (1)
  Jian Yang (3)
  Zhiyong Liu (1)
  Yi Zhao (1)
  Runsheng Chen (1) (2)
  
  1. Bioinformatics Research Group, Key Laboratory of Intelligent Information Processing, Advanced Computing Research Laboratory, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, 100190, China
  4. Graduate School of the Chinese Academy of Sciences, Beijing, 100190, China
  3. State Key Laboratory for Molecular Virology and Genetic Engineering, National Institute for Viral Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, 100176, China
  2. Bioinformatics Laboratory and National Laboratory of Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing, 100101, China
  

文摘

Estimating taxonomic content constitutes a key problem in metagenomic sequencing data analysis. However, extracting such content from high-throughput data of next-generation sequencing is very time-consuming with the currently available software. Here, we present CloudLCA, a parallel LCA algorithm that significantly improves the efficiency of determining taxonomic composition in metagenomic data analysis. Results show that CloudLCA (1) has a running time nearly linear with the increase of dataset magnitude, (2) displays linear speedup as the number of processors grows, especially for large datasets, and (3) reaches a speed of nearly 215 million reads each minute on a cluster with ten thin nodes. In comparison with MEGAN, a well-known metagenome analyzer, the speed of CloudLCA is up to 5 more times faster, and its peak memory usage is approximately 18.5% that of MEGAN, running on a fat node. CloudLCA can be run on one multiprocessor node or a cluster. It is expected to be part of MEGAN to accelerate analyzing reads, with the same output generated as MEGAN, which can be import into MEGAN in a direct way to finish the following analysis. Moreover, CloudLCA is a universal solution for finding the lowest common ancestor, and it can be applied in other fields requiring an LCA algorithm.