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. 2012 Mar 17;3(2):148–152. doi: 10.1007/s13238-012-2015-8

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

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,✉
PMCID: PMC4875413  PMID: 22426983

Abstract

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.

Keywords: CloudLCA, metagenome analysis, cloud computing

Footnotes

These authors contributed equally to the work.

Contributor Information

Yi Zhao, Email: biozy@ict.ac.cn.

Runsheng Chen, Email: chenrs@sun5.ibp.ac.cn.

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