Liang Zhao1,2, Qingfeng Chen1, Wencui Li2, Peng Jiang1, Limsoon Wong3, Jinyan Li4. 1. School of Computing and Electronic Information, Guangxi University, Nanning 530004, China. 2. Taihe Hospital, Hubei University of Medicine, Hubei 442000, China. 3. School of Computing, National University of Singapore, Singapore 117417, Singapore. 4. Advanced Analytics Institute and Centre for Health Technologies, University of Technology Sydney, Broadway, NSW 2007, Australia.
Abstract
MOTIVATION: Next-generation sequencing platforms have produced huge amounts of sequence data. This is revolutionizing every aspect of genetic and genomic research. However, these sequence datasets contain quite a number of machine-induced errors-e.g. errors due to substitution can be as high as 2.5%. Existing error-correction methods are still far from perfect. In fact, more errors are sometimes introduced than correct corrections, especially by the prevalent k-mer based methods. The existing methods have also made limited exploitation of on-demand cloud computing. RESULTS: We introduce an error-correction method named MEC, which uses a two-layered MapReduce technique to achieve high correction performance. In the first layer, all the input sequences are mapped to groups to identify candidate erroneous bases in parallel. In the second layer, the erroneous bases at the same position are linked together from all the groups for making statistically reliable corrections. Experiments on real and simulated datasets show that our method outperforms existing methods remarkably. Its per-position error rate is consistently the lowest, and the correction gain is always the highest. AVAILABILITY AND IMPLEMENTATION: The source code is available at bioinformatics.gxu.edu.cn/ngs/mec. CONTACTS: wongls@comp.nus.edu.sg or jinyan.li@uts.edu.au. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
MOTIVATION: Next-generation sequencing platforms have produced huge amounts of sequence data. This is revolutionizing every aspect of genetic and genomic research. However, these sequence datasets contain quite a number of machine-induced errors-e.g. errors due to substitution can be as high as 2.5%. Existing error-correction methods are still far from perfect. In fact, more errors are sometimes introduced than correct corrections, especially by the prevalent k-mer based methods. The existing methods have also made limited exploitation of on-demand cloud computing. RESULTS: We introduce an error-correction method named MEC, which uses a two-layered MapReduce technique to achieve high correction performance. In the first layer, all the input sequences are mapped to groups to identify candidate erroneous bases in parallel. In the second layer, the erroneous bases at the same position are linked together from all the groups for making statistically reliable corrections. Experiments on real and simulated datasets show that our method outperforms existing methods remarkably. Its per-position error rate is consistently the lowest, and the correction gain is always the highest. AVAILABILITY AND IMPLEMENTATION: The source code is available at bioinformatics.gxu.edu.cn/ngs/mec. CONTACTS: wongls@comp.nus.edu.sg or jinyan.li@uts.edu.au. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Authors: Franziska Pfeiffer; Carsten Gröber; Michael Blank; Kristian Händler; Marc Beyer; Joachim L Schultze; Günter Mayer Journal: Sci Rep Date: 2018-07-19 Impact factor: 4.379
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