MapReduce for accurate error correction of next-generation sequencing data | Zendy

Liang Zhao | Zendy; Qingfeng Chen | Zendy; WenCui Li | Zendy; Peng Jiang | Zendy; Limsoon Wong | Zendy; Jinyan Li | Zendy

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MapReduce for accurate error correction of next-generation sequencing data

Author(s) -

Liang Zhao,

Qingfeng Chen,

WenCui Li,

Peng Jiang,

Limsoon Wong,

Jinyan Li

Publication year - 2017

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btx089

Subject(s) - computer science , error detection and correction , sequence (biology) , position (finance) , word error rate , source code , data mining , cloud computing , code (set theory) , layer (electronics) , set (abstract data type) , algorithm , artificial intelligence , biology , programming language , finance , economics , genetics , operating system , chemistry , organic chemistry

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.

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