Short Read Error Correction

Short reads produced from high-throughput sequencers come with short lengths and high sequencing error rates. These sequencing errors complicate some research fields related to short read analysis, including re-sequencing, single nucleotide polymorphism (SNP) calling, and genome assembly. Fortunately, the low sequencing cost allows producing sufficient reads to obtain a highly redundant coverage of a genome. Thus, it is possible to detect and correct sequencing errors based on this redundancy. However, the error correction procedure is both compute- and memory-intensive due to the large number of short reads, thus requiring both time and memory efficient short read error correctors to tackle the flood of short reads.


Musket is an efficient multistage k-mer based corrector for Illumina short-read data. We employ the k-mer spectrum approach and introduce three correction techniques in a multistage workflow: two-sided conservative correction, one-sided aggressive correction and voting-based refinement. Our performance evaluation results, in terms of correction quality and de novo genome assembly measures, reveal that Musket is consistently one of the top performing correctors. In addition, Musket is multi-threaded using a master-slave model and demonstrates superior parallel scalability compared to all other evaluated correctors as well as a highly competitive overall execution time.

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DecGPU (Distributed Error Correction on GPUs) is the first parallel and distributed error correction algorithm for high-throughput short reads using CUDA C++ and MPI. Using simulated and real datasets, our algorithm demonstrates superior performance, in terms of error correction quality and execution speed, to the existing error correction algorithms. The distributed feature of our algorithm makes it feasible and flexible for the error correction of large-scale datasets.

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SHREC is a new error correction method based on a suffix tree running on standard multi-cores with Java.

Download: Sourceforge