An Error Correction Algorithm for NGS Data

Kchouk, Mehdi; Gibrat, Jean‐François; Elloumi, Mourad

doi:10.1109/dexa.2017.33

Cited by 2 publications

(1 citation statement)

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“…Longer read error correction has become a concern for bioinformaticians, motivating the implementation of novel error correction algorithms tailored to NGS technology. We introduced a new approach for error correction of LLRs created by an NGS sequencing utilizing another LLRs produced by the similar NGS sequencing in this publication [ 20 ]. As a result, our method is self-correcting.…”

Section: Related Workmentioning

confidence: 99%

K-Mer Spectrum-Based Error Correction Algorithm for Next-Generation Sequencing Data

AlEisa

Hamad

Elhadad

2022

Computational Intelligence and Neuroscience

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In the mid-1970s, the first-generation sequencing technique (Sanger) was created. It used Advanced BioSystems sequencing devices and Beckman’s GeXP genetic testing technology. The second-generation sequencing (2GS) technique arrived just several years after the first human genome was published in 2003. 2GS devices are very quicker than Sanger sequencing equipment, with considerably cheaper manufacturing costs and far higher throughput in the form of short reads. The third-generation sequencing (3GS) method, initially introduced in 2005, offers further reduced manufacturing costs and higher throughput. Even though sequencing technique has result generations, it is error-prone due to a large number of reads. The study of this massive amount of data will aid in the decoding of life secrets, the detection of infections, the development of improved crops, and the improvement of life quality, among other things. This is a challenging task, which is complicated not just by a large number of reads and by the occurrence of sequencing mistakes. As a result, error correction is a crucial duty in data processing; it entails identifying and correcting read errors. Various k-spectrum-based error correction algorithms’ performance can be influenced by a variety of characteristics like coverage depth, read length, and genome size, as demonstrated in this work. As a result, time and effort must be put into selecting acceptable approaches for error correction of certain NGS data.

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Section: Related Workmentioning

confidence: 99%