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A Survey of Error-Correction Methods for Next Generation Sequencing

Published: Wednesday, February 20, 2013
Last Updated: Wednesday, February 20, 2013
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This article provides a review of error-correction algorithms for NGS data and their comparative evaluation.

Abstract
Error Correction is important for most next-generation sequencing applications because highly accurate sequenced reads will likely lead to higher quality results.Many techniques for error correction of sequencing data fromnext-gen platforms have been developed in the recent years. However, compared with the fast development of sequencing technologies, there is a lack of standardized evaluation procedure for different error-correction methods, making it difficult to assess their relative merits and demerits. In this article, we provide a comprehensive review of many error-correction methods, and establish a common set of benchmark data and evaluation criteria to provide a comparative assessment.We present experimental results on quality, run-time, memory usage and scalability of several error-correction methods. Apart from providing explicit recommendations useful to practitioners, the review serves to identify the current state of the art and promising directions for future research. Availability: All error-correction programs used in this article are downloaded from hosting websites.

This article was puclished online in Briefings in Bioinformatics and is free to access.


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