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 from next-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. The evaluation tool kit is publicly available at: http://aluru-sun.ece.iastate.edu/doku.php?id=ecr.
%0 Journal Article
%1 Yang06042012
%A Yang, Xiao
%A Chockalingam, Sriram P.
%A Aluru, Srinivas
%D 2012
%J Briefings in Bioinformatics
%K bioinformatics error-correction short-reads
%R 10.1093/bib/bbs015
%T A survey of error-correction methods for next-generation sequencing
%X 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 from next-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. The evaluation tool kit is publicly available at: http://aluru-sun.ece.iastate.edu/doku.php?id=ecr.
@article{Yang06042012,
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 from next-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. The evaluation tool kit is publicly available at: http://aluru-sun.ece.iastate.edu/doku.php?id=ecr.},
added-at = {2012-04-28T09:10:34.000+0200},
author = {Yang, Xiao and Chockalingam, Sriram P. and Aluru, Srinivas},
biburl = {https://www.bibsonomy.org/bibtex/2596d30f7723666e6f51090ebefead6f1/ytyoun},
doi = {10.1093/bib/bbs015},
interhash = {cc83fb4915c82b8cf395c0941c788591},
intrahash = {596d30f7723666e6f51090ebefead6f1},
journal = {Briefings in Bioinformatics},
keywords = {bioinformatics error-correction short-reads},
timestamp = {2016-06-14T13:25:03.000+0200},
title = {A survey of error-correction methods for next-generation sequencing},
year = 2012
}