A survey of error-correction methods for next-generation sequencing
A survey of error-correction methods for next-generation sequencing
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DOI：
10.1093/bib/bbs015
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发表时间：
2013-01-01
影响因子：
9.5
通讯作者：
Aluru, Srinivas
中科院分区：
文献类型：
--
作者：
Yang, Xiao;Chockalingam, Sriram P.;Aluru, Srinivas
错误校正对于大多数下一代测序应用非常重要，因为高度准确的测序读数可能会导致更高质量的结果。近年来，已经开发了许多用于对来自下一代平台的测序数据进行纠错的技术。然而，相对于测序技术的快速发展，目前还缺乏对不同纠错方法的标准化评价程序，难以评估它们的相对优缺点。在这篇文章中，我们提供了一个全面的审查，许多纠错方法，并建立了一套共同的基准数据和评估标准，以提供一个比较评估。我们目前的实验结果的质量，运行时间，内存使用和可扩展性的几个纠错方法。除了提供明确的建议有用的从业人员，审查服务，以确定当前的最先进的和有前途的未来研究方向。可用性：本文中使用的所有纠错程序都是从托管网站下载的。评估工具包可在以下网址公开获取：www.example.com id = ecr。
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.