The Broad institute, 7 Cambridge Center, Cambridge, MA 02142, USA.
Brief Bioinform. 2013 Jan;14(1):56-66. doi: 10.1093/bib/bbs015. Epub 2012 Apr 6.
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.
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.
纠错对于大多数下一代测序应用程序都很重要，因为高度准确的测序读取可能会导致更高质量的结果。近年来已经开发出许多用于从下一代平台测序数据纠错的技术。然而，与测序技术的快速发展相比，不同纠错方法缺乏标准化的评估程序，难以评估它们的优缺点。在本文中，我们全面回顾了许多纠错方法，并建立了一套通用的基准数据和评估标准，以进行比较评估。我们展示了几种纠错方法在质量、运行时间、内存使用和可扩展性方面的实验结果。除了为从业者提供有用的明确建议外，该综述还确定了当前的技术水平和未来研究的有前途的方向。
本文中使用的所有纠错程序都从托管网站下载。评估工具包可在以下网址获得：http://aluru-sun.ece.iastate.edu/doku.php?id=ecr。
