A survey of sequence alignment algorithms for next-generation sequencing
A survey of sequence alignment algorithms for next-generation sequencing
复制标题
DOI：
10.1093/bib/bbq015
复制
发表时间：
2010-09-01
影响因子：
9.5
通讯作者：
Homer, Nils
中科院分区：
文献类型：
--
作者：
Li, Heng;Homer, Nils
快速发展的测序技术以无与伦比的规模产生数据。分析这些数据的一个核心挑战是序列比对，其中必须将序列读数与参考进行比较。在过去两年中，随后开发了各种各样的比对算法和软件。在本文中，我们将系统地回顾这些算法的发展现状，并介绍它们在不同类型的实验数据上的实际应用。我们得出结论，短读比对不再是数据分析的瓶颈。我们还考虑了未来的发展比对算法方面出现的长序列读取和云计算的前景。
Rapidly evolving sequencing technologies produce data on an unparalleled scale. A central challenge to the analysis of this data is sequence alignment, whereby sequence reads must be compared to a reference. A wide variety of alignment algorithms and software have been subsequently developed over the past two years. In this article, we will systematically review the current development of these algorithms and introduce their practical applications on different types of experimental data. We come to the conclusion that short-read alignment is no longer the bottleneck of data analyses. We also consider future development of alignment algorithms with respect to emerging long sequence reads and the prospect of cloud computing.