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SeqAn是一个用于序列分析的高效、通用的C++库。

SeqAn an efficient, generic C++ library for sequence analysis.

作者信息

Döring Andreas, Weese David, Rausch Tobias, Reinert Knut

机构信息

Algorithmische Bioinformatik, Institut für Informatik, Takustr, 9, 14195 Berlin, Germany.

出版信息

BMC Bioinformatics. 2008 Jan 9;9:11. doi: 10.1186/1471-2105-9-11.

Abstract

BACKGROUND

The use of novel algorithmic techniques is pivotal to many important problems in life science. For example the sequencing of the human genome 1 would not have been possible without advanced assembly algorithms. However, owing to the high speed of technological progress and the urgent need for bioinformatics tools, there is a widening gap between state-of-the-art algorithmic techniques and the actual algorithmic components of tools that are in widespread use.

RESULTS

To remedy this trend we propose the use of SeqAn, a library of efficient data types and algorithms for sequence analysis in computational biology. SeqAn comprises implementations of existing, practical state-of-the-art algorithmic components to provide a sound basis for algorithm testing and development. In this paper we describe the design and content of SeqAn and demonstrate its use by giving two examples. In the first example we show an application of SeqAn as an experimental platform by comparing different exact string matching algorithms. The second example is a simple version of the well-known MUMmer tool rewritten in SeqAn. Results indicate that our implementation is very efficient and versatile to use.

CONCLUSION

We anticipate that SeqAn greatly simplifies the rapid development of new bioinformatics tools by providing a collection of readily usable, well-designed algorithmic components which are fundamental for the field of sequence analysis. This leverages not only the implementation of new algorithms, but also enables a sound analysis and comparison of existing algorithms.

摘要

背景

新型算法技术的应用对于生命科学中的许多重要问题至关重要。例如,如果没有先进的组装算法,人类基因组测序就不可能实现。然而,由于技术进步的速度很快以及对生物信息学工具的迫切需求,最先进的算法技术与广泛使用的工具的实际算法组件之间的差距正在不断扩大。

结果

为了纠正这种趋势,我们建议使用SeqAn,这是一个用于计算生物学中序列分析的高效数据类型和算法库。SeqAn包含现有实用的最先进算法组件的实现,为算法测试和开发提供了坚实的基础。在本文中,我们描述了SeqAn的设计和内容,并通过两个例子展示了它的用法。在第一个例子中,我们通过比较不同的精确字符串匹配算法,展示了SeqAn作为实验平台的应用。第二个例子是用SeqAn重写的著名MUMmer工具的简化版本。结果表明,我们的实现非常高效且用途广泛。

结论

我们预计SeqAn通过提供一系列易于使用、设计良好的算法组件,极大地简化了新生物信息学工具的快速开发,这些组件对于序列分析领域至关重要。这不仅有助于新算法的实现,还能对现有算法进行合理的分析和比较。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/540c/2246154/d5ed72690c61/1471-2105-9-11-1.jpg

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