Error correction of high-throughput sequencing datasets with non-uniform coverage.

Error correction of high-throughput sequencing datasets with non-uniform coverage.
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DOI:
10.1093/bioinformatics/btr208
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发表时间:
2011-07-01
期刊:
Bioinformatics (Oxford, England)
影响因子:
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通讯作者:
Pevzner P
Pevzner P
中科院分区:
其他
文献类型:
--
作者:
Medvedev P;Scott E;Kakaradov B;Pevzner P

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动机:高通量测序(HTS)平台的持续改进已经开始展现出无数新的应用。因此,测序读数的纠错仍然是一个重要的问题。尽管有几个工具在对读数进行接近均匀采样的数据集的校正工作上做得很好,但校正来自严重不一致的数据集的读数的问题仍然悬而未决。结果:在本文中,我们在没有任何一致性假设的情况下,发展了Hammer纠错方法。HAMMER基于海明图和简单的测序误差概率模型的组合。它是一种简单且自适应的算法,在非均匀单元格数据上改进了其他工具,而在正常的多单元格数据上取得了类似的结果。可用性:http://www.cs.toronto.edu/~pashadag.联系方式:pmedvedev@cs.ucsd.edu
Motivation: The continuing improvements to high-throughput sequencing (HTS) platforms have begun to unfold a myriad of new applications. As a result, error correction of sequencing reads remains an important problem. Though several tools do an excellent job of correcting datasets where the reads are sampled close to uniformly, the problem of correcting reads coming from drastically non-uniform datasets, such as those from single-cell sequencing, remains open. Results: In this article, we develop the method Hammer for error correction without any uniformity assumptions. Hammer is based on a combination of a Hamming graph and a simple probabilistic model for sequencing errors. It is a simple and adaptable algorithm that improves on other tools on non-uniform single-cell data, while achieving comparable results on normal multi-cell data. Availability: http://www.cs.toronto.edu/~pashadag. Contact: pmedvedev@cs.ucsd.edu
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