HECIL: A Hybrid Error Correction Algorithm for Long Reads with Iterative Learning.
HECIL: A Hybrid Error Correction Algorithm for Long Reads with Iterative Learning.
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DOI：
10.1038/s41598-018-28364-3
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发表时间：
2018-07-02
影响因子：
4.6
通讯作者：
Emrich SJ
中科院分区：
文献类型：
--
作者：
Choudhury O;Chakrabarty A;Emrich SJ
第二代DNA测序技术产生短读段，其可导致片段化的基因组组装。第三代测序平台通过产生跨越复杂和重复区域的更长读段来缓解这种限制。然而，由于高测序错误率，这种长读段的有用性受到限制。为了充分利用这些较长读取的潜力，必须纠正潜在的错误。我们提出了HECIL-混合纠错与迭代学习-一个混合纠错框架，确定错误的长读的校正策略，基于从短读比对获得的决策权重的最佳组合。我们证明，HECIL优于国家的最先进的纠错算法的绝大多数评估指标的多样化，现实世界的数据集，包括E。coli、S. cerevisiae和疟疾媒介蚊A. funestus。此外，我们提供了一个可选的途径，提高HECIL的核心算法的性能，通过引入一个迭代学习范式，提高了在每次迭代的校正策略，通过数据驱动的置信度指标分配给以前的校正，从以前的迭代中收集的知识。
Second-generation DNA sequencing techniques generate short reads that can result in fragmented genome assemblies. Third-generation sequencing platforms mitigate this limitation by producing longer reads that span across complex and repetitive regions. However, the usefulness of such long reads is limited because of high sequencing error rates. To exploit the full potential of these longer reads, it is imperative to correct the underlying errors. We propose HECIL—Hybrid Error Correction with Iterative Learning—a hybrid error correction framework that determines a correction policy for erroneous long reads, based on optimal combinations of decision weights obtained from short read alignments. We demonstrate that HECIL outperforms state-of-the-art error correction algorithms for an overwhelming majority of evaluation metrics on diverse, real-world data sets including E. coli, S. cerevisiae, and the malaria vector mosquito A. funestus. Additionally, we provide an optional avenue of improving the performance of HECIL’s core algorithm by introducing an iterative learning paradigm that enhances the correction policy at each iteration by incorporating knowledge gathered from previous iterations via data-driven confidence metrics assigned to prior corrections.
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DOI：
10.1049/ip-cta:19960244
发表时间：
1996-03-01
期刊：
IEE PROCEEDINGS-CONTROL THEORY AND APPLICATIONS
影响因子：
--
作者：
Amann, N;Owens, DH;Rogers, E
通讯作者：
Rogers, E
影响因子：
3.5
作者：
Schadt, Eric E.;Turner, Steve;Kasarskis, Andrew
通讯作者：
Kasarskis, Andrew
影响因子：
46.9
作者：
通讯作者：
--
影响因子：
12.3
作者：
Kelley DR;Schatz MC;Salzberg SL
通讯作者：
Salzberg SL
影响因子：
7
作者：
Goodwin S;Gurtowski J;Ethe-Sayers S;Deshpande P;Schatz MC;McCombie WR
通讯作者：
McCombie WR