NeuralPolish: a novel Nanopore polishing method based on alignment matrix construction and orthogonal Bi-GRU Networks
NeuralPolish: a novel Nanopore polishing method based on alignment matrix construction and orthogonal Bi-GRU Networks
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DOI：
10.1093/bioinformatics/btab354
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发表时间：
2021-05-11
期刊：
影响因子：
5.8
通讯作者：
Wang, Jianxin
中科院分区：
文献类型：
--
作者：
Huang, Neng;Nie, Fan;Wang, Jianxin
动机：低成本的牛津纳米孔测序在基因组学研究中取得了许多突破。然而，纳米孔基因组组装的大量错误影响了基因组分析的准确性。抛光是一种纠正基因组组装错误的过程，可以提高下游分析的可靠性。然而，现有的抛光方法的性能仍然不令人满意。结果：我们开发了一种基于对齐矩阵构建和正交Bi-GRU网络的新型抛光方法NeuralPolish来校正装配体的误差。在该方法中，我们设计了一个表示从读取到装配的对齐的特征矩阵。矩阵的每一行表示一个读取，每一列表示在序列的每个位置上对齐的碱基。在网络架构中，采用双向GRU网络，通过逐行处理对齐矩阵，提取每次读取的序列信息。之后，将特征矩阵通过另一个双向GRU网络逐列处理，计算概率分布。最后，CTC解码器使用贪婪算法生成抛光序列。我们使用5个真实数据集和Wtdbg2、Flye和Canu 3种装配工具进行测试，并比较了不同抛光方法（NeuralPolish、Racon、MarginPolish、HELEN和Medaka）的结果。综合实验表明，与其他抛光方法相比，NeuralPolish可以实现更精确的装配，误差更小，并且可以提高不同装配者获得的装配精度。
Motivation: Oxford Nanopore sequencing producing long reads at low cost has made many breakthroughs in genomics studies. However, the large number of errors in Nanopore genome assembly affect the accuracy of genome analysis. Polishing is a procedure to correct the errors in genome assembly and can improve the reliability of the downstream analysis. However, the performances of the existing polishing methods are still not satisfactory.Results: We developed a novel polishing method, NeuralPolish, to correct the errors in assemblies based on alignment matrix construction and orthogonal Bi-GRU networks. In this method, we designed an alignment feature matrix for representing read-to-assembly alignment. Each row of the matrix represents a read, and each column represents the aligned bases at each position of the contig. In the network architecture, a bi-directional GRU network is used to extract the sequence information inside each read by processing the alignment matrix row by row. After that, the feature matrix is processed by another bi-directional GRU network column by column to calculate the probability distribution. Finally, a CTC decoder generates a polished sequence with a greedy algorithm. We used five real datasets and three assembly tools including Wtdbg2, Flye and Canu for testing, and compared the results of different polishing methods including NeuralPolish, Racon, MarginPolish, HELEN and Medaka. Comprehensive experiments demonstrate that NeuralPolish achieves more accurate assembly with fewer errors than other polishing methods and can improve the accuracy of assembly obtained by different assemblers.