A Convolutional Auto-Encoder for Haplotype Assembly and Viral Quasispecies Reconstruction
A Convolutional Auto-Encoder for Haplotype Assembly and Viral Quasispecies Reconstruction
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DOI：
10.1101/2020.09.29.318642
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发表时间：
2020-10
期刊：
影响因子：
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通讯作者：
Ziqi Ke;H. Vikalo
中科院分区：
文献类型：
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作者：
Ziqi Ke;H. Vikalo
单倍型组装和病毒准种重建是利用测序数据分析基因组混合物的具有挑战性的任务。高通量测序技术产生大量的短片段（读段），其基本上对混合物的组分进行过采样;表示冗余使得能够重建组分（单倍型、病毒株）。已知为NP困难的重建问题归结为将源自混合物中相同组分的读段分组在一起。现有的方法努力解决这个问题所需的精度和低运行时间的水平，随着组件的数量和长度的增加，这个问题变得越来越具有挑战性。本文提出了一种基于卷积自动编码器的读段聚类方法，该方法首先将排序的片段投影到低维空间，然后使用学习的嵌入特征估计读段起源的概率。通过找到聚集来自相同来源的读段的共有序列来重建组分。小批量随机梯度下降和读段的降维允许所提出的方法有效地处理大量的长读段。模拟，半实验和实验数据的实验表明，所提出的方法能够准确地重建单倍型和病毒准种，往往表现出上级性能相比，国家的最先进的方法。
Haplotype assembly and viral quasispecies reconstruction are challenging tasks concerned with analysis of genomic mixtures using sequencing data. High-throughput sequencing technologies generate enormous amounts of short fragments (reads) which essentially oversample components of a mixture; the representation redundancy enables reconstruction of the components (haplotypes, viral strains). The reconstruction problem, known to be NP-hard, boils down to grouping together reads originating from the same component in a mixture. Existing methods struggle to solve this problem with required level of accuracy and low runtimes; the problem is becoming increasingly more challenging as the number and length of the components increase. This paper proposes a read clustering method based on a convolutional auto-encoder designed to first project sequenced fragments to a low-dimensional space and then estimate the probability of the read origin using learned embedded features. The components are reconstructed by finding consensus sequences that agglomerate reads from the same origin. Mini-batch stochastic gradient descent and dimension reduction of reads allow the proposed method to efficiently deal with massive numbers of long reads. Experiments on simulated, semi-experimental and experimental data demonstrate the ability of the proposed method to accurately reconstruct haplotypes and viral quasispecies, often demonstrating superior performance compared to state-of-the-art methods.