Deep learning for assembly of haplotypes and viral quasispecies from short and long sequencing reads
Deep learning for assembly of haplotypes and viral quasispecies from short and long sequencing reads
复制标题
DOI：
10.1145/3535508.3545524
复制
发表时间：
2022-08
期刊：
影响因子：
--
通讯作者：
Ziqi Ke;H. Vikalo
中科院分区：
文献类型：
--
作者：
Ziqi Ke;H. Vikalo
关于个体基因组或病毒群体中遗传变异的信息提供了对疾病遗传特征的深入了解，并为医学和制药研究提供了方向。最先进的测序平台产生大量的读段，其长度因技术而异，提供了重建单倍型和病毒准种所需的数据。一方面，高通量平台能够提供大量高度准确但相对较短的读数;无法桥接长的遗传距离使得用这种读数进行重建具有挑战性。另一方面，最新一代测序技术能够产生长得多的读数，但这些读数遭受测序错误的比率高于短读数的错误率。这激发了对能够利用短读段的高准确性和长读段的相位分辨能力的重建方法的搜索。我们提出了一个深度学习框架，该框架依赖于具有聚类层的卷积自动编码器来从混合数据源中重建单个单倍型或病毒种群。首先，将用于从短读段进行单体型组装/病毒群体重构的自动编码器与利用长读段进行相同任务的另一个自动编码器分开进行预训练。然后同时重新训练预训练的模型以实现决策融合。现实的合成以及实验数据的结果表明，所提出的框架优于国家的最先进的单倍型组装和病毒准种重建的技术，并实现显着更高的准确性，这些任务比方法只利用一种类型的读取。代码可在https://github.com/WuLoli/HybSeq上获得。
Information about genetic variations in either individual genomes or viral populations provides insight in genetic signatures of diseases and suggests directions for medical and pharmaceutical research. State-of-the-art sequencing platforms generate massive amounts of reads, with length varying from one technology to another, that provide data needed for the reconstruction of haplotypes and viral quasispecies. On the one hand, high-throughput platforms are capable of providing enormous amounts of highly accurate but relatively short reads; inability to bridge long genetic distances renders the reconstruction with such reads challenging. On the other hand, the latest generation of sequencing technologies is capable of generating much longer reads but those reads suffer from sequencing errors at a rate higher than the error rate of short reads. This motivates search for reconstruction methods capable of leveraging both the high accuracy of short reads and the phase resolving power of long reads. We present a deep learning framework that relies on convolutional auto-encoders with a clustering layer to reconstruct individual haplotypes or viral populations from hybrid data sources. First, an auto-encoder for haplotype assembly / viral population reconstruction from short reads is pre-trained separately from another one utilizing long reads for the same task. The pre-trained models are then retrained simultaneously to enable decision fusion. Results on realistic synthetic as well as experimental data demonstrate that the proposed framework outperforms state-of-the-art techniques for haplotype assembly and viral quasispecies reconstruction, and achieves significantly higher accuracy on those tasks than methods utilizing only one type of reads. Code is available at https://github.com/WuLoli/HybSeq.