CONNET: Accurate Genome Consensus in Assembling Nanopore Sequencing Data via Deep Learning

iScience

Tak-Wah Lam¹  Ruibang Luo²  Chi-Man Liu²  Henry C.M. Leung²  Yifan Zhang² 

[1] Corresponding author;Department of Computer Science, The University of Hong Kong, Hong Kong, China;

关键词: Genomics;    Bioinformatics;    Sequence Analysis;   

DOI  :  

来源: DOAJ

【 摘 要 】

Summary: Single-molecule sequencing technologies produce much longer reads compared with next-generation sequencing, greatly improving the contiguity of de novo assembly of genomes. However, the relatively high error rates in long reads make it challenging to obtain high-quality assemblies. A computationally intensive consensus step is needed to resolve the discrepancies in the reads. Efficient consensus tools have emerged in the recent past, based on partial-order alignment. In this study, we discovered that the spatial relationship of alignment pileup is crucial to high-quality consensus and developed a deep learning-based consensus tool, CONNET, which outperforms the fastest tools in terms of both accuracy and speed. We tested CONNET using a 90× dataset of E. coli and a 37× human dataset. In addition to achieving high-quality consensus results, CONNET is capable of delivering phased diploid genome consensus. Diploid consensus on the above-mentioned human assembly further reduced 12% of the consensus errors made in the haploid results.

【 授权许可】

Unknown