Efficient assembly of nanopore reads via highly accurate and intact error correction

Ying Chen, Fan Nie, Shang-Qian Xie, Ying-Feng Zheng, Qi Dai, Thomas Bray, Yao-Xin Wang, Jian-Feng Xing, Zhi-Jian Huang, Wang De-Peng, Li-Juan He, Feng Luo (), Jian-Xin Wang (), Yi-Zhi Liu () and Chuan-Le Xiao ()
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Ying Chen: Sun Yat-sen University
Fan Nie: Central South University
Shang-Qian Xie: Hainan University
Ying-Feng Zheng: Sun Yat-sen University
Qi Dai: Zhejiang Sci-Tech University
Thomas Bray: Oxford Nanopore Technologies
Yao-Xin Wang: Zhejiang Sci-Tech University
Jian-Feng Xing: Hainan University
Zhi-Jian Huang: Sun Yat-sen University
Wang De-Peng: Nextomics Biosciences Co., Ltd
Li-Juan He: Sun Yat-sen University
Feng Luo: Clemson University
Jian-Xin Wang: Central South University
Yi-Zhi Liu: Sun Yat-sen University
Chuan-Le Xiao: Sun Yat-sen University

Nature Communications, 2021, vol. 12, issue 1, 1-10

Abstract: Abstract Long nanopore reads are advantageous in de novo genome assembly. However, nanopore reads usually have broad error distribution and high-error-rate subsequences. Existing error correction tools cannot correct nanopore reads efficiently and effectively. Most methods trim high-error-rate subsequences during error correction, which reduces both the length of the reads and contiguity of the final assembly. Here, we develop an error correction, and de novo assembly tool designed to overcome complex errors in nanopore reads. We propose an adaptive read selection and two-step progressive method to quickly correct nanopore reads to high accuracy. We introduce a two-stage assembler to utilize the full length of nanopore reads. Our tool achieves superior performance in both error correction and de novo assembling nanopore reads. It requires only 8122 hours to assemble a 35X coverage human genome and achieves a 2.47-fold improvement in NG50. Furthermore, our assembly of the human WERI cell line shows an NG50 of 22 Mbp. The high-quality assembly of nanopore reads can significantly reduce false positives in structure variation detection.

Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-020-20236-7

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DOI: 10.1038/s41467-020-20236-7

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