KAUST DepartmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Computer Science Program
Biological and Environmental Sciences and Engineering (BESE) Division
Computational Bioscience Research Center (CBRC)
Online Publication Date2018-04-06
Print Publication Date2018-09-01
Permanent link to this recordhttp://hdl.handle.net/10754/626751
MetadataShow full item record
AbstractOxford Nanopore sequencing is a rapidly developed sequencing technology in recent years. To keep pace with the explosion of the downstream data analytical tools, a versatile Nanopore sequencing simulator is needed to complement the experimental data as well as to benchmark those newly developed tools. However, all the currently available simulators are based on simple statistics of the produced reads, which have difficulty in capturing the complex nature of the Nanopore sequencing procedure, the main task of which is the generation of raw electrical current signals.Here we propose a deep learning based simulator, Deep- Simulator, to mimic the entire pipeline of Nanopore sequencing. Starting from a given reference genome or assembled contigs, we simulate the electrical current signals by a context-dependent deep learning model, followed by a base-calling procedure to yield simulated reads. This workflow mimics the sequencing procedure more naturally. The thorough experiments performed across four species show that the signals generated by our context-dependent model are more similar to the experimentally obtained signals than the ones generated by the official context-independent pore model. In terms of the simulated reads, we provide a parameter interface to users so that they can obtain the reads with different accuracies ranging from 83% to 97%. The reads generated by the default parameter have almost the same properties as the real data. Two case studies demonstrate the application of DeepSimulator to benefit the development of tools in de novo assembly and in low coverage SNP detection.The software can be accessed freely at: https://github.com/lykaust15/DeepSimulator.
CitationLi Y, Han R, Bi C, Li M, Wang S, et al. (2018) DeepSimulator: a deep simulator for Nanopore sequencing. Bioinformatics 34: 2899–2908. Available: http://dx.doi.org/10.1093/bioinformatics/bty223.
SponsorsWe thank Lachlan J.M. Coin, Louise Roddam, and Tania Duarte from University of Queensland for providing the nanopore sequencing data for the lambda phage, E. coli, and Pandoraea pnomenusa samples. This work was supported by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Awards No.FCC/1/1976-04, URF/1/2602-01, URF/1/3007-01, URF/1/3412-01 and URF/1/3450-01.
PublisherOxford University Press (OUP)
Except where otherwise noted, this item's license is described as This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact email@example.com