Chen Yang1,2, Justin Chu1,2, René L Warren1, Inanç Birol1,3,4. 1. Canada's Michael Smith Genome Science Centre, British Columbia Cancer Agency, 570 W 7th Avenue, V5Z 4S6 Vancouver, Canada. 2. Falculty of Science, University of British Columbia, Vancouver, Canada. 3. Department of Medical Genetics, University of British Columbia, Vancouver, Canada. 4. School of Computer Science, Simon Fraser University, Burnaby, Canada.
Abstract
Background: The MinION sequencing instrument from Oxford Nanopore Technologies (ONT) produces long read lengths from single-molecule sequencing - valuable features for detailed genome characterization. To realize the potential of this platform, a number of groups are developing bioinformatics tools tuned for the unique characteristics of its data. We note that these development efforts would benefit from a simulator software, the output of which could be used to benchmark analysis tools. Results: Here, we introduce NanoSim, a fast and scalable read simulator that captures the technology-specific features of ONT data and allows for adjustments upon improvement of nanopore sequencing technology. The first step of NanoSim is read characterization, which provides a comprehensive alignment-based analysis and generates a set of read profiles serving as the input to the next step, the simulation stage. The simulation stage uses the model built in the previous step to produce in silico reads for a given reference genome. NanoSim is written in Python and R. The source files and manual are available at the Genome Sciences Centre website: http://www.bcgsc.ca/platform/bioinfo/software/nanosim. Conclusion: In this work, we model the base-calling errors of ONT reads to inform the simulation of sequences with similar characteristics. We showcase the performance of NanoSim on publicly available datasets generated using the R7 and R7.3 chemistries and different sequencing kits and compare the resulting synthetic reads to those of other long-sequence simulators and experimental ONT reads. We expect NanoSim to have an enabling role in the field and benefit the development of scalable next-generation sequencing technologies for the long nanopore reads, including genome assembly, mutation detection, and even metagenomic analysis software.
Background: The MinION sequencing instrument from Oxford Nanopore Technologies (ONT) produces long read lengths from single-molecule sequencing - valuable features for detailed genome characterization. To realize the potential of this platform, a number of groups are developing bioinformatics tools tuned for the unique characteristics of its data. We note that these development efforts would benefit from a simulator software, the output of which could be used to benchmark analysis tools. Results: Here, we introduce NanoSim, a fast and scalable read simulator that captures the technology-specific features of ONT data and allows for adjustments upon improvement of nanopore sequencing technology. The first step of NanoSim is read characterization, which provides a comprehensive alignment-based analysis and generates a set of read profiles serving as the input to the next step, the simulation stage. The simulation stage uses the model built in the previous step to produce in silico reads for a given reference genome. NanoSim is written in Python and R. The source files and manual are available at the Genome Sciences Centre website: http://www.bcgsc.ca/platform/bioinfo/software/nanosim. Conclusion: In this work, we model the base-calling errors of ONT reads to inform the simulation of sequences with similar characteristics. We showcase the performance of NanoSim on publicly available datasets generated using the R7 and R7.3 chemistries and different sequencing kits and compare the resulting synthetic reads to those of other long-sequence simulators and experimental ONT reads. We expect NanoSim to have an enabling role in the field and benefit the development of scalable next-generation sequencing technologies for the long nanopore reads, including genome assembly, mutation detection, and even metagenomic analysis software.
Authors: Sara Goodwin; James Gurtowski; Scott Ethe-Sayers; Panchajanya Deshpande; Michael C Schatz; W Richard McCombie Journal: Genome Res Date: 2015-10-07 Impact factor: 9.043
Authors: Li Charlie Xia; Dongmei Ai; Hojoon Lee; Noemi Andor; Chao Li; Nancy R Zhang; Hanlee P Ji Journal: Gigascience Date: 2018-07-01 Impact factor: 6.524