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Nanopore SimulatION – a raw data simulator for Nanopore Sequencing

MPS-Authors
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Gießelmann,  Pay
Dept. of Genome Regulation (Head: Alexander Meissner), Max Planck Institute for Molecular Genetics, Max Planck Society;

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Brändl,  Björn
Dept. of Genome Regulation (Head: Alexander Meissner), Max Planck Institute for Molecular Genetics, Max Planck Society;

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Müller,  Franz-Josef
Cellular Phenotyping (Franz-Josef Müller), Dept. of Genome Regulation, (Head: Alexander Meissner), Max Planck Institute for Molecular Genetics, Max Planck Society;

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Rohrandt.pdf
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Citation

Rohrandt, C., Kraft, N., Gießelmann, P., Brändl, B., Schuldt, B. M., Jetzek, U., et al. (2019). Nanopore SimulatION – a raw data simulator for Nanopore Sequencing. In 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). doi:10.1109/BIBM.2018.8621253.


Cite as: http://hdl.handle.net/21.11116/0000-0003-8E1C-F
Abstract
Nanopore DNA sequencing enables the sequence determination of single DNA molecules up to 10,000 times longer than currently permitted by second-generation sequencing platforms. Nanopore sequencing gives real-time access to sequencing data and enables the detection of epigenetic modifications. This unique feature set is poised to foster the development of novel biomedical applications previously deemed unfeasible. Nanopore sequencing is based on picoampere scale measurement of current modulated by DNA or RNA polymers traveling through a nanometer opening between two compartments. Each of the five canonical nucleobases (A, T, G, C, U) has a characteristic electrical resistance, which ultimately enables the determination of the precise base sequence. However, a substantial computational effort is required to resolve the underlying sequence from a time-warped and noisy stream of digitized current measurements. Recently, a wide range of digital signal analysis and machine learning methods have been developed for Nanopore sequencing applications. Clinically relevant questions, such as the quantification of short repetitive DNA sequences remain an unresolved challenge to current generic, state-of-the-art nanopore data analysis methods. We believe realistic simulation of the signal stream can be instrumental in the development of tailored algorithms for such novel biomedical applications. Based on our work with the Oxford Nanopore Technologies MinION and PromethION platform, we have developed Nanopore SimulatION, a software package for the in silico generation of realistic, raw-signal-level data. Nanopore SimulatION starts from a reference genome in conjunction with a configuration and model file derived from real-world nanopore sequencing experiments as input. To validate our algorithm, we have sequenced custom synthetic DNA, and in so doing have generated a “ground-truth” data set potentially useful for downstream algorithm development. Additionally, we demonstrate Nanopore SimulatION` s utility for method development in typical clinical use cases.