Nanopore SimulatION – a raw data simulator for Nanopore Sequencing

Rohrandt, Christian; Kraft, Nadine; Gieselmann, Pay; Brändl, Björn; Schuldt, Bernhard; Jetzek, Ulrich; Muller, F.P.J.

doi:10.1109/bibm.2018.8621253

Cited by 5 publications

(4 citation statements)

References 20 publications

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“…Repeat simulation with Nanopore SimulatION. For development and validation of the in silico experimental setup, repeats were simulated with the Nanopore SimulatION tool (https://github.com/crohrandt/nanopore_simulation) 33 . This tool takes a reference genome fasta file and a configuration (.sic file) from a previous real nanopore experiment as input for a realistic raw signal simulation of nanopore sequencing data.…”

Section: Methodsmentioning

confidence: 99%

Analysis of short tandem repeat expansions and their methylation state with nanopore sequencing

et al. 2019

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Section: Methodsmentioning

confidence: 99%

Analysis of short tandem repeat expansions and their methylation state with nanopore sequencing

et al. 2019

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“…The interesting rationale of this choice is that of opening to the development and testing of base callers. Among the most valuable methods we mention [20] and [21]. It worth citing FASTQSim [22] which is the only attempt to simulate NGS data both for short and long reads (at least PacBio SMRT).…”

Section: Related Workmentioning

confidence: 99%

Technology and Species Independent Simulation of Sequencing Data and Genomic Variants

Geraci

Massidda

Pisanti

2019

2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE)

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Highly accurate genotyping is essential for genomic projects aimed at understanding the etiology of diseases as well as for routinary screening of patients. For this reason, genotyping software packages are subject to a strict validation process that requires a large amount of sequencing data endowed with accurate genotype information. In-vitro assessment of genotyping is a long, complex and expensive activity that also depends on the specific variation and locus, and thus it cannot really be used for validation of in-silico genotyping algorithms. In this scenario, sequencing simulation has emerged as a practical alternative. Simulators must be able to keep up with the continuous improvement of different sequencing technologies producing datasets as much indistinguishable from real ones as possible. Moreover, they must be able to mimic as many types of genomic variant as possible. In this paper we describe OmniSim: a simulator whose ultimate goal is that of being suitable in all the possible applicative scenarios. In order to fulfill this goal, OmniSim uses an abstract model where variations are read from a .vcf file and mapped into edit operations (insertion, deletion, substitution) on the reference genome. Technological parameters (e.g. error distributions, read length and per-base quality) are learned from real data. As a result of the combination of our abstract model and parameter learning module, OmniSim is able to output data in all aspects similar to that produced in a real sequencing experiment.

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“…NanosigSim [33] improves the signal generator module of Deep Simulator using a bidirectional gated recurrent unit (GRU). Nanopore SimulatION [34] is a modular software tool capable of simulating the raw current values of Nanopore sequencing reads. Schwarz et al [35] simulated error sequences with pre-defined error probability, which needs to be specified by an user.…”

Section: Introductionmentioning

confidence: 99%

Generative Adversarial Networks for DNA Storage Channel Simulator

et al. 2023

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DNA data storage systems have rapidly developed with novel error-correcting techniques, random access algorithms, and query systems. However, designing an algorithm for DNA storage systems is challenging, mainly due to the unpredictable nature of errors and the extremely high price of experiments. Thus, a simulator is of interest that can imitate the error statistics of a DNA storage system and replace the experiments in developing processes. We introduce novel generative adversarial networks that learn DNA storage channel statistics. Our simulator takes oligos (DNA sequences to write) as an input and generates a FASTQ file that includes output DNA reads and quality scores as if the oligos are synthesized and sequenced. We trained the proposed simulator with data from a single experiment consisting of 14,400 input oligo strands and 12,108,573 output reads. The error statistics between the input and the output of the trained generator match the actual error statistics, including the error rate at each position, the number of errors for each nucleotide, and high-order statistics. The code is available at https://github.com/gyfbianhuanyun/DNA_storage_simulator_GAN. INDEX TERMS channel simulator, DNA storage, generative adversarial networks, recurrent neural networks, transformer.

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Nanopore SimulatION – a raw data simulator for Nanopore Sequencing

Cited by 5 publications

References 20 publications

Analysis of short tandem repeat expansions and their methylation state with nanopore sequencing

Analysis of short tandem repeat expansions and their methylation state with nanopore sequencing

Technology and Species Independent Simulation of Sequencing Data and Genomic Variants

Generative Adversarial Networks for DNA Storage Channel Simulator

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