DeepSimulator: a deep simulator for Nanopore sequencing
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Summary
A deep learning based simulator, DeepSimulator, to mimic the entire pipeline of Nanopore sequencing, which shows that the signals generated by this context-dependent model are more similar to the experimentally obtained signals than the ones generated by the official context-independent pore model.
- Type
- preprint
- Published
- 2017-12-22
- Cited by
- 89
- References
- 56
- Access
- Open access
- OpenAlex
- https://openalex.org/W2781737088
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:4844451
Keywords
Nanopore sequencing, Computer science, Context (archaeology), Workflow, Pipeline (software)
References
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- The dynamics of mitochondrial DNA heteroplasmy: implications for human health and disease
- Minimap and miniasm: fast mapping and de novo assembly for noisy long sequences
- DeepNano: Deep recurrent neural networks for base calling in MinION nanopore reads
- Fast and sensitive mapping of nanopore sequencing reads with GraphMap
- NanoSim: nanopore sequence read simulator based on statistical characterization
- Three decades of nanopore sequencing
- Scaffolding and completing genome assemblies in real-time with nanopore sequencing
Cited by
- SupportNet: a novel incremental learning framework through deep learning and support data
- SupportNet: solving catastrophic forgetting in class incremental learning with support data
- Featherweight long read alignment using partitioned reference indexes
- Accelerating Flash Calculation through Deep Learning Methods
- An accurate and rapid continuous wavelet dynamic time warping algorithm for end‐to‐end mapping in ultra‐long nanopore sequencing
- WaveNano: a signal-level nanopore base-caller via simultaneous prediction of nucleotide labels and move labels through bi-directional WaveNets
- Novel algorithms for efficient subsequence searching and mapping in nanopore raw signals towards targeted sequencing
- simuG: a general-purpose genome simulator
- mlDEEPre: Multi-Functional Enzyme Function Prediction With Hierarchical Multi-Label Deep Learning
- The bioinformatics tools for the genome assembly and analysis based on third-generation sequencing.
- Deep learning in bioinformatics: introduction, application, and perspective in big data era
- Trans-NanoSim characterizes and simulates nanopore RNA-seq data
- RACS: rapid analysis of ChIP-Seq data for contig based genomes
- Critical assessment of bioinformatics methods for the characterization of pathological repeat expansions with single-molecule sequencing data
- QAlign: Aligning nanopore reads accurately using current-level modeling
- SpecHap: a diploid phasing algorithm based on spectral graph theory
- Recognizing plasmid-reads by machine learning and K-mer statistics
- DNA for cold data archiving: using machine learning for robust decoding
- Technology and Species Independent Simulation of Sequencing Data and Genomic Variants
- Error correction enables use of Oxford Nanopore technology for reference-free transcriptome analysis
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