ArticleSensors (Basel, Switzerland)2020
Simulation of Nanopore Sequencing Signals Based on BiGRU.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
4 citing papers in PubMed.
- NanoSimFormer: an end-to-end transformer-based nanopore signal simulator with basecaller guidance.Bioinformatics (Oxford, England) · 2026Article
- End-to-end simulation of nanopore sequencing signals with feed-forward transformers.Bioinformatics (Oxford, England) · 2024Article
- Basecalling Using Joint Raw and Event Nanopore Data Sequence-to-Sequence Processing.Sensors (Basel, Switzerland) · 2022Article
- Chromosome-scale assembly of the Sparassis latifolia genome obtained using long-read and Hi-C sequencing.G3 (Bethesda, Md.) · 2021Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Oxford Nanopore sequencing is an important sequencing technology, which reads the nucleotide sequence by detecting the electrical current signal changes when DNA molecule is forced to pass through a biological nanopore. The research on signal simulation of nanopore sequencing is highly desirable for method developments of nanopore sequencing applications. To improve the simulation accuracy, we propose a novel signal simulation method based on Bi-directional Gated Recurrent Units (BiGRU). In this method, the signal processing model based on BiGRU is built to replace the traditional low-pass filter to post-process the ground-truth signal calculated by the input nucleotide sequence and nanopore sequencing pore model. Gaussian noise is then added to the filtered signal to generate the final simulated signal. This method can accurately model the relation between ground-truth signal and real-world sequencing signal through experimental sequencing data. The simulation results reveal that the proposed method utilizing the powerful learning ability of the neural network can generate the simulated signal that is closer to the real-world sequencing signal in the time and frequency domains than the existing simulation method.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.