AIMC Topic: Nanopores
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Pair consensus decoding improves accuracy of neural network basecallers for nanopore sequencing.
Genome biology
Jan 19, 2021
We develop a general computational approach for improving the accuracy of basecalling with Oxford Nanopore's 1D and related sequencing protocols. Our software PoreOver ( https://github.com/jordisr/poreover ) finds the consensus of two neural networks...
Solid-State Nanopore Platform Integrated with Machine Learning for Digital Diagnosis of Virus Infection.
Analytical chemistry
Nov 30, 2020
Machine Learning to Improve the Sensing of Biomolecules by Conical Track-Etched Nanopore.
Biosensors
Oct 5, 2020
Single nanopore is a powerful platform to detect, discriminate and identify biomacromolecules. Among the different devices, the conical nanopores obtained by the track-etched technique on a polymer film are stable and easy to functionalize. However, ...
Performance of neural network basecalling tools for Oxford Nanopore sequencing.
Genome biology
Jun 24, 2019
BACKGROUND: Basecalling, the computational process of translating raw electrical signal to nucleotide sequence, is of critical importance to the sequencing platforms produced by Oxford Nanopore Technologies (ONT). Here, we examine the performance of ...
Detection of DNA base modifications by deep recurrent neural network on Oxford Nanopore sequencing data.
Nature communications
Jun 4, 2019
DNA base modifications, such as C5-methylcytosine (5mC) and N6-methyldeoxyadenosine (6mA), are important types of epigenetic regulations. Short-read bisulfite sequencing and long-read PacBio sequencing have inherent limitations to detect DNA modifica...
A multi-task convolutional deep neural network for variant calling in single molecule sequencing.
Nature communications
Mar 1, 2019
The accurate identification of DNA sequence variants is an important, but challenging task in genomics. It is particularly difficult for single molecule sequencing, which has a per-nucleotide error rate of ~5-15%. Meeting this demand, we developed Cl...
Deepbinner: Demultiplexing barcoded Oxford Nanopore reads with deep convolutional neural networks.
PLoS computational biology
Nov 20, 2018
Multiplexing, the simultaneous sequencing of multiple barcoded DNA samples on a single flow cell, has made Oxford Nanopore sequencing cost-effective for small genomes. However, it depends on the ability to sort the resulting sequencing reads by barco...
Recognition Tunneling of Canonical and Modified RNA Nucleotides for Their Identification with the Aid of Machine Learning.
ACS nano
Jun 28, 2018
In the present study, we demonstrate a tunneling nanogap technique to identify individual RNA nucleotides, which can be used as a mechanism to read the nucleobases for direct sequencing of RNA in a solid-state nanopore. The tunneling nanogap is compo...
Synthetic Ion Channels and DNA Logic Gates as Components of Molecular Robots.
Chemphyschem : a European journal of chemical physics and physical chemistry
Dec 8, 2017
A molecular robot is a next-generation biochemical machine that imitates the actions of microorganisms. It is made of biomaterials such as DNA, proteins, and lipids. Three prerequisites have been proposed for the construction of such a robot: sensors...
DeepNano: Deep recurrent neural networks for base calling in MinION nanopore reads.
PloS one
Jun 5, 2017
The MinION device by Oxford Nanopore produces very long reads (reads over 100 kBp were reported); however it suffers from high sequencing error rate. We present an open-source DNA base caller based on deep recurrent neural networks and show that the ...
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