AIMC Topic: Nanopores
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Combining machine learning and nanopore construction creates an artificial intelligence nanopore for coronavirus detection.
Nature communications
Jun 17, 2021
High-throughput, high-accuracy detection of emerging viruses allows for the control of disease outbreaks. Currently, reverse transcription-polymerase chain reaction (RT-PCR) is currently the most-widely used technology to diagnose the presence of SAR...
DNAscent v2: detecting replication forks in nanopore sequencing data with deep learning.
BMC genomics
Jun 9, 2021
BACKGROUND: Measuring DNA replication dynamics with high throughput and single-molecule resolution is critical for understanding both the basic biology behind how cells replicate their DNA and how DNA replication can be used as a therapeutic target f...
Deep Learning-Enhanced Nanopore Sensing of Single-Nanoparticle Translocation Dynamics.
Small methods
May 14, 2021
Noise is ubiquitous in real space that hinders detection of minute yet important signals in electrical sensors. Here, the authors report on a deep learning approach for denoising ionic current in resistive pulse sensing. Electrophoretically-driven tr...
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...
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