AIMC Topic: Nanopore Sequencing

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Comprehensive benchmark and architectural analysis of deep learning models for nanopore sequencing basecalling.

Genome biology Apr 11, 2023
BACKGROUND: Nanopore-based DNA sequencing relies on basecalling the electric current signal. Basecalling requires neural networks to achieve competitive accuracies. To improve sequencing accuracy further, new models are continuously proposed with new...
Neural Networks, Computer Nanopore Sequencing Deep Learning Sequence Analysis, DNA
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SquiggleNet: real-time, direct classification of nanopore signals.

Genome biology Oct 27, 2021
We present SquiggleNet, the first deep-learning model that can classify nanopore reads directly from their electrical signals. SquiggleNet operates faster than DNA passes through the pore, allowing real-time classification and read ejection. Using 1 ...
Nanopore Sequencing Respiratory System Metagenome Humans DNA, Bacterial Long Interspersed Nucleotide Elements Deep Learning
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NanoCaller for accurate detection of SNPs and indels in difficult-to-map regions from long-read sequencing by haplotype-aware deep neural networks.

Genome biology Sep 6, 2021
Long-read sequencing enables variant detection in genomic regions that are considered difficult-to-map by short-read sequencing. To fully exploit the benefits of longer reads, here we present a deep learning method NanoCaller, which detects SNPs usin...
Nanopore Sequencing Chromosome Mapping Major Histocompatibility Complex Genome, Human INDEL Mutation Benchmarking Alleles Haplotypes Base Sequence High-Throughput Nucleotide Sequencing Neural Networks, Computer Humans Polymorphism, Single Nucleotide Nanoparticles
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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...
Software Deep Learning High-Throughput Nucleotide Sequencing Nanopores Sequence Analysis, DNA Nanopore Sequencing
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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...
Nanopores Nanopore Sequencing Software Consensus Humans Neural Networks, Computer
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Molecular barcoding of native RNAs using nanopore sequencing and deep learning.

Genome research Sep 9, 2020
Nanopore sequencing enables direct measurement of RNA molecules without conversion to cDNA, thus opening the gates to a new era for RNA biology. However, the lack of molecular barcoding of direct RNA nanopore sequencing data sets severely affects the...
Algorithms Deep Learning Nanopore Sequencing Sequence Analysis, RNA
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ReorientExpress: reference-free orientation of nanopore cDNA reads with deep learning.

Genome biology Nov 29, 2019
We describe ReorientExpress, a method to perform reference-free orientation of transcriptomic long sequencing reads. ReorientExpress uses deep learning to correctly predict the orientation of the majority of reads, and in particular when trained on a...
Nanopore Sequencing Sequence Analysis, DNA Software DNA, Complementary Deep Learning
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De novo Nanopore read quality improvement using deep learning.

BMC bioinformatics Nov 6, 2019
BACKGROUND: Long read sequencing technologies such as Oxford Nanopore can greatly decrease the complexity of de novo genome assembly and large structural variation identification. Currently Nanopore reads have high error rates, and the errors often c...
Nanopore Sequencing Quality Improvement Metagenome Humans Deep Learning Software Databases, Genetic
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ModiDeC: a multi-RNA modification classifier for direct nanopore sequencing.

Nucleic acids research Jul 19, 2025
RNA modifications play a crucial role in various cellular functions. Here, we present ModiDeC, a deep-learning-based classifier able to identify and distinguish multiple RNA modifications (N6-methyladenosine, inosine, pseudouridine, 2'-O-methylguanos...
Sequence Analysis, RNA Humans RNA RNA Processing, Post-Transcriptional Pseudouridine Adenosine Software Nanopore Sequencing Inosine Deep Learning HEK293 Cells
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ReQuant: improved base modification calling by k-mer value imputation.

Nucleic acids research May 10, 2025
Nanopore sequencing allows identification of base modifications, such as methylation, directly from raw current data. Prevailing approaches, including deep learning (DL) methods, require training data covering all possible sequence contexts. These da...
Humans Software DNA CpG Islands Sequence Analysis, DNA Algorithms DNA Methylation Nanopore Sequencing Deep Learning Bacteriophage lambda
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