AIMC Topic: Nanopore Sequencing
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ConsensuSV-ONT - A modern method for accurate structural variant calling.
Scientific reports
May 17, 2025
Improvements in sequencing technology make the development of new tools for detection of structural variance more and more common. However, since the tools available for the long-read Oxford Nanopore sequencing are limited, and the selection of the o...
LevSeq: Rapid Generation of Sequence-Function Data for Directed Evolution and Machine Learning.
ACS synthetic biology
Dec 24, 2024
Sequence-function data provides valuable information about the protein functional landscape but is rarely obtained during directed evolution campaigns. Here, we present Long-read every variant Sequencing (LevSeq), a pipeline that combines a dual barc...
A mapping-free natural language processing-based technique for sequence search in nanopore long-reads.
BMC bioinformatics
Nov 13, 2024
BACKGROUND: In unforeseen situations, such as nuclear power plant's or civilian radiation accidents, there is a need for effective and computationally inexpensive methods to determine the expression level of a selected gene panel, allowing for rough ...
Adapting nanopore sequencing basecalling models for modification detection via incremental learning and anomaly detection.
Nature communications
Aug 21, 2024
We leverage machine learning approaches to adapt nanopore sequencing basecallers for nucleotide modification detection. We first apply the incremental learning (IL) technique to improve the basecalling of modification-rich sequences, which are usuall...
Digital telomere measurement by long-read sequencing distinguishes healthy aging from disease.
Nature communications
Jun 18, 2024
Telomere length is an important biomarker of organismal aging and cellular replicative potential, but existing measurement methods are limited in resolution and accuracy. Here, we deploy digital telomere measurement (DTM) by nanopore sequencing to un...
Effective training of nanopore callers for epigenetic marks with limited labelled data.
Open biology
Jun 12, 2024
Nanopore sequencing platforms combined with supervised machine learning (ML) have been effective at detecting base modifications in DNA such as 5-methylcytosine (5mC) and N6-methyladenine (6mA). These ML-based nanopore callers have typically been tra...
Quantitative profiling N1-methyladenosine (m1A) RNA methylation from Oxford nanopore direct RNA sequencing data.
Methods (San Diego, Calif.)
May 18, 2024
With the recent advanced direct RNA sequencing technique that proposed by the Oxford Nanopore Technologies, RNA modifications can be detected and profiled in a simple and straightforward manner. Majority nanopore-based modification studies were devot...
Nm-Nano: a machine learning framework for transcriptome-wide single-molecule mapping of 2´-O-methylation (Nm) sites in nanopore direct RNA sequencing datasets.
RNA biology
May 17, 2024
2´-O-methylation (Nm) is one of the most abundant modifications found in both mRNAs and noncoding RNAs. It contributes to many biological processes, such as the normal functioning of tRNA, the protection of mRNA against degradation by the decapping a...
A signal processing and deep learning framework for methylation detection using Oxford Nanopore sequencing.
Nature communications
Feb 16, 2024
Oxford Nanopore sequencing can detect DNA methylations from ionic current signal of single molecules, offering a unique advantage over conventional methods. Additionally, adaptive sampling, a software-controlled enrichment method for targeted sequenc...
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...
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