AIMC Topic: Nanopore Sequencing

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ReorientExpress: reference-free orientation of nanopore cDNA reads with deep learning.

Genome biology
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...

De novo Nanopore read quality improvement using deep learning.

BMC bioinformatics
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...

ModiDeC: a multi-RNA modification classifier for direct nanopore sequencing.

Nucleic acids research
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...

ReQuant: improved base modification calling by k-mer value imputation.

Nucleic acids research
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...

Enhancing novel isoform discovery: leveraging nanopore long-read sequencing and machine learning approaches.

Briefings in functional genomics
Long-read sequencing technologies can capture entire RNA transcripts in a single sequencing read, reducing the ambiguity in constructing and quantifying transcript models in comparison to more common and earlier methods, such as short-read sequencing...

Precision DNA methylation typing via hierarchical clustering of Nanopore current signals and attention-based neural network.

Briefings in bioinformatics
Decoding DNA methylation sites through nanopore sequencing has emerged as a cutting-edge technology in the field of DNA methylation research, as it enables direct sequencing of native DNA molecules without the need for prior enzymatic or chemical tre...

m6ATM: a deep learning framework for demystifying the m6A epitranscriptome with Nanopore long-read RNA-seq data.

Briefings in bioinformatics
N6-methyladenosine (m6A) is one of the most abundant and well-known modifications in messenger RNAs since its discovery in the 1970s. Recent studies have demonstrated that m6A is involved in various biological processes, such as alternative splicing ...

NanoDeep: a deep learning framework for nanopore adaptive sampling on microbial sequencing.

Briefings in bioinformatics
Nanopore sequencers can enrich or deplete the targeted DNA molecules in a library by reversing the voltage across individual nanopores. However, it requires substantial computational resources to achieve rapid operations in parallel at read-time sequ...

[Unsupervised deep learning for identifying the O -carboxymethyl guanine by nanopore sequencing].

Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi
O -carboxymethyl guanine(O -CMG) is a highly mutagenic alkylation product of DNA that causes gastrointestinal cancer in organisms. Existing studies used mutant porin A (MspA) nanopore assisted by Phi29 DNA polymerase to localize it. Recently, machi...