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Nanopore base-calling from a perspective of instance segmentation

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/978-3-319-24574-4_28openalex-snapshot

U-Net: Convolutional Networks for Biomedical Image Segmentation

inferredcites →https://doi.org/10.1038/ncomms11307openalex-snapshot

Fast and sensitive mapping of nanopore sequencing reads with GraphMap

inferredcites →https://doi.org/10.1093/bib/bbx062openalex-snapshot

Nanopore sequencing data analysis: state of the art, applications and challenges

inferredcites →https://doi.org/10.1093/bioinformatics/btw152openalex-snapshot

Minimap and miniasm: fast mapping and de novo assembly for noisy long sequences

inferredcites →https://doi.org/10.1093/bioinformatics/btw569openalex-snapshot

Nanocall: an open source basecaller for Oxford Nanopore sequencing data

inferredcites →https://doi.org/10.1093/gigascience/giy037openalex-snapshot

Chiron: translating nanopore raw signal directly into nucleotide sequence using deep learning

inferredcites →https://doi.org/10.1101/094672openalex-snapshot

De novo Identification of DNA Modifications Enabled by Genome-Guided Nanopore Signal Processing

inferredcites →https://doi.org/10.1101/133058openalex-snapshot

BasecRAWller: Streaming Nanopore Basecalling Directly from Raw Signal

inferredcites →https://doi.org/10.1186/s13059-018-1462-9openalex-snapshot

From squiggle to basepair: computational approaches for improving nanopore sequencing read accuracy

inferredcites →https://doi.org/10.1371/journal.pone.0178751openalex-snapshot

DeepNano: Deep recurrent neural networks for base calling in MinION nanopore reads

inferredcites →https://doi.org/10.4230/oasics.dx.2026.15openalex-snapshot

AutoResearch Lite: a reproducible paper-inspired experiment runner (Adam mechanism reproduction and convergence-speed comparison)

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.1093/bioinformatics/btad272openalex-snapshot

RawHash: enabling fast and accurate real-time analysis of raw nanopore signals for large genomes

inferred← citeshttps://doi.org/10.1101/2022.11.20.517297openalex-snapshot

A Framework for Designing Efficient Deep Learning-Based Genomic Basecallers

inferred← citeshttps://doi.org/10.1101/2025.10.04.680405openalex-snapshot

RawBench: A Comprehensive Benchmarking Framework for Raw Nanopore Signal Analysis Techniques

inferred← citeshttps://doi.org/10.21203/rs.3.rs-3015491/v1openalex-snapshot

Aptamer-functionalized interface nanopores enable amino acid-specific single-molecule proteomics

inferred← citeshttps://doi.org/10.21203/rs.3.rs-783575/v1openalex-snapshot

Optimisation of rapid untargeted nanopore DNA virus metagenomics using cell cultures and calves experimentally infected with bovine herpes virus-1

inferred← citeshttps://doi.org/10.3389/fgene.2024.1429306openalex-snapshot

TargetCall: eliminating the wasted computation in basecalling via pre-basecalling filtering

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