Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

RNA-seq03:21

RNA-seq

11.6K
RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...
11.6K
Next-generation Sequencing03:00

Next-generation Sequencing

97.3K
The first human genome sequencing project cost $2.7 billion and was declared complete in 2003, after 15 years of international cooperation and collaboration between several research teams and funding agencies. Today, with the advent of next-generation sequencing technologies, the cost and time of sequencing a human genome have dropped over 100 fold.
Next-Generation Sequencing Methods
Although all next-generation methods use different technologies, they all share a set of standard features....
97.3K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Explaining hidden mechanisms: a generative model for causal graphs with nonlinear latent factors.

Frontiers in artificial intelligence·2026
Same author

Correction: Machine learning model for predicting the cold-heat pattern in Kampo medicine: a multicenter prospective observational study.

Frontiers in pharmacology·2026
Same author

Case Report: Mixed ductal-lobular carcinoma consisting of invasive lobular carcinoma with a glycogen-rich clear cell pattern and elevated tumor mutation burden.

Frontiers in oncology·2026
Same author

Dual knockout of Fas and TCRα in Jurkat reporter cells enables highly sensitive identification of antigen-specific TCRs.

Biochemical and biophysical research communications·2026
Same author

Functional and structural analysis of KK-LC-1-specific T cell receptors from patients with lung Cancer for immunotherapy.

Cellular immunology·2026
Same author

The difference in tumor immune microenvironment and IFN-γ response between solid and non-solid adenocarcinoma.

Translational lung cancer research·2025
Same journal

OpenIMC: an open-source platform for analyzing single-cell and spatial proteomics by imaging mass cytometry.

BMC bioinformatics·2026
Same journal

NAP: an open source pipeline for cross-domain microbiome profiling using Nanopore sequencing-derived amplicon data.

BMC bioinformatics·2026
Same journal

SurvGME: an R package for survival analysis with graphical and measurement error models.

BMC bioinformatics·2026
Same journal

SimMapNet: a Bayesian framework for gene regulatory network inference using gene ontology similarities as external hint.

BMC bioinformatics·2026
Same journal

Dual channel drug-drug interactions extraction based on cross attention.

BMC bioinformatics·2026
Same journal

FeSseqdb: a curated sequence-level database and interpretable machine learning framework for identifying iron-sulfur proteins.

BMC bioinformatics·2026
See all related articles

Related Experiment Video

Updated: Dec 23, 2025

Sequencing of mRNA from Whole Blood using Nanopore Sequencing
11:26

Sequencing of mRNA from Whole Blood using Nanopore Sequencing

Published on: June 3, 2019

14.5K

Nanopore basecalling from a perspective of instance segmentation.

Yao-Zhong Zhang1, Arda Akdemir1, Georg Tremmel1

  • 1The Institute of Medical Science, The University of Tokyo, Shirokanedai 4-6-1, Minato-ku, Tokyo, 108-8639, Japan.

BMC Bioinformatics
|April 24, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces URnano, a novel nanopore sequencing basecalling method using instance segmentation. URnano achieves competitive accuracy by treating basecalling as a 1D segmentation task, improving upon existing deep learning approaches.

Keywords:
Deep learningNanopore basecallingUR-net

More Related Videos

Nanopore DNA Sequencing for Metagenomic Soil Analysis
07:33

Nanopore DNA Sequencing for Metagenomic Soil Analysis

Published on: December 14, 2017

31.5K
Validating Whole Genome Nanopore Sequencing, using Usutu Virus as an Example
05:45

Validating Whole Genome Nanopore Sequencing, using Usutu Virus as an Example

Published on: March 11, 2020

9.2K

Related Experiment Videos

Last Updated: Dec 23, 2025

Sequencing of mRNA from Whole Blood using Nanopore Sequencing
11:26

Sequencing of mRNA from Whole Blood using Nanopore Sequencing

Published on: June 3, 2019

14.5K
Nanopore DNA Sequencing for Metagenomic Soil Analysis
07:33

Nanopore DNA Sequencing for Metagenomic Soil Analysis

Published on: December 14, 2017

31.5K
Validating Whole Genome Nanopore Sequencing, using Usutu Virus as an Example
05:45

Validating Whole Genome Nanopore Sequencing, using Usutu Virus as an Example

Published on: March 11, 2020

9.2K

Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Nanopore sequencing offers real-time, long-read DNA/RNA analysis via portable devices.
  • Current nanopore basecalling accuracy (85-95%) lags behind short-read methods, despite deep learning advancements.

Purpose of the Study:

  • To develop a novel, more accurate nanopore sequencing basecalling approach.
  • To address limitations in current basecalling accuracy using advanced deep learning techniques.

Main Methods:

  • Proposed a novel basecalling approach based on instance segmentation, formulating it as a multi-label segmentation task.
  • Developed a refined U-net model, termed UR-net, to effectively model sequential dependencies for 1D segmentation.

Main Results:

  • The proposed basecaller, URnano, demonstrated competitive performance on in-species data.
  • URnano achieved comparable results to recently proposed CTC-featured basecallers.

Conclusions:

  • Formulating nanopore basecalling as a 1D segmentation task is a promising strategy.
  • Jointly performing basecalling and segmentation offers a viable path for improved accuracy.