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

10.0K
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...
10.0K
Next-generation Sequencing03:00

Next-generation Sequencing

88.8K
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....
88.8K

You might also read

Related Articles

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

Sort by
Same author

Foundation Model-Based Zero-Shot Tissue Segmentation of Pathological Images via the Mixture of Local-to-Global Experts.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same author

EfficientCovNet: Modeling the Pairwise Voxel Dependency for Brain ROI Segmentation.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same author

MoHD: Multi-mOdal survival prediction through Hierarchical Decoupling of whole-slide image pyramids and genomics.

Medical image analysis·2026
Same author

Functional system-specific brain aging across the Alzheimer's disease continuum.

Translational psychiatry·2026
Same author

Direct PET-to-CT Generation for Attenuation Correction: A Slice-to-Slice Continual Transformer Segmentation-Aware Network.

IEEE journal of biomedical and health informatics·2026
Same author

Shared genetic architecture between the topology of brain white matter structural connectome and fluid intelligence.

Communications biology·2026

Related Experiment Video

Updated: Jul 2, 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

13.7K

T-S2Inet: Transformer-based sequence-to-image network for accurate nanopore sequence recognition.

Xiaoyu Guan1,2, Wei Shao1,2, Daoqiang Zhang1,2

  • 1College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, MIIT Key Laboratory of Pattern Analysis and Machine Intelligence, Nanjing 211106, China.

Bioinformatics (Oxford, England)
|February 17, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a novel sequence-to-image (S2I) module and T-S2Inet model for nanopore sequencing data analysis. The method enhances classification accuracy for DNA, RNA, and protein sequences, improving upon existing deep learning techniques.

More Related Videos

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

8.8K
Nanopore DNA Sequencing for Metagenomic Soil Analysis
07:33

Nanopore DNA Sequencing for Metagenomic Soil Analysis

Published on: December 14, 2017

30.5K

Related Experiment Videos

Last Updated: Jul 2, 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

13.7K
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

8.8K
Nanopore DNA Sequencing for Metagenomic Soil Analysis
07:33

Nanopore DNA Sequencing for Metagenomic Soil Analysis

Published on: December 14, 2017

30.5K

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Nanopore sequencing offers high-throughput analysis of DNA, RNA, and proteins, but traditional data analysis is time-consuming and costly.
  • Deep learning methods show promise for nanopore data analysis, yet often struggle with classification accuracy compared to traditional approaches.
  • Existing deep learning techniques for nanopore data may not preserve crucial local sequence features.

Purpose of the Study:

  • To develop a novel deep learning approach for analyzing nanopore sequencing data.
  • To address the limitations of existing methods in preserving local sequence features.
  • To improve the classification accuracy of nanopore sequence data.

Main Methods:

  • A sequence-to-image (S2I) module was developed to transform variable-length nanopore sequences into images.
  • A Transformer-based model, T-S2Inet, was proposed to effectively capture salient information from these images.
  • The S2I module and T-S2Inet model were designed to preserve local sequence features during transformation.

Main Results:

  • The proposed T-S2Inet model demonstrated an approximate 2% improvement in classification accuracy compared to previous methods.
  • Quantitative and qualitative analyses confirmed the effectiveness of the S2I module and T-S2Inet model.
  • The method proved adaptable to various nanopore platforms, including Oxford Nanopore.

Conclusions:

  • The T-S2Inet model offers a significant advancement in nanopore sequence data analysis, particularly for sequences of unequal length.
  • The sequence-to-image transformation approach provides a valuable strategy for preserving local features in deep learning applications.
  • This work presents a generalizable framework for enhancing the analysis of complex biological sequence data.