Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

RNA-seq

RNA-seq

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...

You might also read

Related Articles

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

Sort by

Same author

Quantum-inspired interpretable deep learning architecture for text sentiment analysis.

Neural networks : the official journal of the International Neural Network Society·2026

Same author

Constructing Layered Double Hydroxide-Based Micro-Nano Reactors for Enhanced Nitrogen Photofixation.

Advanced materials (Deerfield Beach, Fla.)·2026

Same author

Ammonia-Assisted Photosynthesis of Ethylene Glycol.

Journal of the American Chemical Society·2025

Same author

From performance to prediction: extracting aging data from the effects of base load aging on washing machines for a machine learning model.

Scientific reports·2025

Same author

A Comprehensive Survey on Evidential Deep Learning and its Applications.

IEEE transactions on pattern analysis and machine intelligence·2025

Same author

SpaBalance: Balanced Learning for Efficient Spatial Multi-Omics Decoding.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2025

Same journal

conMItion: an R package adjusting confounding factors for associations in multi-omics.

Bioinformatics (Oxford, England)·2026

Same journal

SpaMFG: a Spatial Multi-omics Integration Method based on Feature Grouping.

Bioinformatics (Oxford, England)·2026

Same journal

CSCN: Inference of Cell-Specific Causal Networks Using Single-Cell RNA-Seq Data.

Bioinformatics (Oxford, England)·2026

Same journal

Sparse CCA-Based Mediation Analysis with High-Dimensional Exposures and Mediators.

Bioinformatics (Oxford, England)·2026

Same journal

Enhancing Cross-Context Generalization in Drug Perturbation Prediction with a Multimodal Conditional Diffusion Framework.

Bioinformatics (Oxford, England)·2026

Same journal

Primer Design through Submodular Function Estimation.

Bioinformatics (Oxford, England)·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 14, 2025

Identification of Alternative Splicing and Polyadenylation in RNA-seq Data

Identification of Alternative Splicing and Polyadenylation in RNA-seq Data

Published on: June 24, 2021

CSV-Filter: a deep learning-based comprehensive structural variant filtering method for both short and long reads.

Zeyu Xia¹, Weiming Xiang², Qingzhe Wang¹

¹College of Computer Science and Technology, National University of Defense Technology, Hunan 410073, P. R. China.

Bioinformatics (Oxford, England)

|September 6, 2024

Summary

This summary is machine-generated.

CSV-Filter, a new deep learning tool, effectively reduces false positive structural variant (SV) calls in genetic data from both short and long reads. This method enhances accuracy in genetic research and precision medicine by improving SV detection filtering.

More Related Videos

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

Published on: June 23, 2012

Ultra-long Read Sequencing for Whole Genomic DNA Analysis

Ultra-long Read Sequencing for Whole Genomic DNA Analysis

Published on: March 15, 2019

Related Experiment Videos

Last Updated: Jun 14, 2025

Identification of Alternative Splicing and Polyadenylation in RNA-seq Data

Identification of Alternative Splicing and Polyadenylation in RNA-seq Data

Published on: June 24, 2021

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

Published on: June 23, 2012

Ultra-long Read Sequencing for Whole Genomic DNA Analysis

Ultra-long Read Sequencing for Whole Genomic DNA Analysis

Published on: March 15, 2019

Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Structural variants (SVs) are crucial in genetic research and precision medicine.
Current SV detection methods often yield numerous false positive calls, necessitating effective filtering strategies.

Purpose of the Study:

To develop and evaluate CSV-Filter, a novel deep learning-based tool for filtering structural variant calls.
To improve the accuracy of SV detection for both short and long sequencing reads.

Main Methods:

Developed CSV-Filter, a deep learning tool utilizing multi-level grayscale image encoding of CIGAR strings.
Employed image augmentation and self-supervised learning networks for enhanced SV feature extraction and classification.
Implemented mixed-precision operations to accelerate model training.

Main Results:

CSV-Filter significantly reduced false positive SVs when integrated with existing SV detection tools for both short and long reads.
True positive SV calls were largely preserved, maintaining high sensitivity.
CSV-Filter outperformed DeepSVFilter in identifying false positives and additionally supports long-read data.

Conclusions:

CSV-Filter offers a robust and accurate solution for filtering structural variants, applicable to diverse sequencing data types.
The tool enhances the reliability of SV detection, supporting advancements in genetic research and precision medicine.