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Related Experiment Video

Updated: Aug 5, 2025

Detection of Rare Mutations in CtDNA Using Next Generation Sequencing

Detection of Rare Mutations in CtDNA Using Next Generation Sequencing

Published on: August 24, 2017

cnnLSV: detecting structural variants by encoding long-read alignment information and convolutional neural network.

Huidong Ma^1,2, Cheng Zhong^3,4, Danyang Chen^1,2

¹School of Computer, Electronics and Information, Guangxi University, Nanning, 530004, China.

BMC Bioinformatics

|March 28, 2023

Summary

This summary is machine-generated.

We developed cnnLSV, a novel method for genomic structural variant detection using convolutional neural networks and long-read sequencing data. This approach enhances accuracy by filtering false positives and correctly identifying multiple variant types.

Keywords:

Convolutional neural network Encoding alignment information Long reads Structural variant detection

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Last Updated: Aug 5, 2025

Detection of Rare Mutations in CtDNA Using Next Generation Sequencing

Detection of Rare Mutations in CtDNA Using Next Generation Sequencing

Published on: August 24, 2017

Detection of Copy Number Alterations Using Single Cell Sequencing

Detection of Copy Number Alterations Using Single Cell Sequencing

Published on: February 17, 2017

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

Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Genomic structural variant (SV) detection is crucial but challenging in genome analysis.
Existing long-read SV detection methods require improvement for multi-type SV identification.

Purpose of the Study:

To introduce cnnLSV, a method enhancing SV detection quality by reducing false positives.
To improve the accuracy of detecting insertions, deletions, inversions, and duplications.

Main Methods:

Developed an image encoding strategy for SVs from long-read alignment data.
Utilized a convolutional neural network (CNN) to train a filter model for false positive reduction.
Employed Principal Component Analysis (PCA) and k-means clustering to eliminate mislabeled training samples.

Main Results:

cnnLSV effectively eliminates false positives from merged detection results.
The method demonstrates superior performance in detecting multiple types of structural variants.
Experimental results on simulated and real datasets confirm the outperformance of cnnLSV.

Conclusions:

cnnLSV leverages long-read alignment data and CNNs for high-performance SV detection.
The integration of PCA and k-means effectively addresses mislabeled training data.
The proposed method offers a significant advancement in accurate and comprehensive SV identification.