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Generation of Genomic Deletions in Mammalian Cell Lines via CRISPR/Cas9

Generation of Genomic Deletions in Mammalian Cell Lines via CRISPR/Cas9

Published on: January 3, 2015

BreakNet: detecting deletions using long reads and a deep learning approach.

Junwei Luo¹, Hongyu Ding¹, Jiquan Shen²

¹College of Computer Science and Technology, Henan Polytechnic University, Jiaozuo, 454003, China.

BMC Bioinformatics

|December 3, 2021

Summary

BreakNet, a novel deep learning method, effectively detects deletions using long-read sequencing data. This approach surpasses existing tools in accuracy for identifying structural variations linked to genetic diseases.

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Rare Event Detection Using Error-corrected DNA and RNA Sequencing

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Published on: August 3, 2018

Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Structural variations (SVs), particularly deletions, are key to human genetic diversity and disease association.
Existing long-read deletion calling methods require enhanced feature extraction from alignment data.
Deep learning presents a promising avenue for advancing SV detection in genomics.

Purpose of the Study:

To introduce BreakNet, a deep learning-based method for accurate deletion detection using long-read sequencing.
To leverage alignment information for improved SV calling.
To demonstrate the efficacy of deep learning in enhancing deletion detection.

Main Methods:

BreakNet employs a deep learning architecture incorporating convolutional neural networks (CNNs) and bidirectional long short-term memory (BLSTM) models.
Feature matrices are extracted from long-read alignments and processed into feature vectors.
A classification module determines the presence of deletions based on integrated features.

Main Results:

BreakNet demonstrates superior performance compared to established methods like Sniffles, SVIM, and cuteSV.
The method achieves higher F1 scores on real-world long-read sequencing datasets.
The source code is publicly available for community use and further development.

Conclusions:

Deep learning integration with long-read data significantly improves deletion calling accuracy.
BreakNet offers a more effective approach for identifying deletions, advancing genomic variation analysis.
This study highlights the potential of AI in uncovering genetic factors of disease.