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INSnet: a method for detecting insertions based on deep learning network.

Runtian Gao¹, Junwei Luo², Hongyu Ding¹

¹School of Software, Henan Polytechnic University, Jiaozuo, 454003, China.

BMC Bioinformatics

|March 6, 2023

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INSnet, a novel deep learning method, accurately detects insertions, a common cause of genetic diseases. This advancement improves upon existing methods by achieving superior performance on real-world datasets.

Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Structural variations (SVs), particularly insertions, are significantly linked to human diseases.
Accurate detection of insertions is crucial for understanding genetic disorders.
Existing insertion detection methods often suffer from errors and missed variants, highlighting a persistent challenge.

Purpose of the Study:

To introduce INSnet, a novel deep learning-based method for accurate insertion detection.
To improve the precision and recall of insertion variant identification.

Main Methods:

INSnet employs a deep learning network that processes the reference genome in sub-regions.
It utilizes five features derived from long-read alignments and incorporates depthwise separable convolutional networks with attention mechanisms (CBAM, ECA).

Keywords:

Deep learning Depthwise separable convolutional network Gated recurrent unit Insertion Structural variation

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A gated recurrent unit (GRU) network captures relationships between adjacent sub-regions for enhanced signature extraction.

Main Results:

INSnet accurately predicts the presence, precise site, and length of insertions within genomic sub-regions.
The method effectively extracts informative alignment features using convolutional and attention mechanisms.
GRU networks further refine the identification of insertion signatures by considering adjacent genomic contexts.

Conclusions:

INSnet demonstrates superior performance compared to existing methods, as evidenced by its higher F1 score on real datasets.
The deep learning approach offers a significant advancement in the accuracy of insertion detection.
The developed method provides a valuable tool for genetic disease research and diagnostics.