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Non-LTR Retrotransposons

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As the name suggests, non-LTR retrotransposons lack the long terminal repeats characteristic of the LTR retrotransposons. Additionally, both LTR and non-LTR retrotransposons use distinct mechanisms of mobilization. Non-LTR retrotransposons are further divided into two classes - Long interspersed nuclear elements (LINEs) and short interspersed nuclear elements (SINEs), both of which occur abundantly in most mammals, including humans. Some of the active non-LTR retrotransposons in humans are L1...

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Updated: Jun 13, 2025

Detection of Retrotransposition Activity of Hot LINE-1s by Long-Distance Inverse PCR

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Published on: July 27, 2019

MEHunter: transformer-based mobile element variant detection from long reads.

Tao Jiang^1,2, Zuji Zhou¹, Zhendong Zhang¹

¹Center for Bioinformatics, Faculty of Computing, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China.

Bioinformatics (Oxford, England)

|September 17, 2024

Summary

This summary is machine-generated.

MEHunter accurately detects mobile genetic element variants (MEVs) using a transformer model, improving genetic disease research. This tool identifies novel MEVs missed by existing methods, enhancing genomic analysis.

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Last Updated: Jun 13, 2025

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Published on: August 21, 2016

Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Mobile genetic elements (MEs) are significant mutagens contributing to genetic diseases.
Long-read sequencing technologies offer potential for comprehensive ME variant (MEV) detection.
Accurate MEV detection is challenging due to variable lengths and noisy long-read data.

Purpose of the Study:

To develop a high-performance approach for detecting mobile genetic element variants (MEVs).
To improve the accuracy and sensitivity of MEV detection using advanced computational methods.

Main Methods:

Proposed MEHunter, a novel approach utilizing a fine-tuned transformer model.
The model is designed to identify MEVs by recognizing fragmented features within long reads.
Evaluated MEHunter on simulated and real-world datasets.

Main Results:

MEHunter demonstrated superior accuracy and sensitivity compared to state-of-the-art tools.
The approach effectively handles the challenges posed by noisy long-read sequencing data.
Identified novel, potentially individual-specific MEVs previously overlooked in population studies.

Conclusions:

MEHunter offers a robust solution for precise and sensitive MEV detection.
The tool advances the comprehensive analysis of mobile genetic elements in genomics.
Facilitates the discovery of new MEVs relevant to genetic diseases and individual variation.