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Updated: Jan 6, 2026

RNA Next-Generation Sequencing and a Bioinformatics Pipeline to Identify Expressed LINE-1s at the Locus-Specific Level

RNA Next-Generation Sequencing and a Bioinformatics Pipeline to Identify Expressed LINE-1s at the Locus-Specific Level

Published on: May 19, 2019

L1EM: a tool for accurate locus specific LINE-1 RNA quantification.

Wilson McKerrow^1,2, David Fenyö^1,2

¹Institute for Systems Genetics, USA.

Bioinformatics (Oxford, England)

|October 5, 2019

Summary

We developed L1EM, a tool to accurately quantify LINE-1 RNA at specific genomic loci. This method distinguishes retrotransposition-competent transcripts, aiding disease research.

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Area of Science:

Genomics
Molecular Biology
Bioinformatics

Background:

LINE-1 elements are retrotransposons that can copy sequences to new genomic locations.
Derepressed LINE-1 elements are linked to diseases and can cause cellular damage.
Quantifying LINE-1 RNA at specific loci and distinguishing functional transcripts is challenging due to repetitive sequences.

Purpose of the Study:

To develop a computational tool for accurate quantification of LINE-1 RNA at specific genomic loci.
To differentiate LINE-1 RNA transcripts capable of retrotransposition from non-functional ones.
To provide a reliable method for analyzing LINE-1 activity in the context of disease.

Main Methods:

Utilized the expectation-maximization algorithm for quantitative analysis.

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Last Updated: Jan 6, 2026

RNA Next-Generation Sequencing and a Bioinformatics Pipeline to Identify Expressed LINE-1s at the Locus-Specific Level

RNA Next-Generation Sequencing and a Bioinformatics Pipeline to Identify Expressed LINE-1s at the Locus-Specific Level

Published on: May 19, 2019

Analysis of LINE-1 Retrotransposition at the Single Nucleus Level

Analysis of LINE-1 Retrotransposition at the Single Nucleus Level

Published on: April 23, 2016

Using LEXY and LINuS Optogenetics Tools and Automated Image Analysis to Quantify Nucleocytoplasmic Transport Dynamics in Live Cells

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Developed the L1EM tool in Python for processing sequencing data.

Validated L1EM accuracy using simulated data and long-read sequencing from HEK cells.

Main Results:

L1EM accurately quantifies LINE-1 RNA at individual genomic loci.
The tool successfully separates retrotransposition-competent LINE-1 transcripts.
Demonstrated high accuracy on both simulated datasets and real-world sequencing data.

Conclusions:

L1EM offers a robust solution for quantifying locus-specific LINE-1 RNA.
This tool facilitates the study of LINE-1's role in disease pathogenesis.
The developed method enhances the analysis of retrotransposon activity.