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Updated: Sep 12, 2025

RNA Next-Generation Sequencing and a Bioinformatics Pipeline to Identify Expressed LINE-1s at the Locus-Specific Level
Published on: May 19, 2019
Identification of cell-type-specific, transcriptionally active transposable elements using long-read RNA-sequencing
Chaemin Lim1, Hyunsu An1, Jihwan Park2
1School of Life Sciences, Gwangju Institute of Science and Technology (GIST), Gwangju, 61005, Republic of Korea.
Researchers developed a new transcriptomic annotation for transposable element (TE)-derived transcripts using extensive long-read RNA sequencing data. This tool enhances the detection of cell-type-specific TE expression, offering insights into cellular identity and disease mechanisms.
Area of Science:
- Genomics
- Transcriptomics
- Molecular Biology
Background:
- Biological functions of transposable element (TE)-derived transcripts are known, but locus-specific expression in human cells is understudied.
- Limited research exists on the role of TE-derived transcripts in various human cell types.
Purpose of the Study:
- To create the first transcriptomic annotation for TE-derived transcripts.
- To enable the detection of locus-specific TE-derived transcripts in diverse human cell types.
- To provide a comprehensive reference for analyzing TE transcription.
Main Methods:
- Processed 2596 human long-read RNA sequencing (LR RNA-seq) datasets from 21 organs and 71 cell lines.
- Developed a pipeline to assemble transcripts containing TE sequences.
- Applied the TE annotation to Genotype-Tissue Expression (GTEx) single-cell RNA sequencing (scRNA-seq) data.
Main Results:
- Constructed a comprehensive TE-derived transcript annotation, outperforming existing methods like RepeatMasker and GENCODE.
- Identified novel TE-derived transcripts, isoforms, and alternative transcription end sites for long noncoding genes.
- Discovered cell-type-specific TE-derived transcripts in human tissues using scRNA-seq data.
Conclusions:
- Generated a comprehensive TE-derived transcript annotation from large-scale LR RNA-seq data.
- The annotation serves as a valuable resource for analyzing active TE transcripts and their isoforms.
- Discovered cell-type-specific TE transcripts that may elucidate cellular identity maintenance and disease pathology.
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