Evidence map›Paper›PMID 40770674›Full record

ArticleGenomics & informatics2025

Identification of cell-type-specific, transcriptionally active transposable elements using long-read RNA-sequencing data-based comprehensive annotation.

Chaemin Lim, Hyunsu An, Jihwan Park

Abstract read
In one paragraph

Article in Genomics & informatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

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3citing papers in PubMed
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1 · What the graph read from it

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3 · Its place in the literature

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3 citing papers in PubMed.

  1. Article
  2. Review
  3. RNA variation as the driver of genomic efficiency and phenotypic complexity.International journal of biological sciences · 2026
    Review
4 · The record

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5 · Who and what money

Authors and funding

3 authors.

Chaemin LimSchool of Life Sciences, Gwangju Institute of Science and Technology (GIST), Gwangju, 61005, Republic of Korea.
Hyunsu AnSchool of Life Sciences, Gwangju Institute of Science and Technology (GIST), Gwangju, 61005, Republic of Korea.
Jihwan ParkSchool of Life Sciences, Gwangju Institute of Science and Technology (GIST), Gwangju, 61005, Republic of Korea. jihwan.park@gist.ac.kr.

Funding

Korean government RS-2024-00335026
6 · The paper itself

Abstract

backgroundThe biological functions of transposable element (TE)-derived transcripts during physiological development, disease development, and progression have been previously reported. However, research on locus-specific TE-derived transcript expression in various human cell types remains limited.

methodsWe processed 2596 publicly available human long-read RNA-sequencing (LR RNA-seq) datasets covering 21 organs and 71 cell lines in both healthy individuals and diseased patients with various conditions to compile this TE-derived transcript annotation. We established a pipeline for assembling transcripts containing TE sequences to measure transcriptionally active TE-derived transcripts in diverse tissues and cell types. Next, we applied our TE annotation to the Genotype-Tissue Expression (GTEx) single-cell RNA-sequencing (scRNA-seq) data from eight tissues.

resultsWe constructed the first transcriptom6e-based TE annotation using massive amounts of human LR RNA-seq data for use as a comprehensive reference to detect locus-specific TE-derived transcripts. Our annotation showed better detection accuracy for TE-derived transcripts than the RepeatMasker and GENCODE nonTE gene annotations. This annotation enabled the identification of novel TE-derived transcripts and their isoforms. We also identified alternative transcription end sites for long noncoding genes and confirmed previously annotated TE-nonTE gene fusion transcripts. Next, we applied our TE-derived transcript annotation to public scRNA-seq data from various human tissues and identified several cell-type-specific TE-derived transcripts in a locus-specific manner.

conclusionsWe generated a comprehensive, TE-derived transcript annotation using large-scale, LR RNA-seq data. Researchers can use our TE reference annotation to analyze active TE transcripts and their splicing isoforms in specific transcriptome datasets and to detect de novo TE transcripts. The discovery of cell-type-specific TE-derived transcripts may help explain mechanisms underlying the maintenance of cellular identity and provide new insights into the pathological mechanisms of various diseases.

Indexed as

De novo transcriptLong-read RNA-sequencingSingle-cell RNA-sequencingTranscriptome annotationTransposable element

Identifiers

PMID40770674
PMCPMC12326599

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.