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Researchers developed TEscape, a computational framework combined with long-read sequencing technology, to comprehensively map transposable element-derived transcripts in the human genome. Analyzing over 235 million full-length reads from six different human cell types, they identified more than 83,000 previously unknown transcript isoforms originating from transposable elements, with 84% arising from complex combinations of multiple TEs. The study creates the first high-resolution atlas of transcribed transposable elements in humans, detecting expression of 14,312 protein-coding genes and confirming TE-transcript signatures in liver cancer samples.
Why it matters
This atlas provides a foundational resource for understanding how transposable elements, which comprise half the human genome, contribute to gene expression diversity in health and disease. The framework enables more accurate quantification of TE-derived transcripts, which could reveal their roles in diseases like cancer and potentially identify new therapeutic targets or biomarkers.
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⚠️ Preprint – Noch nicht peer-reviewed
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Transposable elements (TEs) not only account for half of the human genome sequence but also generate transcripts that contribute to transcriptomic diversity. Yet, their repetitive nature has hindered accurate quantification of the full TE-derived transcriptome, a challenge that long-read sequencing can overcome. Here, we combined multiplexed arrays isoform sequencing (MAS-ISO-seq) with a dedicated computational framework (TEscape) to perform an in-depth annotation of the human TE transcriptome. To capture the breadth of human transcriptome diversity, we profiled six representative cell types spanning three distinct biological contexts, including metabolism with, primary patient-derived adipogenic cells at two differentiation stages, and iPSC derived hepatic progenitor cells; the nervous system with iPSC-derived neurons, neural progenitor cells (NPCs), and pluripotency using induced pluripotent stem cells (iPSCs). Together, these datasets yielded over 235 million full-length long reads. First, to assess data coverage and transcriptome depth, we quantified protein-coding gene expression, detecting 14,312 genes (73.6% of all annotated protein-coding genes), which is a level consistent with deep and comprehensive transcriptome representation. Second, focusing on TE-derived transcripts, we identified >83,000 previously unannotated isoforms, the vast majority (84%) originating from a complex combination of multi-TEs. We also identified solo TEs, which are predominantly from LINE1 (14%). We confirmed that TE-transcripts are able to be exemplified by signatures detected in Liver Hepatocellular Carcinoma (LICH). Together, MAS-ISO-seq and TEscape establish the first long-read-based, high-resolution atlas of transcribed human TEs, providing a foundational resource for integrative transcriptome analyses and for investigating TE expression and regulation in health and disease.