A Transformers Analysis Pipeline (TrAP) to Evaluate LINE-1 RNA Sequence Content
- 1Center for Genomic and Precision Medicine, Texas A&M Institute of Biosciences and Technology, Texas Medical Center, Houston, TX 77030, USA
Abstract
LINE-1 RNA has emerged as a potential regulator of chromatin organization and gene expression, as well as a candidate readout of cellular and disease-associated states. Accurate measurement of LINE-1 transcript abundance is therefore important for evaluating its regulatory, biomarker, and potential therapeutic relevance. Measurement is challenging in short-read RNA sequencing because similar LINE-1 loci produce ambiguous, multi-mapping reads and LINE-1-associated signal can arise from passive as well as autonomous transcription. We developed the Transformers Analysis Pipeline (TrAP), an alignment-free ALBERT-based workflow that classifies short RNA-seq reads as L1HS, L1PA, or non-LINE-1 before abundance estimation, converting overlapping k-mers to integer tokens and integrating predictions with Salmon- or expectation–maximization-based quantification. TrAP was evaluated using synthetic LINE-1 insertion datasets and HEK293 short-read RNA-seq benchmarked against 5′ rapid amplification of cDNA end (5′RACE) long-read measurements. AlbertSalmon agreed most closely with the synthetic insertion levels, ranking first in all insertion levels and deletion-probability strata. Agreement with the 5′RACE reference was also highest, although the locus-level gain there was only incremental. High-attribution k-mers were modestly over-represented within functional ORF2 coding regions rather than being uniformly distributed across LINE-1. These findings support TrAP as a transformer-assisted framework for improving LINE-1 quantification in short-read RNA-seq datasets, thereby supporting future regulatory and biomarker studies.
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BibTeX
@article{chamorroparejo2026transformers,
title = {{A Transformers Analysis Pipeline (TrAP) to Evaluate LINE-1 RNA Sequence Content}},
author = {Andres D. Chamorro-Parejo and Kenneth S. Ramos},
journal = {International Journal of Molecular Sciences},
year = {2026},
month = oct,
doi = {10.3390/ijms27219762},
url = {https://www.mdpi.com/1422-0067/27/21/9762},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - A Transformers Analysis Pipeline (TrAP) to Evaluate LINE-1 RNA Sequence Content
AU - Andres D. Chamorro-Parejo
AU - Kenneth S. Ramos
PY - 2026
DA - 2026/10/30
JO - International Journal of Molecular Sciences
DO - 10.3390/ijms27219762
UR - https://www.mdpi.com/1422-0067/27/21/9762
AB - LINE-1 RNA has emerged as a potential regulator of chromatin organization and gene expression, as well as a candidate readout of cellular and disease-associated states. Accurate measurement of LINE-1 transcript abundance is therefore important for evaluating its regulatory, biomarker, and potential therapeutic relevance. Measurement is challenging in short-read RNA sequencing because similar LINE-1 loci produce ambiguous, multi-mapping reads and LINE-1-associated signal can arise from passive as well as autonomous transcription. We developed the Transformers Analysis Pipeline (TrAP), an alignment-free ALBERT-based workflow that classifies short RNA-seq reads as L1HS, L1PA, or non-LINE-1 before abundance estimation, converting overlapping k-mers to integer tokens and integrating predictions with Salmon- or expectation–maximization-based quantification. TrAP was evaluated using synthetic LINE-1 insertion datasets and HEK293 short-read RNA-seq benchmarked against 5′ rapid amplification of cDNA end (5′RACE) long-read measurements. AlbertSalmon agreed most closely with the synthetic insertion levels, ranking first in all insertion levels and deletion-probability strata. Agreement with the 5′RACE reference was also highest, although the locus-level gain there was only incremental. High-attribution k-mers were modestly over-represented within functional ORF2 coding regions rather than being uniformly distributed across LINE-1. These findings support TrAP as a transformer-assisted framework for improving LINE-1 quantification in short-read RNA-seq datasets, thereby supporting future regulatory and biomarker studies.
SN - 1422-0067
ER -