MAJEC: unified gene, isoform, and locus-level transposable element quantification from RNA-seq.
- 1Calico Life Sciences LLC, South San Francisco, CA 94080, United States.
Abstract
Motivation
The study of transposable elements (TEs) has become increasingly central to fields such as cancer biology, immunology, and aging. Accurately quantifying disease- or laboratory-mediated perturbations in these elements is critical to support this expanding research, yet current RNA-seq pipelines struggle with the pervasive overlap between TEs and protein-coding genes. Existing tools either aggregate to the subfamily level with no locus resolution (TEtranscripts), or provide locus-level quantification without modeling gene overlap (Telescope), with the latter attributing over 40% of TE signal to the 1.1% of loci that overlap gene exons.Results
We present Momentum Accelerated Junction Enhanced Counting (MAJEC), a unified Expectation-Maximization (EM) framework that jointly quantifies genes, transcript isoforms, and individual TE loci from BAM alignments in a single pass. Splice junction evidence informs transcript-level priors, enabling MAJEC to probabilistically distinguish genic from TE-derived reads. This approach was independently validated against Salmon and RSEM on isoform quantification benchmarks. The joint feature space reduces exon-overlap contamination of locus-level TE estimates from 43% of total signal (Telescope) to 5% (MAJEC), while preserving subfamily-level accuracy (differential expression r = 0.987 versus TEtranscripts). Using paired biological vignettes, we demonstrate that MAJEC correctly resolves both the false TE reactivation artifacts endemic to TE-only models, and the false gene upregulation artifacts that occur when heuristic rules misassign genuine intragenic TE transcription. MAJEC simultaneously produces the isoform and locus-level resolution that TEtranscripts lacks, with greater accuracy than Telescope, and runs faster than either.Availability and implementation
MAJEC is available at https://github.com/calico/majec and via PyPI/Bioconda; analysis code and data are deposited at https://doi.org/10.5281/zenodo.19224157.
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BibTeX
@article{lim2026majec,
title = {{MAJEC: unified gene, isoform, and locus-level transposable element quantification from RNA-seq.}},
author = {Tian-Yeh Lim and Ari J Firestone},
journal = {Bioinformatics (Oxford, England)},
year = {2026},
month = oct,
doi = {10.1093/bioinformatics/btag737},
url = {https://europepmc.org/article/MED/42834519},
note = {CC BY},
}
RIS
TY - JOUR
TI - MAJEC: unified gene, isoform, and locus-level transposable element quantification from RNA-seq.
AU - Tian-Yeh Lim
AU - Ari J Firestone
PY - 2026
DA - 2026/10/01
JO - Bioinformatics (Oxford, England)
DO - 10.1093/bioinformatics/btag737
UR - https://europepmc.org/article/MED/42834519
AB - Motivation The study of transposable elements (TEs) has become increasingly central to fields such as cancer biology, immunology, and aging. Accurately quantifying disease- or laboratory-mediated perturbations in these elements is critical to support this expanding research, yet current RNA-seq pipelines struggle with the pervasive overlap between TEs and protein-coding genes. Existing tools either aggregate to the subfamily level with no locus resolution (TEtranscripts), or provide locus-level quantification without modeling gene overlap (Telescope), with the latter attributing over 40% of TE signal to the 1.1% of loci that overlap gene exons.Results We present Momentum Accelerated Junction Enhanced Counting (MAJEC), a unified Expectation-Maximization (EM) framework that jointly quantifies genes, transcript isoforms, and individual TE loci from BAM alignments in a single pass. Splice junction evidence informs transcript-level priors, enabling MAJEC to probabilistically distinguish genic from TE-derived reads. This approach was independently validated against Salmon and RSEM on isoform quantification benchmarks. The joint feature space reduces exon-overlap contamination of locus-level TE estimates from 43% of total signal (Telescope) to 5% (MAJEC), while preserving subfamily-level accuracy (differential expression r = 0.987 versus TEtranscripts). Using paired biological vignettes, we demonstrate that MAJEC correctly resolves both the false TE reactivation artifacts endemic to TE-only models, and the false gene upregulation artifacts that occur when heuristic rules misassign genuine intragenic TE transcription. MAJEC simultaneously produces the isoform and locus-level resolution that TEtranscripts lacks, with greater accuracy than Telescope, and runs faster than either.Availability and implementation MAJEC is available at https://github.com/calico/majec and via PyPI/Bioconda; analysis code and data are deposited at https://doi.org/10.5281/zenodo.19224157.
SN - 1367-4803
SN - 1367-4811
ER -