MAJEC: unified gene, isoform, and locus-level transposable element quantification from RNA-seq

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ID: 331751
2026
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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 MAJEC (Momentum Accelerated Junction Enhanced Counting), 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 vs 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: 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. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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openalex_W7154224357 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Tian-Yeh Lim, Ari J. Firestone
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Journal BMC Bioinformatics
Year 2026
DOI
10.1093/bioinformatics/btag737
URL
https://doi.org/10.1093/bioinformatics/btag737
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