An alignment-last approach enables rapid transcriptomic biomarker discovery in large cohorts

Narmanli, E.; Lanau, A.; Neacsu, M.; Koshkina, M. K.; Fumeron, P.; Martin, P.; Servant, N.; Perrin-Gilbert, N.; Waterfall, J. J.

doi:10.64898/2026.08.31.748301

bioRxiv · 10.64898/2026.08.31.748301

An alignment-last approach enables rapid transcriptomic biomarker discovery in large cohorts

Narmanli, E.·Lanau, A.·Neacsu, M.·Koshkina, M. K.·Fumeron, P.·Martin, P.·Servant, N.·Perrin-Gilbert, N.·Waterfall, J. J.

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Abstract

Canonical transcriptomic analysis requires committing from the outset to a reference genome or transcriptome, which imposes a predefined feature set, usually annotated genes or isoforms. Alignment and annotation dilute the signal through feature-level aggregation, discard any sequence absent from the reference, and require reprocessing the entire dataset for each new question (mutations, fusions, transposable elements). Here, we introduce the alignment-last paradigm, in which the read becomes the unit of comparison across samples, and alignment is deferred to annotate only the relevant sequences. Querying the merome, a reference-free cohort k-mer index, with just a handful of reads (about 0.01% of a sample's) reveals the cohort's transcriptomic structure in bulk and single-cell data. At single-cell resolution, these reads outperform genes for cell classification and rediscover, without supervision, a transposable-element signature (VL30) of exhausted T cells. Finally, unsupervised read-level differential analysis recovers established lncRNA biomarkers; uncovers new prognostic transposable-element reads in adrenocortical carcinoma and sarcomas; and extracts signals even from reads that fail to align.

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Narmanli, E., Lanau, A., Neacsu, M., Koshkina, M. K., Fumeron, P., Martin, P., Servant, N., Perrin-Gilbert, N., Waterfall, J. J.. 2026-09-03. An alignment-last approach enables rapid transcriptomic biomarker discovery in large cohorts. https://doi.org/10.64898/2026.08.31.748301

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