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

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.