Unsupervised reference-free inference reveals unrecognized regulated transcriptomic complexity in human single cells

Myriad mechanisms diversify the sequence content of eukaryotic transcripts at both the DNA and RNA levels, leading to profound functional consequences. Examples of this diversity include RNA splicing and V(D)J recombination. Currently, these mechanisms are detected using fragmented bioinformatic tools that require predefining a form of transcript diversification and rely on alignment to an incomplete reference genome, filtering out unaligned sequences, potentially crucial for novel discoveries. Here, we develop SPLASH+, a new analytic method that performs unified, reference-free statistical inference directly on raw sequencing reads. By integrating a micro-assembly and biological interpretation framework with the recently developed SPLASH algorithm, SPLASH+ discovers broad and novel examples of transcript diversification in single cells de novo, without the need for genome alignment and cell type metadata, which is impossible with current algorithms. Applied to 10,326 primary human single cells across 19 tissues profiled with SmartSeq2, SPLASH+ discovers a set of splicing and histone regulators with highly conserved intronic regions that are themselves subject to targets of complex splicing regulation. Additionally, it reveals unreported transcript diversity in the heat shock protein HSP90AA1, as well as diversification in centromeric RNA expression, V(D)J recombination, RNA editing, and repeat expansion, all missed by existing methods. SPLASH+ is unbiased and highly efficient, enabling the discovery of an unprecedented breadth of RNA regulation and diversification in single cells through a new paradigm of transcriptomic analysis. ### Competing Interest Statement The authors have declared no competing interest.