Abstract Single-cell RNA-seq (scRNA-seq) is a powerful tool for cell type identification but is not readily applicable to organisms without well-annotated reference genomes. Of the approximately 10 million animal species predicted to exist on Earth, &gt;99.9% do not have any submitted genome assembly. To enable scRNA-seq for the vast majority of animals on the planet, here we introduce the concept of “ k -mer homology,” combining biochemical synonyms in degenerate protein alphabets with uniform data subsampling via MinHash into a pipeline called Kmermaid . Implementing this pipeline enables direct detection of similar cell types across species from transcriptomic data without the need for a reference genome. Underpinning Kmermaid is the tool Orpheum , a memory-efficient method for extracting high-confidence protein-coding sequences from RNA-seq data. After validating Kmermaid using datasets from human and mouse lung, we applied Kmermaid to the Chinese horseshoe bat ( Rhinolophus sinicus ), where we propagated cellular compartment labels at high fidelity. Our pipeline provides a high-throughput tool that enables analyses of transcriptomic data across divergent species’ transcriptomes in a genome- and gene annotation-agnostic manner. Thus, the combination of Kmermaid and Orpheum identifies cell type-specific sequences that may be missing from genome annotations and empowers molecular cellular phenotyping for novel model organisms and species.

Single-cell transcriptomics for the 99.9% of species without reference genomes