New platform enables ultra-fast, reference-free searches of raw single-cell RNA data
Researchers introduced Malva, a computational system that lets scientists query raw RNA sequences across massive single-cell and spatial transcriptomics datasets without needing a reference genome.
The Malva platform provides a species-agnostic, reference-free method for searching raw RNA sequences in single-cell and spatial transcriptomics data at ultrafast speeds. By indexing non-overlapping k-mers together with cell identifiers, Malva avoids alignment and can return cells containing any user-specified nucleotide pattern within seconds. Users can submit exact sequences, gene identifiers or natural-language prompts, and the system translates these into structured searches that retrieve matching cells and coverage profiles.
Benchmarks demonstrate that Malva’s k-mer-derived pseudocounts align closely with conventional UMI counts and retain key biological patterns, including cell-type clustering and pseudotime trajectories. The public Malva Index, assembled from thousands of public experiments, currently spans millions of cells across diverse tissues, diseases and technologies, and is accessible via a free API. By turning static gene-count tables into interactive, sequence-level archives, Malva aims to accelerate discovery of mutations, isoforms, viral RNAs and other non-reference elements in large-scale single-cell studies.
Why it matters
Malva lets scientists instantly explore raw RNA sequences across huge datasets, speeding up discovery of disease-related variants and novel transcripts.
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