Modular, efficient and constant-memory single-cell RNA-seq preprocessing

By Páll Melsted, A. S. Booeshaghi, Lauren Liu, Fan Gao, Lambda Lu, Kyung Hoi, Joseph Min, E. Beltrame, Kristján Eldjárn Hjörleifsson, Jase Gehring and 1 more. Nature Biotechnology, 2021

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We describe a workflow for preprocessing of single-cell RNA-sequencing data that balances efficiency and accuracy. Our workflow is based on the kallisto and bustools programs, and is near optimal in speed with a constant memory requirement providing scalability for arbitrarily large datasets. The workflow is modular, and we demonstrate its flexibility by showing how it can be used for RNA velocity analyses. A preprocessing workflow for single-cell RNA-seq data achieves near-optimal speed.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/683bfd26033de8b4dfe6ecac96d2e71cdef55028