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Alevin-fry-atac enables rapid and memory frugal mapping of single-cell ATAC-seq data using virtual colors for accurate genomic pseudoalignment

Singh, N. P.; Khan, J.; Patro, R.

2025-02-13 bioinformatics
10.1101/2024.11.27.625771 bioRxiv
Show abstract

Ultrafast mapping of short reads via lightweight mapping techniques such as pseudoalignment has significantly accelerated transcriptomic and metagenomic analyses, often with minimal accuracy loss compared to alignment-based methods. However, applying pseudoalignment to large genomic references, like chromosomes, is challenging due to their size and repetitive sequences. We introduce a new and modified pseudoalignment scheme that partitions each reference into "virtual colors.... These are essentially overlapping bins of fixed maximal extent on the reference sequences that are treated as distinct "colors" from the perspective of the pseudoalignment algorithm. We apply this modified pseudoalignment procedure to process and map single-cell ATAC-seq data in our new tool alevin-fry-atac. We compare alevin-fry-atac to both Chromap and Cell Ranger ATAC. Alevin-fry-atac is highly scalable and, when using 32 threads, is approximately 2.8 times faster than Chromap (the second fastest approach) while using approximately one third of the memory and mapping slightly more reads. The resulting peaks and clusters generated from alevin-fry-atac show high concordance with those obtained from both Chromap and the Cell Ranger ATAC pipeline, demonstrating that virtual colorenhanced pseudoalignment directly to the genome provides a fast, memory-frugal, and accurate alternative to existing approaches for single-cell ATAC-seq processing. The development of alevin-fry-atac brings single-cell ATAC-seq processing into a unified ecosystem with single-cell RNA-seq processing (via alevin-fry) to work toward providing a truly open alternative to many of the varied capabilities of CellRanger. Furthermore, our modified pseudoalignment approach should be easily applicable and extendable to other genome-centric mapping-based tasks and modalities such as standard DNA-seq, DNase-seq, Chip-seq and Hi-C.

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