Linear time complexity de novo long read genome assembly with GoldRush

Johnathan Wong (), Lauren Coombe, Vladimir Nikolić, Emily Zhang, Ka Ming Nip, Puneet Sidhu, René L. Warren and Inanç Birol ()
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Johnathan Wong: Canada’s Michael Smith Genome Sciences Centre, BC Cancer
Lauren Coombe: Canada’s Michael Smith Genome Sciences Centre, BC Cancer
Vladimir Nikolić: Canada’s Michael Smith Genome Sciences Centre, BC Cancer
Emily Zhang: Canada’s Michael Smith Genome Sciences Centre, BC Cancer
Ka Ming Nip: Canada’s Michael Smith Genome Sciences Centre, BC Cancer
Puneet Sidhu: Canada’s Michael Smith Genome Sciences Centre, BC Cancer
René L. Warren: Canada’s Michael Smith Genome Sciences Centre, BC Cancer
Inanç Birol: Canada’s Michael Smith Genome Sciences Centre, BC Cancer

Nature Communications, 2023, vol. 14, issue 1, 1-9

Abstract: Abstract Current state-of-the-art de novo long read genome assemblers follow the Overlap-Layout-Consensus paradigm. While read-to-read overlap – its most costly step – was improved in modern long read genome assemblers, these tools still often require excessive RAM when assembling a typical human dataset. Our work departs from this paradigm, foregoing all-vs-all sequence alignments in favor of a dynamic data structure implemented in GoldRush, a de novo long read genome assembly algorithm with linear time complexity. We tested GoldRush on Oxford Nanopore Technologies long sequencing read datasets with different base error profiles sourced from three human cell lines, rice, and tomato. Here, we show that GoldRush achieves assembly scaffold NGA50 lengths of 18.3-22.2, 0.3 and 2.6 Mbp, for the genomes of human, rice, and tomato, respectively, and assembles each genome within a day, using at most 54.5 GB of random-access memory, demonstrating the scalability of our genome assembly paradigm and its implementation.

Date: 2023
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DOI: 10.1038/s41467-023-38716-x

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