Reference-free assembly of long-read transcriptome sequencing data with RNA-Bloom2

Ka Ming Nip (), Saber Hafezqorani, Kristina K. Gagalova, Readman Chiu, Chen Yang, René L. Warren and Inanc Birol ()
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Ka Ming Nip: Canada’s Michael Smith Genome Sciences Centre, BC Cancer
Saber Hafezqorani: Canada’s Michael Smith Genome Sciences Centre, BC Cancer
Kristina K. Gagalova: Canada’s Michael Smith Genome Sciences Centre, BC Cancer
Readman Chiu: Canada’s Michael Smith Genome Sciences Centre, BC Cancer
Chen Yang: Canada’s Michael Smith Genome Sciences Centre, BC Cancer
René L. Warren: Canada’s Michael Smith Genome Sciences Centre, BC Cancer
Inanc Birol: Canada’s Michael Smith Genome Sciences Centre, BC Cancer

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

Abstract: Abstract Long-read sequencing technologies have improved significantly since their emergence. Their read lengths, potentially spanning entire transcripts, is advantageous for reconstructing transcriptomes. Existing long-read transcriptome assembly methods are primarily reference-based and to date, there is little focus on reference-free transcriptome assembly. We introduce “RNA-Bloom2 [ https://github.com/bcgsc/RNA-Bloom ]”, a reference-free assembly method for long-read transcriptome sequencing data. Using simulated datasets and spike-in control data, we show that the transcriptome assembly quality of RNA-Bloom2 is competitive to those of reference-based methods. Furthermore, we find that RNA-Bloom2 requires 27.0 to 80.6% of the peak memory and 3.6 to 10.8% of the total wall-clock runtime of a competing reference-free method. Finally, we showcase RNA-Bloom2 in assembling a transcriptome sample of Picea sitchensis (Sitka spruce). Since our method does not rely on a reference, it further sets the groundwork for large-scale comparative transcriptomics where high-quality draft genome assemblies are not readily available.

Date: 2023
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DOI: 10.1038/s41467-023-38553-y

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