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When building Burrows-Wheeler Transforms (BWTs) of truly huge datasets, prefix-free parsing (PFP) can use an unreasonable amount of memory. In this paper we show how if a dataset can be broken down into small datasets that are not very similar to each other—such as collections of many copies of genomes of each of several species, or collections of many copies of each of the human chromosomes—then we can drastically reduce PFP’s memory footprint by building the BWTs of the small datasets and then merging them into the BWT of the whole dataset.

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Prefix-Free Parsing for Merging Big BWTs

  • 会议论文

  • 发表日期：

    2026年

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  • Diego Díaz-Domínguez,
  • Travis Gagie,
  • Veronica Guerrini,
  • Ben Langmead,
  • Zsuzsanna Lipták,
  • Giovanni Manzini,
  • Francesco Masillo,
  • Vikram Shivakumar

摘要

When building Burrows-Wheeler Transforms (BWTs) of truly huge datasets, prefix-free parsing (PFP) can use an unreasonable amount of memory. In this paper we show how if a dataset can be broken down into small datasets that are not very similar to each other—such as collections of many copies of genomes of each of several species, or collections of many copies of each of the human chromosomes—then we can drastically reduce PFP’s memory footprint by building the BWTs of the small datasets and then merging them into the BWT of the whole dataset.