2020/06/21 by Christina Boucher, Ondrej Cvacho, Boucher, Christina +11
Biochemistry, Genetics and Molecular Biology · Computer Science · #Algorithms and Data Compression #Data Structures and Algorithms (cs.DS) #FOS: Computer and information sciences #Genomics and Phylogenetic Studies #Natural Language Processing Techniques
paper · pdf · doi:10.48550/arxiv.2006.11687
openalex publication_date 2020/06/21 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Prefix-free parsing (PFP) was introduced by Boucher et al. (2019) as a preprocessing step to ease the computation of Burrows-Wheeler Transforms (BWTs) of genomic databases. Given a string S, it produces a dictionary D and a parse P of overlapping phrases such that BWT (S) can be computed from D and P in time and workspace bounded in terms of their combined size |PFP (S)|. In practice D and P are significantly smaller than S and computing BWT (S) from them is more efficient than computing it from S directly, at least when S consists of genomes from individuals of the same species. In this paper, we consider PFP (S) as a \em data structure and show how it can be augmented to support the following queries quickly, still in O (|PFP (S)|) space: longest common extension (LCE), suffix array (SA), longest common prefix (LCP) and BWT. Lastly, we provide experimental evidence that the PFP data structure can be efficiently constructed for very large repetitive datasets: it takes one hour and 54 GB peak memory for 1000 variants of human chromosome 19, initially occupying roughly 56 GB.