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Building genomic data structures from compressed representations using prefix-free parsing
Rahul Varki1, Christina Boucher2
1Department of Computer and Information Science and Engineering, Herbert Wertheim College of Engineering, University of Florida, Gainesville, Florida 32611, USA rvarki@ufl.edu.
Genome Research
|May 15, 2026
Summary
Prefix-free parsing (PFP) enables bioinformatics tools to handle massive genome datasets by compressing repetitive text. This allows essential data structures to be built from compressed data, overcoming memory limitations for large-scale pangenomics.
Area of Science:
- Bioinformatics
- Genomics
- Computational Biology
Background:
- High-throughput sequencing enables large pangenomic datasets, exceeding petabyte scale.
- Traditional bioinformatics tools struggle with memory limitations on these massive datasets.
- A need exists for methods that process data directly from compressed representations.
Purpose of the Study:
- To survey prefix-free parsing (PFP) as a solution for handling large-scale genomic data.
- To explain the core principles and applications of PFP.
- To outline future research directions in PFP for bioinformatics.
Main Methods:
- Prefix-free parsing (PFP) as a preprocessing technique.
- Compression of repetitive text within large datasets.
- Construction of data structures directly from compressed PFP output.
Main Results:
- PFP compresses repetitive text efficiently.
- Enables the construction of essential data structures from compressed data.
- Addresses memory limitations in traditional bioinformatics tools for large datasets.
Conclusions:
- PFP is a crucial method for managing and analyzing large-scale pangenomic data.
- It overcomes memory constraints by operating on compressed representations.
- Further research can expand PFP's applications in bioinformatics.
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