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Recursive Parsing and Grammar Compression in the Era of Pangenomics (Invited Talk)

Author Christina Boucher

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Subject Classification

ACM Subject Classification
  • Applied computing → Bioinformatics
Keywords
  • Prefix-Free Parsing
  • Recursive Prefix-Free Parsing
  • Grammar-Based Compression
  • Succinct Data Structures
  • RePair Compression

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Abstract

Prefix-Free Parsing (PFP) and its recursive variant (RPFP) provide a scalable framework for compressing and indexing large genomic datasets. By enabling efficient construction of succinct data structures, these methods support fast and memory-efficient read alignment across thousands of genomes. Their deterministic and modular design makes them especially well-suited for pangenomics and large-scale sequence analysis.

Cite As Get BibTex

Christina Boucher. Recursive Parsing and Grammar Compression in the Era of Pangenomics (Invited Talk). In 25th International Conference on Algorithms for Bioinformatics (WABI 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 344, pp. 1:1-1:2, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025) https://doi.org/10.4230/LIPIcs.WABI.2025.1

Author Details

Christina Boucher
  • Department of Computer and Information Science and Engineering, Herbert Wertheim College of Engineering, University of Florida, Gainesville, FL, USA

References

  1. Christina Boucher, Travis Gagie, Alan Kuhnle, Ben Langmead, Giovanni Manzini, and Taher Mun. Prefix-free parsing for building big BWTs. Algorithms Molecular Biology, 14(1):13:1-13:15, 2019. URL: https://doi.org/10.1186/S13015-019-0148-5.
  2. Christina Boucher, Travis Gagie, Alan Kuhnle, and Giovanni Manzini. Prefix-free parsing for building big BWTs. In Proceedings of the Workshop of Algorithms in Biology (WABI), pages 2:1-2:16, 2018. URL: https://doi.org/10.4230/LIPICS.WABI.2018.2.
  3. Eddie Ferro, Marco Oliva, Travis Gagie, and Christina Boucher. Building a pangenome alignment index via recursive prefix-free parsing. iScience, 27(10):110933, 2024. Google Scholar
  4. Aaron Hong, Massimiliano Rossi, and Christina Boucher. LZ77 via Prefix-Free Parsing. In Proceedings of the Symposium on Algorithm Engineering and Experiments (ALENEX 2023), pages 123-134. SIAM, 2023. URL: https://doi.org/10.1137/1.9781611977561.CH11.
  5. Justin Kim, Rahul Varki, Marco Oliva, and Christina Boucher. Re²Pair: Increasing the Scalability of RePair by Decreasing Memory Usage. In Proceedings of the 32nd Annual European Symposium on Algorithms (ESA 2024), pages 78:1-78:15, 2024. URL: https://doi.org/10.4230/LIPICS.ESA.2024.78.
  6. Taher Mun, Alan Kuhnle, Christina Boucher, Travis Gagie, Ben Langmead, and Giovanni Manzini. Matching Reads to Many Genomes with the r-Index. Journal of Computational Biology, 27(4):514-518, 2020. URL: https://doi.org/10.1089/cmb.2019.0316.
  7. Marco Oliva, Davide Cenzato, Massimiliano Rossi, Zsuzsanna Lipták, Travis Gagie, and Christina Boucher. CSTs for Terabyte-Sized Data. In Proceedings of the 2022 Data Compression Conference (DCC 2022), pages 93-102. IEEE, 2022. URL: https://doi.org/10.1109/DCC52660.2022.00017.
  8. Ravin Varki, Matteo Rossi, Eddie Ferro, Marco Oliva, Erik Garrison, Ben Langmead, and Christina Boucher. Accurate short-read alignment through r-index-based pangenome indexing. Genome Research, June 2025. URL: https://doi.org/10.1101/gr.279858.124.
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