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Parallel Lossy Compression for Large FASTQ Files

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Biomedical Engineering Systems and Technologies (BIOSTEC 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1814))

  • 149 Accesses

Abstract

In this paper we present a parallel version for the algorithm BFQzip, we introduced in [Guerrini et al., BIOSTEC – BIOINFORMATICS 2022], that modifies the bases and quality scores components taking into account both information at the same time, while preserving variant calling. The resulting FASTQ file achieves better compression than the original data. Here, we introduce a strategy that splits the FASTQ file into t blocks and processes them in parallel independently by using the BFQzip algorithm. The resulting blocks with the modified bases and smoothed qualities are merged (in order) and compressed. We show that our strategy can improve the compression ratio of large FASTQ files by taking advantage of the redundancy of reads. When splitting into blocks, the reads belonging to the same portion of the genome could end up in different blocks. Therefore, we analyze how reordering reads before splitting the input FASTQ can improve the compression ratio as the number of threads increases. We also propose a paired-end mode that allows to exploit the paired-end information by processing blocks of FASTQ files in pairs.

Availability: The software is freely available at https://github.com/veronicaguerrini/BFQzip

An earlier version of this contribution appeared in the Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies: Bioinformatics [26].

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Notes

  1. 1.

    By using the code/library at https://github.com/nicolaprezza/bwt2lcp.

  2. 2.

    Note that the value Q does not depend on the cluster analyzed only in strategy (a).

  3. 3.

    We recall that a paired-end read is a pair consisting of two reads (or mates), such that the second read occurs in the genome at a known distance after the first one.

  4. 4.

    https://github.com/shubhamchandak94/Spring/tree/reorder-only.

  5. 5.

    http://libbsc.com/.

  6. 6.

    https://www.realtimegenomics.com/products/rtg-tools.

  7. 7.

    https://github.com/BEETL/BEETL/blob/RELEASE_1_1_0/scripts/lcp/applyLcpCutoff.pl.

  8. 8.

    http://gatb.inria.fr/software/leon/.

  9. 9.

    http://sfu-compbio.github.io/scalce/.

  10. 10.

    SNPs calling pipeline available at https://github.com/veronicaguerrini/BFQzip/blob/main/variant_calling/pipeline_SNPsCall.sh.

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Acknowledgments

The authors thank Prof. Nalvo Almeida (UFMS, Brazil) for granting access to the computer used in the experiments.

Funding

Work partially supported by MIUR, the Italian Ministry of Education, University and Research, under PRIN Project n. 20174LF3T8 AHeAD (“Efficient Algorithms for HArnessing Networked Data”). and by PNRR - M4C2 - Investimento 1.5, Ecosistema dell’Innovazione ECS00000017 - “THE - Tuscany Health Ecosystem” - Spoke 6 “Precision medicine & personalized healthcare”, funded by the European Commission under the NextGeneration EU programme.

F.A.L. acknowledges the financial support of CNPq (grant number 406418/2021-7) and FAPEMIG (grant number APQ-01217-22).

Author information

Authors and Affiliations

  1. Department of Computer Science, University of Pisa, Pisa, Italy

    Veronica Guerrini & Giovanna Rosone

  2. Faculty of Electrical Engineering, Federal University of Uberlândia, Uberlândia, Brazil

    Felipe A. Louza

Authors
  1. Veronica Guerrini
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  2. Felipe A. Louza
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  3. Giovanna Rosone
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Corresponding author

Correspondence to Veronica Guerrini .

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Editors and Affiliations

  1. Universidade Nova de Lisboa, Caparica, Portugal

    Ana Cecília A. Roque

  2. Virginia Tech, Blacksburg, VA, USA

    Denis Gracanin

  3. University of Vienna, Vienna, Austria

    Ronny Lorenz

  4. University of Edinburgh, Edinburgh, UK

    Athanasios Tsanas

  5. Université de Montréal, Montréal, QC, Canada

    Nathalie Bier

  6. Instituto de Telecomunicações and Instituto Superior Técnico - Lisbon University, Lisbon, Portugal

    Ana Fred

  7. Nova School of Science and Technology and University of Lisbon, Caparica, Portugal

    Hugo Gamboa

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Guerrini, V., Louza, F.A., Rosone, G. (2023). Parallel Lossy Compression for Large FASTQ Files. In: Roque, A.C.A., et al. Biomedical Engineering Systems and Technologies. BIOSTEC 2022. Communications in Computer and Information Science, vol 1814. Springer, Cham. https://doi.org/10.1007/978-3-031-38854-5_6

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  • DOI: https://doi.org/10.1007/978-3-031-38854-5_6

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