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LFastqC: A lossless non-reference-based FASTQ compressor

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  • Sultan Al Yami
  • Chun-Hsi Huang

Abstract

The cost-effectiveness of next-generation sequencing (NGS) has led to the advancement of genomic research, thereby regularly generating a large amount of raw data that often requires efficient infrastructures such as data centers to manage the storage and transmission of such data. The generated NGS data are highly redundant and need to be efficiently compressed to reduce the cost of storage space and transmission bandwidth. We present a lossless, non-reference-based FASTQ compression algorithm, known as LFastqC, an improvement over the LFQC tool, to address these issues. LFastqC is compared with several state-of-the-art compressors, and the results indicate that LFastqC achieves better compression ratios for important datasets such as the LS454, PacBio, and MinION. Moreover, LFastqC has a better compression and decompression speed than LFQC, which was previously the top-performing compression algorithm for the LS454 dataset. LFastqC is freely available at https://github.uconn.edu/sya12005/LFastqC.

Suggested Citation

  • Sultan Al Yami & Chun-Hsi Huang, 2019. "LFastqC: A lossless non-reference-based FASTQ compressor," PLOS ONE, Public Library of Science, vol. 14(11), pages 1-10, November.
    • Handle: RePEc:plo:pone00:0224806
    DOI: 10.1371/journal.pone.0224806
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    1. Armando J Pinho & Paulo J S G Ferreira & António J R Neves & Carlos A C Bastos, 2011. "On the Representability of Complete Genomes by Multiple Competing Finite-Context (Markov) Models," PLOS ONE, Public Library of Science, vol. 6(6), pages 1-7, June.
    2. Pinghao Li & Shuang Wang & Jihoon Kim & Hongkai Xiong & Lucila Ohno-Machado & Xiaoqian Jiang, 2013. "DNA-COMPACT: DNA COMpression Based on a Pattern-Aware Contextual Modeling Technique," PLOS ONE, Public Library of Science, vol. 8(11), pages 1-13, November.
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