  Minimizing data storage poses a significant challenge in large-scale
metagenomic projects. In this paper, we present a new method for improving the
encoding of FASTQ files generated by metagenomic sequencing. This method
incorporates metagenomic classification followed by a recursive filter for
clustering reads by DNA sequence similarity to improve the overall
reference-free compression. In the results, we show an overall improvement in
the compression of several datasets. As hypothesized, we show a progressive
compression gain for higher coverage depth and number of identified species.
Additionally, we provide an implementation that is freely available at
https://github.com/cobilab/mizar and can be customized to work with other FASTQ
compression tools.
