introduction.tex

\section{Introduction}\label{sec:introduction}

The rapid growth of bioinformatics databases leads to the increasing demand
for disk storage.
In recent years, there happened a gigantic leap in the speed of DNA
sequencing methods, which allowed us to sequence DNAs of complex organisms,
such as humans, quickly\cite{Ashley2016}.
However, this leads to increasing demand for disk storage, as the sizes of
the databases containing such data can easily reach dozens of terabytes.

Therefore, it seems that it could be very beneficial to try to compress the
sequences, hoping to make some savings and avoid needing to store such vast
amounts of data.
It seems that the choice of FASTQ compression tools is quite
limited at the moment, so one can expect some improvements in both
compression ratio and speed are possible.

In his article ``Context binning, model clustering and adaptivity for data
compression of genetic data''\cite{https://doi.org/10.48550/arxiv.2201.05028},
Jarek Duda proposes promising compression techniques that should help build a
compressor better than the current state of the art.
In order to allow real-world evaluation of those
techniques, a compressor called \emph{idencomp} has been built.
This article focuses on this compressor's implementation details and its
evaluation with some real-world data.