Data from large Next Generation Sequencing (NGS) experiments present challenges both in terms of costs associated with storage and in time required for file transfer. It is sometimes possible to store only a summary relevant to particular applications, but generally it is desirable to keep all information needed to revisit experimental results in the future. Thus, the need for efficient lossless compression methods for NGS reads arises. It has been shown that NGS-specific compression schemes can improve results over generic compression methods, such as the Lempel-Ziv algorithm, Burrows-Wheeler transform, or Arithmetic Coding. When a reference genome is available, effective compression can be achieved by first aligning the reads to the reference genome, and then encoding each read using the alignment position combined with the differences in the read relative to the reference. These reference-based methods have been shown to compress better than reference-free schemes, but the alignment step they require demands several hours of CPU time on a typical dataset, whereas reference-free methods can usually compress in minutes.