Exploring deep Markov models in genomic data compression using sequence pre-analysis

The pressure to find efficient genomic compression algorithms is being felt worldwide, as proved by several prizes and competitions. In this paper, we propose a compression algorithm that relies on a pre-analysis of the data before compression, with the aim of identifying regions of low complexity. This strategy enables us to use deeper context models, supported by hash-tables, without requiring huge amounts of memory. As an example, context depths as large as 32 are attainable for alphabets of four symbols, as is the case of genomic sequences. These deeper context models show very high compression capabilities in very repetitive genomic sequences, yielding improvements over previous algorithms. Furthermore, this method is universal, in the sense that it can be used in any type of textual data (such as quality-scores).