## 1 Introduction  
Since 1953, when the engineers at IBM's San Jose California laboratory invented the first \"random access file\" [1], storage performance has continuously grown both in terms of capacity and speed data access. Inversely, the space/cost ratio has dropped drastically with time. Data recorded over the last 30 years show a very strong exponential correlation, that is, space per unit cost has doubled roughly every 14 months and, then, has increased by an order of magnitude every 48 months. The cost was estimated to follow the equation: \(cost=10^{0.2502\times(year-1980)+6.304}\), with an extraordinarily high coefficient of correlation (\(r=0.9916\)) [2]. Unfortunately, this positive trend has only mitigated the economical impact of the highly elevated data production rates of today's biomedical instruments. Next Generation Sequencing (NGS) platforms, for example, are developing at a much greater rate than was seen for close technologies, yielding a 1000-fold drop in sequencing costs since 1990. Consequently, several new large data projects arose from one side (e.g., 1000 Genomes Project [3], the ENCODE project [4], Exome Sequencing Project (ESP) [5]), which brought the downside of having to stock lots of data on the other side. Storage costs have in fact largely exceeded reagent costs, leading sometimes to the extreme decision of carrying out the experiment again rather than retain raw data for long time. Next generation sequencers belong to the category of high-throughput instruments. They sequence short stretches of DNA/RNA molecules (also known as short-reads), previously extracted from biological samples, sheared and amplified according to manufacturer protocols. The sequencing procedure itself varies with the instrument. For example, the SOLiD platforms by Applied Biosystems (now Life Technology) perform five different steps, called _primer rounds_. At any round, primers, which are strands of nucleic acids that serve as starting points for DNA synthesis, hybridize to the adapter sequences within the library templates and a fluorescence is emitted as a consequence of each ligation. After detection, dyes are cleaved off, giving way to the next differentially labeled probe that can hybridize to the target sequence. A quality value (QV) is associated to each dye, which typically ranges from \(25\) to \(35\), even if it can span a wider range of values (_i.e._, \(-1\) (missing color call) to \(93\), even though it rarely exceeds \(60\)). When visualizing the dyes on a satay plot (Figure 0(a)), those that are on the axis and far from the origin are the beads that have a bright signal in only one channel. In general, the brighter the dye, the greater the difference in signal between colors and the higher the QV. Unlike other sequencers, which adhere to the standard FASTQ file, SOLiD outputs either a couple of Color Space FASTA (CSFASTA) and QV files, or an eXtensible SeQuence (XSQ) file. The former output encodes sequences of short-reads by four dye color codes (6-FAM = 0 (blue), CY3 = 1 (green), Texas Red = 2 (yellow), CY5 = 3 (red)). Each corresponds unambiguously to four combinations of adjacent nucleotides, according to Figure 0(b). In fact, rather than reading one base per cycle, information on two bases is measured simultaneously, and in each cycle, one of four colors is called. In a separate file, the quality of color calls is reported. For the sake of completeness, we recall that each QV is calculated using a Phred-like score \(q=-10 log10(p)\)  
Figure 1: Elements of color-space sequencing. (**a**) Satay plot; (**b**) Color space matrix. where \(q\) is the quality value and \(p\) is the predicted probability that the color call is incorrect. Contrarily, the XSQ data format organizes in a hierarchical fashion and conforms to the HDF5 format. It losslessly compresses FASTA sequences and QVs in binary files. Average compression rates are around 60%. Irrespective of their encoding, short-reads are mapped against a reference genome in order to sort them and reconstruct the target genome (Figure 2).