## 1 Introduction
Aligned reads are piled up in textual (Sequence Alignment/Map, SAM) or binary (Binary Alignment/Map, BAM) archives [6]. SAM is a tab-delimited text format consisting of a header section, which is optional, and an alignment section. Each alignment line has several mandatory fields for essential alignment information such as mapping position, and variable number of optional fields for aligner specific information. The SAM/BAM format is already supported by a number of read mappers and is becoming the de-facto standard alignment format. After alignment, SAM/BAM files are naturally subjected to deep scan, in search of point mutations (Single Nucleotide Polymorphisms, SNPs) or insertions/deletions (Indels) of short amino acid sequences. According to several factors, such as the ratio between alternate and reference alleles, depth of coverage and quality of call, a genotype is assigned to each locus. On average, a typical whole genome experiment identifies \(3.3\) million SNPs, of which around \(15\%\) are novel. This means about \(1\) out of every \(1000\) nucleotides yields a mutation, but only \(1\) out of every \(6700\) nucleotides yields a novel mutation. Variant callers usually format information of the detected variants in Variant Call Format (VCF), a text file format (most likely stored in uncompressed manner). It contains meta-information lines and data lines, which carry information about a locus in the genome. Hence, NGS experiments require storage of three levels of data. Raw or unprocessed data often comprise images and standard textual representations of short biological sequences (also known as  
Figure 2: Short-Reads alignment. short-reads) and quality scores. Data occupancy highly depends on the sequencing platform, size of the target regions, wished average depth and breadth of coverage and sequencing modality. Roughly, raw files require about tens of GB to a few TB of free space to be stored. Size of SAM/BAM files depends on the experimental settings as well, but roughly ranges from tens to hundreds of GB. For example, assuming a \(3\) gigabase (GB) human genome length and an average depth of coverage of \(30\times\), one would have \(90\) GB (roughly occupying 90 GB of disk space, if we map one character to one byte). Considering that a typical SAM file contains both mapped/unmapped short-reads and QVs, the total size would be around \(180\) GB (ignoring control lines and carriage returns). BAM files typically compress to \(25\%\) of original size. A resulting \(30\times\) BAM should occupy about \(100\) GB of disk space. The size of a basic VCF file yet depends on the target genomic regions. Assuming each data line taking around \(45\) bytes, a whole human genome search would yield a file with a maximum size of 140 MB. We focus here on data produced by SOLiD sequencers. We deal with software tools capable of compressing BAM files generated from XSQ files. We do not consider compression solutions for XSQ or VCF files in this study, since they do not actually require any special inflation that any general-purpose compressor software wound not be able to provide. In Section 2, we describe our analysis strategy. We present our datasets, the software tools under evaluation and the criteria for measuring their compression performance. In Sections 3 and 4, we comment results and conclude the paper, respectively.