last character; the two codes are actually the first. The rotation holds nevertheless.) BWT applications As a lossless compression algorithm the Burrows–Wheeler transform offers the important quality that its encoding is reversible and hence the original data may be recovered from the resulting compression. The lossless quality of Burrows algorithm has provided for different algorithms with different purposes in mind. To name a few, Burrows–Wheeler transform is used in algorithms for sequence alignment, image compression, data compression, etc. The following is a compilation of some uses given to the Burrows–Wheeler Transform. BWT for sequence alignment The advent of next-generation sequencing (NGS) techniques at the end of the 2000s decade has led to another application of the Burrows–Wheeler transformation. In NGS, DNA is fragmented into small pieces, of which the first few bases are sequenced, yielding several millions of "reads", each 30 to 500 base pairs ("DNA characters") long. In many experiments, e.g., in ChIP-Seq, the task is now to align these reads to a reference genome, i.e., to the known, nearly complete sequence of the organism in question (which may be up to several billion base pairs long). A number of alignment programs, specialized for this task, were published, which initially relied on hashing (e.g., Eland, SOAP, or Maq). In an effort to reduce the memory requirement for sequence alignment, several alignment programs were developed (Bowtie, BWA, and SOAP2) that use the Burrows–Wheeler transform. BWT for image compression The Burrows–Wheeler transformation has proved to be fundamental for image compression applications. For example, Showed a compression pipeline based on the application of the Burrows–Wheeler transformation followed by inversion, run-length, and arithmetic encoders. The pipeline developed in this case is known as Burrows–Wheeler transform with an inversion encoder (BWIC). The results shown by BWIC are shown to outperform the compression performance of well-known and widely used algorithms like Lossless JPEG and JPEG 2000. BWIC is shown to outperform those in terms of final compression size of radiography medical images on the order of 5.1% and 4.1% respectively. The improvements are achieved by combining BWIC and a pre-BWIC scan of the image in a vertical snake order fashion. More recently, additional works like that of have shown the implementation of the Burrows–Wheeler Transform in conjunction with the known move-to-front transform (MTF) achieve near lossless compression of images. BWT for compression of genomic databases Cox et al. presented a genomic compression scheme that uses