Approaching sequence analysis by analyzing the distribution of succession patterns, which is to say, of L-tuple (oligomer) frequencies [9], is advantageous when the sequence similarity is low because alignment algorithms cease to recognize common motifs that are inexactly conserved, as recently illustrated for the SCOP protein database [10]. Furthermore, oligomeric frequencies are a natural genomic signature for analysis of collections of isolates [11-13] where, again, the advantages of the CGR representation did not go unnoticed [13]. These observations argue for the value of having a neutral format, one that is scale and succession-independent, to represent Biological sequences. We have used CGR as the starting point to develop just such a general procedure, which we designated as Universal Sequence Maps, USM [14]. The USM procedure provides a bijective mapping (see also [3]) between any symbolic sequence and a unique position in the USM unit hypercube. Furthermore, the distances between map positions were found to be associated with sequence dissimilarity. Because the procedure itself is not dependent on the scale targeted by its analysis (length of motifs, Markov order or memory length, depending on the technique chosen) this is of both fundamental and practical relevance.