Similarity of sequences is a key mathematical notion for Classification and 
Phylogenetic studies in Biology. It is currently primarily handled using 
alignments. However, the alignment methods seem inadequate for post-genomic 
studies since they do not scale well with data set size and they seem to be 
confined only to genomic and proteomic sequences. Therefore, alignment-free 
similarity measures are actively pursued. Those measures offer the additional 
advantage of being applicable to structural data, as long as the structure is 
encoded into a string. A series of recent ground-breaking results have given 
evidence that those techniques are valid alternatives to classic methods, 
based on alignments, for classification and comparison of biological sequences 
and structures. In this talk, the state of the art in this area is presented.
In depth attention will be given to the Universal Similarity Metric, a 
methodology based on Kolmogorov Complexity. In particular, the first set of 
extensive quantitative experiments showing that USM is successfully applicable 
in Biology will be presented.