BACKGROUND: The problem of finding the shortest absent words in DNA data has 
been recently addressed, and algorithms for its solution have been described. It 
has been noted that longer absent words might also be of interest, but the 
existing algorithms only provide generic absent words by trivially extending the 
shortest ones.
RESULTS: We show how absent words relate to the repetitions and structure of the 
data, and define a new and larger class of absent words, called minimal absent 
words, that still captures the essential properties of the shortest absent words 
introduced in recent works. The words of this new class are minimal in the sense 
that if their leftmost or rightmost character is removed, then the resulting 
word is no longer an absent word. We describe an algorithm for generating 
minimal absent words that, in practice, runs in approximately linear time. An 
implementation of this algorithm is publicly available at 
ftp://www.ieeta.pt/~ap/maws.
CONCLUSION: Because the set of minimal absent words that we propose is much 
larger than the set of the shortest absent words, it is potentially more useful 
for applications that require a richer variety of absent words. Nevertheless, 
the number of minimal absent words is still manageable since it grows at most 
linearly with the string size, unlike generic absent words that grow 
exponentially. Both the algorithm and the concepts upon which it depends shed 
additional light on the structure of absent words and complement the existing 
studies on the topic.