MOTIVATION: New sequencing technologies generate larger amount of short reads 
data at decreasing cost. De novo sequence assembly is the problem of combining 
these reads back to the original genome sequence, without relying on a reference 
genome. This presents algorithmic and computational challenges, especially for 
long and repetitive genome sequences. Most existing approaches to the assembly 
problem operate in the framework of de Bruijn graphs. Yet, a number of recent 
works use the paradigm of string graph, using a variety of methods for storing 
and processing suffixes and prefixes, like suffix arrays, the Burrows-Wheeler 
transform or the FM index. Our work is motivated by a search for new approaches 
to constructing the string graph, using alternative yet simple data structures 
and algorithmic concepts.
RESULTS: We introduce a novel hash-based method for constructing the string 
graph. We use incremental hashing, and specifically a modification of the 
Karp-Rabin fingerprint, and Bloom filters. Using these probabilistic methods 
might create false-positive and false-negative edges during the algorithm's 
execution, but these are all detected and corrected. The advantages of the 
proposed approach over existing methods are its simplicity and the incorporation 
of established probabilistic techniques in the context of de novo genome 
sequencing. Our preliminary implementation is favorably comparable with the 
first string graph construction of Simpson and Durbin (2010) (but not with 
subsequent improvements). Further research and optimizations will hopefully 
enable the algorithm to be incorporated, with noticeable performance 
improvement, in state-of-the-art string graph-based assemblers.