  This paper describes a method to efficiently retrieve protein database
sequences similar to a query sequence, while allowing for significant numbers
of mutations. We call this method SEQR for SEQuence Retrieval. This approach
increases the speed of sequence similarity searches by an order of magnitude
compared to conventional algorithms at the expense of sensitivity. Furthermore,
retrieval time increases less than linearly with the number of sequences, a
desirable property during an era when next generation sequencing technologies
have yielded greater than exponential increases in sequence records. The lower
sensitivity of the algorithm for distantly related sequences compared to
benchmarks is not intrinsic to the method itself, but rather due to the
procedure used to construct the indexing terms, and may be improved. The
indexing terms themselves can be added to standard information retrieval
engines, enabling complex queries that include sequence similarity and other
descriptors such as taxonomy and text descriptions.
