BACKGROUND: This paper describes techniques for accelerating the performance of 
the string set matching problem with particular emphasis on applications in 
computational proteomics. The process of matching peptide sequences against a 
genome translated in six reading frames is part of a proteogenomic mapping 
pipeline that is used as a case-study. The Aho-Corasick algorithm is adapted for 
execution in field programmable gate array (FPGA) devices in a manner that 
optimizes space and performance. In this approach, the traditional Aho-Corasick 
finite state machine (FSM) is split into smaller FSMs, operating in parallel, 
each of which matches up to 20 peptides in the input translated genome. Each of 
the smaller FSMs is further divided into five simpler FSMs such that each simple 
FSM operates on a single bit position in the input (five bits are sufficient for 
representing all amino acids and special symbols in protein sequences).
RESULTS: This bit-split organization of the Aho-Corasick implementation enables 
efficient utilization of the limited random access memory (RAM) resources 
available in typical FPGAs. The use of on-chip RAM as opposed to FPGA logic 
resources for FSM implementation also enables rapid reconfiguration of the FPGA 
without the place and routing delays associated with complex digital designs.
CONCLUSION: Experimental results show storage efficiencies of over 80% for 
several data sets. Furthermore, the FPGA implementation executing at 100 MHz is 
nearly 20 times faster than an implementation of the traditional Aho-Corasick 
algorithm executing on a 2.67 GHz workstation.