MOTIVATION: Computational gene identification plays an important role in genome 
projects. The approaches used in gene identification programs are often tuned to 
one particular organism, and accuracy for one organism or class of organism does 
not necessarily translate to accurate predictions for other organisms. In this 
paper we evaluate five computer programs on their ability to locate coding 
regions and to predict gene structure in Neurospora crassa. One of these 
programs (FFG) was designed specifically for gene-finding in N.crassa, but the 
model parameters have not yet been fully 'tuned', and the program should thus be 
viewed as an initial prototype. The other four programs were neither designed 
nor tuned for N.crassa.
RESULTS: We describe the data sets on which the experiments were performed, the 
approaches employed by the five algorithms: GenScan, HMMGene, GeneMark, Pombe 
and FFG, the methodology of our evaluation, and the results of the experiments. 
Our results show that, while none of the programs consistently performs well, 
overall the GenScan program has the best performance on sensitivity and Missing 
Exons (ME) while the HMMGene and FFG programs have good performance in locating 
the exons roughly. Additional work motivated by this study includes the creation 
of a tool for the automated evaluation of gene-finding programs, the collection 
of larger and more reliable data sets for N.crassa, parameterization of the 
model used in FFG to produce a more accurate gene-finding program for this 
species, and a more in-depth evaluation of the reasons that existing programs 
generally fail for N.crassa.
AVAILABILITY: Data sets, the FFG program source code, and links to the other 
programs analyzed are available at http://jerry.cs.uga.edu/~wang/genefind.html.
CONTACT: eileen@cs.uga.edu.