Predicting protein-coding genes still remains a significant challenge. Although 
a variety of computational programs that use commonly machine learning methods 
have emerged, the accuracy of predictions remains a low level when implementing 
in large genomic sequences. Moreover, computational gene finding in newly 
sequenced genomes is especially a difficult task due to the absence of a 
training set of abundant validated genes. Here we present a new gene-finding 
program, SCGPred, to improve the accuracy of prediction by combining multiple 
sources of evidence. SCGPred can perform both supervised method in previously 
well-studied genomes and unsupervised one in novel genomes. By testing with 
datasets composed of large DNA sequences from human and a novel genome of 
Ustilago maydi, SCG-Pred gains a significant improvement in comparison to the 
popular ab initio gene predictors. We also demonstrate that SCGPred can 
significantly improve prediction in novel genomes by combining several foreign 
gene finders with similarity alignments, which is superior to other unsupervised 
methods. Therefore, SCG-Pred can serve as an alternative gene-finding tool for 
newly sequenced eukaryotic genomes. The program is freely available at 
http://bio.scu.edu.cn/SCGPred/.