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 se-quenced 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 predic-tion 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/.
