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, SCGPred 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, SCGPred 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/.

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/.
Algorithms ; Chromosome Mapping ; methods ; Computational Biology ; methods ; Exons ; genetics ; Genes, Fungal ; genetics ; Genome, Fungal ; Genome, Human ; Humans ; Reproducibility of Results ; Software ; Ustilago ; genetics

Objective To establish rabbit model of scoliosis induced with stable asymmetric lumbar loads. Methods Scoliosis was induced in 10 two-month-old New Zealand rabbits using 316L stainless steel springs placed between the unilateral transverse processes of L2 and L5. Serial radiographs were documented before and at 1, 4, 8, 9 and 12 weeks after the operation. At weeks, the rabbits were randomly divided into SR group (n=5) with the spring removed and SK group (n=5) without spring removal. Results All the rabbits survived the experiment with Cobb angle all greater than 10°at the end of the experiment. Significant changes were found in the Cobb angles and kyphotic angles at 1, 4 and 8 weeks after the operation (P<0.05). At 8 weeks, the Cobb angle, the kyphotic angle and the length of the spring were similar between SR and SK groups (P>0.05), and in the 4 weeks following spring removal in SR group, the Cobb angle and the kyphosis decreased significantly compared with those in SK group (P<0.05). Micro-CT showed that the BV/TV of the concave side was greater than that of the convex side. The length of the spring did not show obvious changes during the experiment (P>0.05). Conclusion Asymmetric lumbar loading is a convenient, time-saving, and highly reproducible approach for establishing rabbit models of scoliosis.

Objective To establish rabbit model of scoliosis induced with stable asymmetric lumbar loads. Methods Scoliosis was induced in 10 two-month-old New Zealand rabbits using 316L stainless steel springs placed between the unilateral transverse processes of L2 and L5. Serial radiographs were documented before and at 1, 4, 8, 9 and 12 weeks after the operation. At weeks, the rabbits were randomly divided into SR group (n=5) with the spring removed and SK group (n=5) without spring removal. Results All the rabbits survived the experiment with Cobb angle all greater than 10°at the end of the experiment. Significant changes were found in the Cobb angles and kyphotic angles at 1, 4 and 8 weeks after the operation (P<0.05). At 8 weeks, the Cobb angle, the kyphotic angle and the length of the spring were similar between SR and SK groups (P>0.05), and in the 4 weeks following spring removal in SR group, the Cobb angle and the kyphosis decreased significantly compared with those in SK group (P<0.05). Micro-CT showed that the BV/TV of the concave side was greater than that of the convex side. The length of the spring did not show obvious changes during the experiment (P>0.05). Conclusion Asymmetric lumbar loading is a convenient, time-saving, and highly reproducible approach for establishing rabbit models of scoliosis.