iTerm-PseKNC: a sequence-based tool for predicting bacterial transcriptional terminators.

MOTIVATION: Transcription termination is an important regulatory step of gene expression. If there is no terminator in gene, transcription could not stop, which will result in abnormal gene expression. Detecting such terminators can determine the operon structure in bacterial organisms and improve genome annotation. Thus, accurate identification of transcriptional terminators is essential and extremely important in the research of transcription regulations. RESULTS: In this study, we developed a new predictor called 'iTerm-PseKNC' based on support vector machine to identify transcription terminators. The binomial distribution approach was used to pick out the optimal feature subset derived from pseudo k-tuple nucleotide composition (PseKNC). The 5-fold cross-validation test results showed that our proposed method achieved an accuracy of 95%. To further evaluate the generalization ability of 'iTerm-PseKNC', the model was examined on independent datasets which are experimentally confirmed Rho-independent terminators in Escherichia coli and Bacillus subtilis genomes. As a result, all the terminators in E. coli and 87.5% of the terminators in B. subtilis were correctly identified, suggesting that the proposed model could become a powerful tool for bacterial terminator recognition. AVAILABILITY AND IMPLEMENTATION: For the convenience of most of wet-experimental researchers, the web-server for 'iTerm-PseKNC' was established at http://lin-group.cn/server/iTerm-PseKNC/, by which users can easily obtain their desired result without the need to go through the detailed mathematical equations involved.

Identify origin of replication in Saccharomyces cerevisiae using two-step feature selection technique.

MOTIVATION: DNA replication is a key step to maintain the continuity of genetic information between parental generation and offspring. The initiation site of DNA replication, also called origin of replication (ORI), plays an extremely important role in the basic biochemical process. Thus, rapidly and effectively identifying the location of ORI in genome will provide key clues for genome analysis. Although biochemical experiments could provide detailed information for ORI, it requires high experimental cost and long experimental period. As good complements to experimental techniques, computational methods could overcome these disadvantages. RESULTS: Thus, in this study, we developed a predictor called iORI-PseKNC2.0 to identify ORIs in the Saccharomyces cerevisiae genome based on sequence information. The PseKNC including 90 physicochemical properties was proposed to formulate ORI and non-ORI samples. In order to improve the accuracy, a two-step feature selection was proposed to exclude redundant and noise information. As a result, the overall success rate of 88.53% was achieved in the 5-fold cross-validation test by using support vector machine. AVAILABILITY AND IMPLEMENTATION: Based on the proposed model, a user-friendly webserver was established and can be freely accessed at http://lin-group.cn/server/iORI-PseKNC2.0. The webserver will provide more convenience to most of wet-experimental scholars.

A Brief Survey of Machine Learning Application in Cancerlectin Identification.

Proteins with at least one carbohydrate recognition domain are lectins that can identify and reversibly interact with glycan moiety of glycoconjugates or a soluble carbohydrate. It has been proved that lectins can play various vital roles in mediating signal transduction, cell-cell recognition and interaction, immune defense, and so on. Most organisms can synthesize and secret lectins. A portion of lectins closely related to diverse cancers, called cancerlectins, are involved in tumor initiation, growth and recrudescence. Cancerlectins have been investigated for their applications in the laboratory study, clinical diagnosis and therapy, and drug delivery and targeting of cancers. The identification of cancerlectin genes from a lot of lectins is helpful for dissecting cancers. Several cancerlectin prediction tools based on machine learning approaches have been established and have become an excellent complement to experimental methods. In this review, we comprehensively summarize and expound the indispensable materials for implementing cancerlectin prediction models. We hope that this review will contribute to understanding cancerlectins and provide valuable clues for the study of cancerlectins. Novel systems for cancerlectin gene identification are expected to be developed for clinical applications and gene therapy.