One of the most important and challenging problems in biomedicine and genomics is how to identify the disease genes. In this study, we developed a computational method to identify colorectal cancer-related genes based on (i) the gene expression profiles, and (ii) the shortest path analysis of functional protein association networks. The former has been used to select differentially expressed genes as disease genes for quite a long time, while the latter has been widely used to study the mechanism of diseases. With the existing protein-protein interaction data from STRING ( S earch T ool for the R etrieval of I nteracting G enes), a weighted functional protein association network was constructed. By means of the mRMR ( M aximum R elevance M inimum R edundancy) approach, six genes were identified that can distinguish the colorectal tumors and normal adjacent colonic tissues from their gene expression profiles. Meanwhile, according to the shortest path approach, we further found an additional 35 genes, of which some have been reported to be relevant to colorectal cancer and some are very likely to be relevant to it. Interestingly, the genes we identified from both the gene expression profiles and the functional protein association network have more cancer genes than the genes identified from the gene expression profiles alone. Besides, these genes also had greater functional similarity with the reported colorectal cancer genes than the genes identified from the gene expression profiles alone. All these indicate that our method as presented in this paper is quite promising. The method may become a useful tool, or at least plays a complementary role to the existing method, for identifying colorectal cancer genes. It has not escaped our notice that the method can be applied to identify the genes of other diseases as well.

Aquaculture in China accounts for nearly 70% of world aquaculture production. Aquaculture, including a wide variety of freshwater and marine fishes, shellfish, crustaceans, and aquatic plants, has become one of the most vital primary industries and a center of economic activity within the local and global economies. Along with the development of aquaculture, concerns come about such problems in the industry such as aquatic pollution, disease, genetic degradation of aquaculture species, decline of comparative profitability, lack of knowledge on market risks, and financial crises. Thus, there is a need to acquire further knowledge on this industry and provide sound suggestions for its sustainable development. This review aims to identify the current state of and challenges facing the aquaculture industry in China and to provide some suggestions for its sustainable development.

Prediction of protein-protein interaction (PPI) sites is one of the most challenging problems in computational biology. Although great progress has been made by employing various machine learning approaches with numerous characteristic features, the problem is still far from being solved. In this study, we developed a novel predictor based on Random Forest (RF) algorithm with the Minimum Redundancy Maximal Relevance (mRMR) method followed by incremental feature selection (IFS). We incorporated features of physicochemical/biochemical properties, sequence conservation, residual disorder, secondary structure and solvent accessibility. We also included five 3D structural features to predict protein-protein interaction sites and achieved an overall accuracy of 0.672997 and MCC of 0.347977. Feature analysis showed that 3D structural features such as Depth Index (DPX) and surface curvature (SC) contributed most to the prediction of protein-protein interaction sites. It was also shown via site-specific feature analysis that the features of individual residues from PPI sites contribute most to the determination of protein-protein interaction sites. It is anticipated that our prediction method will become a useful tool for identifying PPI sites, and that the feature analysis described in this paper will provide useful insights into the mechanisms of interaction.

No abstract

Aptamers are oligonucleic acid or peptide molecules that bind to specific target molecules. As a novel and powerful class of ligands, aptamers are thought to have excellent potential for applications in the fields of biosensing, diagnostics and therapeutics. In this study, a new method for predicting aptamer-target interacting pairs was proposed by integrating features derived from both aptamers and their targets. Features of nucleotide composition and traditional amino acid composition as well as pseudo amino acid were utilized to represent aptamers and targets, respectively. The predictor was constructed based on Random Forest and the optimal features were selected by using the maximum relevance minimum redundancy (mRMR) method and the incremental feature selection (IFS) method. As a result, 81.34% accuracy and 0.4612 MCC were obtained for the training dataset, and 77.41% accuracy and 0.3717 MCC were achieved for the testing dataset. An optimal feature set of 220 features were selected, which were considered as the ones that contributed significantly to the interacting aptamer-target pair predictions. Analysis of the optimal feature set indicated several important factors in determining aptamer-target interactions. It is anticipated that our prediction method may become a useful tool for identifying aptamer-target pairs and the features selected and analyzed in this study may provide useful insights into the mechanism of interactions between aptamers and targets.

The domains are the structural and functional units of proteins. With the avalanche of protein sequences generated in the postgenomic age, it is highly desired to develop effective methods for predicting the protein domains according to the sequences information alone, so as to facilitate the structure prediction of proteins and speed up their functional annotation. However, although many efforts have been made in this regard, prediction of protein domains from the sequence information still remains a challenging and elusive problem. Here, a new method was developed by combing the techniques of RF (random forest), mRMR (maximum relevance minimum redundancy), and IFS (incremental feature selection), as well as by incorporating the features of physicochemical and biochemical properties, sequence conservation, residual disorder, secondary structure, and solvent accessibility. The overall success rate achieved by the new method on an independent dataset was around 73%, which was about 28–40% higher than those by the existing method on the same benchmark dataset. Furthermore, it was revealed by an in-depth analysis that the features of evolution, codon diversity, electrostatic charge, and disorder played more important roles than the others in predicting protein domains, quite consistent with experimental observations. It is anticipated that the new method may become a high-throughput tool in annotating protein domains, or may, at the very least, play a complementary role to the existing domain prediction methods, and that the findings about the key features with high impacts to the domain prediction might provide useful insights or clues for further experimental investigations in this area. Finally, it has not escaped our notice that the current approach can also be utilized to study protein signal peptides, B-cell epitopes, HIV protease cleavage sites, among many other important topics in protein science and biomedicine.

The glutamate g-carboxylation plays a pivotal part in a number of important human diseases. However, traditional protein g-carboxylation site detection by experimental approaches are often laborious and time-consuming. In this study, we initiated an attempt for the computational prediction of protein g-carboxylation sites. We developed a new method for predicting the g-carboxylation sites based on a Random Forest method. As a result, 90.44% accuracy and 0.7739 MCC value were obtained for the training dataset, and 89.83% accuracy and 0.7448 MCC value for the testing dataset. Our method considered several features including sequence conservation, residual disorder, secondary structures, solvent accessibility, physicochemical/ biochemical properties and amino acid occurrence frequencies. By means of the feature selection algorithm, an optimal set of 327 features were selected; these features were considered as the ones that contributed significantly to the prediction of protein g-carboxylation sites. Analysis of the optimal feature set indicated several important factors in determining the g-carboxylation and a possible consensus sequence of the g-carboxylation recognition site (g-CRS) was suggested. These may shed some light on the in-depth understanding of the mechanisms of g-carboxylation, providing guidelines for experimental validation.

BackgroundThe emergence of vertebrates is characterized by a strong increase in miRNA families. MicroRNAs interact broadly with many transcripts, and the evolution of such a system is intriguing. However, evolutionary questions concerning the origin of miRNA genes and their subsequent evolution remain unexplained.ResultsIn order to systematically understand the evolutionary relationship between miRNAs gene and their function, we classified human known miRNAs into eight groups based on their evolutionary ages estimated by maximum parsimony method. New miRNA genes with new functional sequences accumulated more dynamically in vertebrates than that observed in Drosophila. Different levels of evolutionary selection were observed over miRNA gene sequences with different time of origin. Most genic miRNAs differ from their host genes in time of origin, there is no particular relationship between the age of a miRNA and the age of its host genes, genic miRNAs are mostly younger than the corresponding host genes. MicroRNAs originated over different time-scales are often predicted/verified to target the same or overlapping sets of genes, opening the possibility of substantial functional redundancy among miRNAs of different ages. Higher degree of tissue specificity and lower expression level was found in young miRNAs.ConclusionsOur data showed that compared with protein coding genes, miRNA genes are more dynamic in terms of emergence and decay. Evolution patterns are quite different between miRNAs of different ages. MicroRNAs activity is under tight control with well-regulated expression increased and targeting decreased over time. Our work calls attention to the study of miRNA activity with a consideration of their origin time.