Motivation: Pseudoknots in RNA structures make visualization of RNA structures difficult. Even if a pseudoknot itself is represented without a crossing, visualization of the entire RNA structure with a pseudoknot often results in a drawing with crossings between the pseudoknot and other structural elements, and requires additional intervention by the user to ensure that the structure graph is overlap-free. Many programs such as web services prefer to obtain an overlap-free graph in one-shot rather than get a graph with overlaps to be edited. There are few programs for visualizing RNA pseudoknots, and PseudoViewer has been the almost only program that automatically draws RNA secondary structures with pseudoknots. The previous version of PseudoViewer visualizes all the known types of RNA pseudoknots as planar drawings, but visualizes some hypothetical pseudoknots as non-planar drawings. Results: We developed a new version of PseudoViewer for efficiently visualizing large RNA structures with any types of pseudoknots, both known and hypothetical, as planar drawings in one-shot. It is about 10 times faster than the previous algorithm, and produces a more compact and aesthetic structure drawing. PseudoViewer3 supports both web services and web applications. Availability: The new version of PseudoViewer, PseudoViewer3, is available at http://pseudoviewer.inha.ac.kr. Contact:khan@inha.ac.kr Supplementary information: Supplementary data are available at Bioinformatics online.

Background: Several computational methods have been developed to predict protein-protein interactions from amino acid sequences, but most of those methods are intended for the interactions within a species rather than for interactions across different species. Methods for predicting interactions between homogeneous proteins are not appropriate for finding those between heterogeneous proteins since they do not distinguish the interactions between proteins of the same species from those of different species. Results: We developed a new method for representing a protein sequence of variable length in a frequency vector of fixed length, which encodes the relative frequency of three consecutive amino acids of a sequence. We built a support vector machine (SVM) model to predict human proteins that interact with virus proteins. In two types of viruses, human papillomaviruses (HPV) and hepatitis C virus (HCV), our SVM model achieved an average accuracy above 80%, which is higher than that of another SVM model with a different representation scheme. Using the SVM model and Gene Ontology (GO) annotations of proteins, we predicted new interactions between virus proteins and human proteins. Conclusions: Encoding the relative frequency of amino acid triplets of a protein sequence is a simple yet powerful representation method for predicting protein-protein interactions across different species. The representation method has several advantages: (1) it enables a prediction model to achieve a better performance than other representations, (2) it generates feature vectors of fixed length regardless of the sequence length, and (3) the same representation is applicable to different types of proteins.

Visualizing RNA secondary structures and pseudoknot structures is essential to bioinformatics systems that deal with RNA structures. However, many bioinformatics systems use heterogeneous data structures and incompatible software components, so integration of software components (including a visualization component) into a system can be hindered by incompatibilities between the components of the system. This paper presents an XML web service and web application program for visualizing RNA secondary structures with pseudoknots. Experimental results show that the PseudoViewer web service and web application are useful for resolving many problems with incompatible software components as well as for visualizing large-scale RNA secondary structures with pseudoknots of any type. The web service and web application are available at http://pseudoviewer.inha.ac.kr/ .

Background: Genetic interaction profiles are highly informative and helpful for understanding the functional linkages between genes, and therefore have been extensively exploited for annotating gene functions and dissecting specific pathway structures. However, our understanding is rather limited to the relationship between double concurrent perturbation and various higher level phenotypic changes, e.g. those in cells, tissues or organs. Modifier screens, such as synthetic genetic arrays (SGA) can help us to understand the phenotype caused by combined gene mutations. Unfortunately, exhaustive tests on all possible combined mutations in any genome are vulnerable to combinatorial explosion and are infeasible either technically or financially. Therefore, an accurate computational approach to predict genetic interaction is highly desirable, and such methods have the potential of alleviating the bottleneck on experiment design. Results: In this work, we introduce a computational systems biology approach for the accurate prediction of pairwise synthetic genetic interactions (SGI). First, a high-coverage and high-precision functional gene network (FGN) is constructed by integrating protein-protein interaction (PPI), protein complex and gene expression data; then, a graph-based semi-supervised learning (SSL) classifier is utilized to identify SGI, where the topological properties of protein pairs in weighted FGN is used as input features of the classifier. We compare the proposed SSL method with the state-of-the-art supervised classifier, the support vector machines (SVM), on a benchmark dataset in S. cerevisiae to validate our method's ability to distinguish synthetic genetic interactions from non-interaction gene pairs. Experimental results show that the proposed method can accurately predict genetic interactions in S. cerevisiae (with a sensitivity of 92% and specificity of 91%). Noticeably, the SSL method is more efficient than SVM, especially for very small training sets and large test sets. Conclusions: We developed a graph-based SSL classifier for predicting the SGI. The classifier employs topological properties of weighted FGN as input features and simultaneously employs information induced from labelled and unlabelled data. Our analysis indicates that the topological properties of weighted FGN can be employed to accurately predict SGI. Also, the graph-based SSL method outperforms the traditional standard supervised approach, especially when used with small training sets. The proposed method can alleviate experimental burden of exhaustive test and provide a useful guide for the biologist in narrowing down the candidate gene pairs with SGI. The data and source code implementing the method are available from the website: http://home.ustc.edu.cn/~yzh33108/GeneticInterPred.htm

The Human Protein Interaction Database (http://www.hpid.org) was designed (1) to provide human protein interaction information pre-computed from existing structural and experimental data, (2) to predict potential interactions between proteins submitted by users and (3) to provide a depository for new human protein interaction data from users. Two types of interaction are available from the pre-computed data: (1) interactions at the protein superfamily level and (2) those transferred from the interactions of yeast proteins. Interactions at the superfamily level were obtained by locating known structural interactions of the PDB in the SCOP domains and identifying homologs of the domains in the human proteins. Interactions transferred from yeast proteins were obtained by identifying homologs of the yeast proteins in the human proteins. For each human protein in the database and each query submitted by users, the protein superfamilies and yeast proteins assigned to the protein are shown, along with their interacting partners. We have also developed a set of web-based programs so that users can visualize and analyze protein interaction networks in order to explore the networks further. http://www.hpid.org.

Visualizing RNA pseudoknot structures is computationally more difficult than depicting RNA secondary structures, because a drawing of a pseudoknot structure is a graph (and possibly a nonplanar graph) with inner cycles within the pseudoknot, and possibly outer cycles formed between the pseudoknot and other structural elements. We previously developed PseudoViewer for visualizing H-type pseudoknots. PseudoViewer2 improves on the first version in many ways: (i) PseudoViewer2 is more general because it can visualize a pseudoknot of any type, including H-type pseudoknots, as a planar graph; (ii) PseudoViewer2 computes a drawing of RNA structures much more efficiently and is an order of magnitude faster in actual running time; and (iii) PseudoViewer2 is a web-based application program. Experimental results demonstrate that PseudoViewer2 generates an aesthetically pleasing drawing of pseudoknots of any type and that the new representation offered by PseudoViewer2 ensures uniform and clear drawings, with no edge crossing, for all types of pseudoknots. The PseudoViewer2 algorithm is the first developed for the automatic drawing of RNA secondary structures, including pseudoknots of any type. PseudoViewer2 is accessible at http://wilab.inha.ac.kr/pseudoviewer2/.