BACKGROUND: QStrain is an integrated resource platform designed to facilitate viral genome analysis and the rational design of nucleic acid therapeutics. RESULTS: The system integrates reference-guided multiple sequence alignment of high-risk viral pathogens, including Severe Acute Respiratory Syndrome Coronavirus 2, Lassa virus, Severe Fever with Thrombocytopenia Syndrome virus, Middle East Respiratory Syndrome Coronavirus, Influenza A virus subtype H5N1 and H7N9, Nipah virus, Respiratory Syncytial Virus, and Dengue virus, with a growing database of viral genomes. QStrain provides versatile visualization modules such as frequency profiles, conservation scores, RNA expression patterns, and phylogenetic trees to enhance the interpretation of viral sequence variation. By identifying highly conserved genomic regions, QStrain supports the prediction of candidate therapeutic targets and enables the automated design of small interfering RNA (siRNA) and antisense oligonucleotides (ASOs). Additional features include off-target searching and 2D structure viewing for comprehensive assessment of therapeutic potential. CONCLUSIONS: Ultimately, QStrain ( https://qstrain.sookmyung.ac.kr ) provides a critical, user-friendly framework to accelerate the design pipeline of nucleic acid-based therapies, enhancing preparedness against current and future viral outbreaks.

SUMMARY: The mutational status of specific cancer lineages can affect the sensitivity to or resistance against cancer drugs. The MACE database provides web-based interactive tools for interpreting large chemical screening and gene expression datasets of cancer cell lines in terms of mutation and lineage categories. GI50 data of chemicals against individual NCI60 cell lines were normalized and organized to statistically identify mutation- or lineage-specific chemical responses. Similarly, DNA microarray data on NCI60 cell lines were processed to analyze mutation- or lineage-specific gene expression signatures. A combined analysis of GI50 and gene expression data to find potential associations between chemicals and genes is also a capability of this system. This database will provide extensive, systematic information to identify lineage- or mutation-specific anticancer agents and related gene targets. AVAILABILITY AND IMPLEMENTATION: The MACE web database is available at http://mace.sookmyung.ac.kr/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. CONTACT: [email protected].

SUMMARY: Manual curation and validation of large-scale biological pathways are required to obtain high-quality pathway databases. In a typical curation process, model validation and model update based on appropriate feedback are repeated and requires considerable cooperation of scientists. We have developed a CSO (Cell System Ontology) validator to reduce the repetition and time during the curation process. This tool assists in quickly obtaining agreement among curators and domain experts and in providing a consistent and accurate pathway database. AVAILABILITY: The tool is available on http://csovalidator.csml.org. CONTACT: [email protected].

SUMMARY: The Macrophage Pathway Knowledgebase (MACPAK) is a computational system that allows biomedical researchers to query and study the dynamic behaviors of macrophage molecular pathways. It integrates the knowledge of 230 reviews that were carefully checked by specialists for their accuracy and then converted to 230 dynamic mathematical pathway models. MACPAK comprises a total of 24 009 entities and 12 774 processes and is described in the Cell System Markup Language (CSML), an XML format that runs on the Cell Illustrator platform and can be visualized with a customized Cytoscape for further analysis. AVAILABILITY: MACPAK can be accessed via an interactive web site at http://macpak.csml.org. The CSML pathway models are available under the Creative Commons license.

BACKGROUND: Modeling in systems biology is vital for understanding the complexity of biological systems across scales and predicting system-level behaviors. To obtain high-quality pathway databases, it is essential to improve the efficiency of model validation and model update based on appropriate feedback. RESULTS: We have developed a new method to guide creating novel high-quality biological pathways, using a rule-based validation. Rules are defined to correct models against biological semantics and improve models for dynamic simulation. In this work, we have defined 40 rules which constrain event-specific participants and the related features and adding missing processes based on biological events. This approach is applied to data in Cell System Ontology which is a comprehensive ontology that represents complex biological pathways with dynamics and visualization. The experimental results show that the relatively simple rules can efficiently detect errors made during curation, such as misassignment and misuse of ontology concepts and terms in curated models. CONCLUSIONS: A new rule-based approach has been developed to facilitate model validation and model complementation. Our rule-based validation embedding biological semantics enables us to provide high-quality curated biological pathways. This approach can serve as a preprocessing step for model integration, exchange and extraction data, and simulation.

BACKGROUND: Clearly visualized biopathways provide a great help in understanding biological systems. However, manual drawing of large-scale biopathways is time consuming. We proposed a grid layout algorithm that can handle gene-regulatory networks and signal transduction pathways by considering edge-edge crossing, node-edge crossing, distance measure between nodes, and subcellular localization information from Gene Ontology. Consequently, the layout algorithm succeeded in drastically reducing these crossings in the apoptosis model. However, for larger-scale networks, we encountered three problems: (i) the initial layout is often very far from any local optimum because nodes are initially placed at random, (ii) from a biological viewpoint, human layouts still exceed automatic layouts in understanding because except subcellular localization, it does not fully utilize biological information of pathways, and (iii) it employs a local search strategy in which the neighborhood is obtained by moving one node at each step, and automatic layouts suggest that simultaneous movements of multiple nodes are necessary for better layouts, while such extension may face worsening the time complexity. RESULTS: We propose a new grid layout algorithm. To address problem (i), we devised a new force-directed algorithm whose output is suitable as the initial layout. For (ii), we considered that an appropriate alignment of nodes having the same biological attribute is one of the most important factors of the comprehension, and we defined a new score function that gives an advantage to such configurations. For solving problem (iii), we developed a search strategy that considers swapping nodes as well as moving a node, while keeping the order of the time complexity. Though a naïve implementation increases by one order, the time complexity, we solved this difficulty by devising a method that caches differences between scores of a layout and its possible updates. CONCLUSION: Layouts of the new grid layout algorithm are compared with that of the previous algorithm and human layout in an endothelial cell model, three times as large as the apoptosis model. The total cost of the result from the new grid layout algorithm is similar to that of the human layout. In addition, its convergence time is drastically reduced (40% reduction).

This paper proposes a novel approach to integrating heterogeneous XML DTDs. With this approach, an information agent can be easily extended to integrate heterogeneous XML-based contents and perform federated search. Based on a tree grammar inference technique, this approach derives an integrated view of XML DTDs in an information integration framework. The derivation takes advantages of naming and structural similarities among DTDs in similar domains. The complete approach consists of three main steps. (1) DTD clustering clusters DTDs in similar domains into classes. (2) Schema learning applies a tree grammar inference technique to generate a set of tree grammar rules from the DTDs in a class from the previous step. (3) Minimization optimizes the rules generated in the previous step and transforms them into an integrated view. We have implemented the proposed approach into a system called DEEP and tested the system on artificial and real domains. The experimental results reveal that this system can effectively and efficiently integrate radically different DTDs.