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.

Abstract Understanding the complex nature of multi-functional interactions among genes is crucial for interpreting omics data. We developed NetCrafter, an ontology-driven platform for constructing de novo gene networks that are specific to each input gene list and quantitatively defined by ontology-weighted similarity. By incorporating the probabilistic association of ontology or curated gene sets into a weighted Tanimoto similarity metric, NetCrafter transforms enrichment results into quantitative semantic similarity scores between genes, enabling the creation of context-specific statistical networks. These networks can be further decomposed into optimal sub-networks, facilitating multi-functional interpretation and the identification of gene interaction hotspots. NetCrafter also supports meta-analysis across heterogeneous datasets by integrating multiple gene lists through consensus ontology scoring. Importantly, this list-specific, quantitative framework reveals functional hotspots and target-biomarker relationships – even in cases where ontology terms alone are not predictive of node-level attributes such as CRISPR efficacy. NetCrafter provides an interactive platform for constructing and interpreting dynamic, context-specific gene networks, leveraging ontology-based functional associations to uncover underlying mechanisms and identify key nodes. It is freely available at https://netcrafter.sookmyung.ac.kr and integrated into Q-omics platform (https://qomics.io) to enhance the utility of cancer omics data.

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].

MOTIVATION: It is expected that individual genes have intrinsically different variability in the global expressional trend among them. Thus, the consideration of gene-specific expressional properties will help us to distinguish target-selective gene expression over non-selective over-expression. RESULTS: The re-standardization and integration of heterogeneous microarray datasets, available from public databases, have enabled us to determine the global expression properties of individual genes across a wide variety of experimental conditions and samples. The global averages and SDs of expression for each gene in the integrated microarray datasets were found to be intrinsic properties, which were consistent among independent collections of datasets using different microarray platforms. Using the gene-specific intrinsic parameters to rescale the microarray data, we were able to distinguish novel selective gene expression [cartilage oligomeric matrix protein (COMP) and Collagen X] in breast cancer tissues from non-selective over-expression, a difference that has not been detectable by conventional methods. AVAILABILITY AND IMPLEMENTATION: The web-based tool for GS-LAGE is available at http://lage.sookmyung.ac.kr

MOTIVATION: The identification of the change of gene expression in multifactorial diseases, such as breast cancer is a major goal of DNA microarray experiments. Here we present a new data mining strategy to better analyze the marginal difference in gene expression between microarray samples. The idea is based on the notion that the consideration of gene's behavior in a wide variety of experiments can improve the statistical reliability on identifying genes with moderate changes between samples. RESULTS: The availability of a large collection of array samples sharing the same platform in public databases, such as NCBI GEO, enabled us to re-standardize the expression intensity of a gene using its mean and variation in the wide variety of experimental conditions. This approach was evaluated via the re-identification of breast cancer-specific gene expression. It successfully prioritized several genes associated with breast tumor, for which the expression difference between normal and breast cancer cells was marginal and thus would have been difficult to recognize using conventional analysis methods. Maximizing the utility of microarray data in the public database, it provides a valuable tool particularly for the identification of previously unrecognized disease-related genes. AVAILABILITY: A user friendly web-interface (http://compbio.sookmyung.ac.kr/~lage/) was constructed to provide the present large-scale approach for the analysis of GEO microarray data (GS-LAGE server).

Motivation: Numerous methods for predicting β-turns in proteins have been developed based on various computational schemes. Here, we introduce a new method of β-turn prediction that uses the support vector machine (SVM) algorithm together with predicted secondary structure information. Various parameters from the SVM have been adjusted to achieve optimal prediction performance. Results: The SVM method achieved excellent performance as measured by the Matthews correlation coefficient (MCC = 0.45) using a 7-fold cross validation on a database of 426 non-homologous protein chains. To our best knowledge, this MCC value is the highest achieved so far for predicting β-turn. The overall prediction accuracy Qtotal was 77.3%, which is the best among the existing prediction methods. Among its unique attractive features, the present SVM method avoids overtraining and compresses information and provides a predicted reliability index. Availability: The algorithm is available via a web server on: http://serine.umdnj.edu/~zhangq3/betaturn/ Contact:[email protected] Supplementary information:http://serine.umdnj.edu/~zhangq3/betaturn