Unitsa Sangket

Background: Influenza viruses remain a significant global health threat due to their high mutation rates and extensive subtype diversity. Genetic variability promotes the mixing of viral subpopulations and facilitates the emergence of novel, potentially virulent, strains. Seasonal influenza vaccines, typically targeting the A/H1N1pdm09 subtype, A/H3N2 subtype, B/Victoria lineage, and B/Yamagata lineage must be reformulated annually, yet their effectiveness and antiviral resistance are continually challenged by antigenic drift. Notably, the H275Y mutation confers a marked reduction in sensitivity to oseltamivir. Comparative variant analysis provides an effective approach for understanding these genetic variations, which is essential for enhancing vaccine effectiveness and predicting drug resistance. Existing tools enable pairwise comparison of variant call format (VCF) files through command-line interfaces but a major limitation is that they only merge variants, and users must manually group variants across multiple samples. Methods: Here, we present ShinyVar, a web-based application developed using the R Shiny framework for comparative variant analysis. The application automatically identifies and visualizes mutations in influenza viruses (IFVs) through interactive comparison of variant profiles across subpopulations. The analysis included the A/Wisconsin/67/2022 (H1N1)pdm09-like virus, A/District of Columbia/27/2023 (H3N2)-like virus, B/Austria/1359417/2021 (B/Victoria lineage)-like virus, and B/Phuket/3073/2013 (B/Yamagata lineage)-like virus as representative subtypes for comparison across the past four years. Results: Variant analysis revealed 181 unique variants in B/Victoria lineage 2025, 101 unique variants in A/H1N1pdm09 2024, 37 unique variants in A/H3N2 subtype 2024, and seven unique variants in B/Yamagata lineage 2025. Structural analysis focused on HA_A/H1N pdm091, NA_A/H1N1 pdm09, and NA_B/Victoria. In HA_A/H1N1pdm09, eight non-synonymous mutations were identified. For downstream structural analysis, p.Thr137Ala or p.Thr120Ala mature numbering led to the loss of a hydrogen bond within the 5J8 antibody binding site, resulting in a slight decrease in binding affinity. In the NA_B/Victoria lineage, eleven non-synonymous mutations marginally disrupted key oseltamivir interaction residues (E276 and R292), resulting in lower binding affinity compared with NA_A/H1N1pdm09. Conclusions: ShinyVar is a fast and efficient tool for variant detection and visualization, making it suitable for monitoring not only IFV evolution but also the evolution of other pathogens. ShinyVar facilitates the identification of novel, shared, and unique variants linked to important traits, including drug resistance and virulence under selective pressure, contributing to improved insights into pathogen evolution and the rational design of vaccines and antiviral drugs.

Background: The development of serodiagnostic tests and vaccines for COVID-19 depends on the identification of epitopes from the SARS-CoV-2 genome. An epitope is the specific part of an antigen that is recognized by the immune system and can elicit an immune response. However, when the genetic variants contained in epitopes are used to develop rapid antigen tests (Ag-RDTs) and DNA or RNA vaccines, test sensitivity and vaccine efficacy can be low. Methods: Here, we developed a “variant on epitope (VOE)” software, a new Python script for identifying variants located on an epitope. Variant analysis and sensitivity calculation for seven recommended epitopes were processed by VOE. Variants in 1,011 Omicron SRA reads from two variant databases (BCFtools and SARS-CoV-2-Freebayes) were processed by VOE. Results: A variant with HIGH or MODERATE impact was found on all epitopes from both variant databases except the epitopes KLNDLCFTNV, RVQPTES, LKPFERD, and ITLCFTLKRK on the S gene and ORF7a gene. All epitope variants from the BCFtools and SARS-CoV-2 Freebayes variant databases showed about 100% sensitivity except epitopes APGQTGK and DSKVGGNYN on the S gene, which showed respective sensitivities of 28.4866% and 6.8249%, and 87.7349% and 71.1177%. Conclusions: Therefore, the epitopes KLNDLCFTNV, RVQPTES, LKPFERD, and ITLCFTLKRK may be useful for the development of an epitope-based peptide vaccine and GGDGKMKD on the N gene may be useful for the development of serodiagnostic tests. Moreover, VOE can also be used to analyze other epitopes, and a new variant database for VOE may be further established when a new variant of SARS-CoV-2 emerges.

Background: Genetic variants may potentially play a contributing factor in the development of diseases. Several genetic disease databases are used in medical research and diagnosis but the web applications used to search these databases for disease-associated variants have limitations. The application may not be able to search for large-scale genetic variants, the results of searches may be difficult to interpret and variants mapped from the latest reference genome (GRCH38/hg38) may not be supported. Methods: In this study, we developed a novel R library called “DisVar” to identify disease-associated genetic variants in large-scale individual genomic data. This R library is compatible with variants from the latest reference genome version. DisVar uses five databases of disease-associated variants. Over 100 million variants can be simultaneously searched for specific associated diseases. Results: The package was evaluated using 24 Variant Call Format (VCF) files (215,054 to 11,346,899 sites) from the 1000 Genomes Project. Disease-associated variants were detected in 298,227 hits across all the VCF files, taking a total of 63.58 m to complete. The package was also tested on ClinVar’s VCF file (2,120,558 variants), where 20,657 hits associated with diseases were identified with an estimated elapsed time of 45.98 s. Conclusions: DisVar can overcome the limitations of existing tools and is a fast and effective diagnostic and preventive tool that identifies disease-associated variations from large-scale genetic variants against the latest reference genome.

Human enterovirus causes various clinical manifestations in the form of rashes, febrile illness, flu-like illness, uveitis, hand–foot–mouth disease (HFMD), herpangina, meningitis, and encephalitis. Enterovirus A71 and coxsackievirus are significant causes of epidemic HFMD worldwide, especially in children aged from birth to five years old. The enterovirus genotype variants causing HFMD epidemics have been reported increasingly worldwide in the last decade. We aim to use simple and robust molecular tools to investigate human enteroviruses circulating among kindergarten students at genotype and subgenotype levels. With the partial 5′-UTR sequencing analysis as a low-resolution preliminary grouping tool, ten enterovirus A71 (EV-A71) and coxsackievirus clusters were identified among 18 symptomatic cases and 14 asymptomatic cases in five kindergartens in Bangkok, Thailand, between July 2019 and January 2020. Two occurrences of a single clone causing an infection cluster were identified (EV-A71 C1-like subgenotype and coxsackievirus A6). Random amplification-based sequencing using MinION (Oxford Nanopore Technology) helped identify viral transmission between two closely related clones. Diverse genotypes co-circulating among children in kindergartens are reservoirs for new genotype variants emerging, which might be more virulent or better at immune escape. Surveillance of highly contagious enterovirus in communities is essential for disease notifications and controls.

Background: Differential gene expression analysis using RNA sequencing technology (RNA-Seq) has become the most popular technique in transcriptome research. Although many R packages have been developed to analyze differentially expressed genes (DEGs), several evaluations have shown that no single DEG analysis method outperforms all others. The validity of DEG identification could be increased by using multiple methods and producing the consensus results. However, DEG analysis methods are complex and most of them require prior knowledge of a programming language or command-line shell. Users who do not have this knowledge need to invest time and effort to acquire it. Methods: We developed a novel web application called “bestDEG” to automatically analyze DEGs with different tools and compare the results. A differential expression (DE) analysis pipeline was created combining the edgeR, DESeq2, NOISeq, and EBSeq packages; selected because they use different statistical methods to identify DEGs. bestDEG was evaluated on human datasets from the MicroArray Quality Control (MAQC) project. Results: The performance of the bestDEG web application with the human datasets showed excellent results, and the consensus method outperformed the other DE analysis methods in terms of precision (94.71%) and specificity (97.01%). bestDEG is a rapid and efficient tool to analyze DEGs. With bestDEG, users can select DE analysis methods and parameters in the user-friendly web interface. bestDEG also provides a Venn diagram and a table of results. Moreover, the consensus method of this tool can maximize the precision or minimize the false discovery rate (FDR), which reduces the cost of gene expression validation by minimizing wet-lab experiments.

Phylogenetic tree analysis is a process for finding the highest possible revolution tree history of an interested organism. The important step of the process is multiple sequences alignment (MSA) which is operated using any MSA tool that produces a result in blocks of the Phylip format. Bioinformaticians have to manually determine and trim gaps of the MSA blocks using relevant tools of a software package in the off-line mode. The data blocks need to be manually cut-and-pasted between these tools. This working steps tend to be error-prone and time consuming. In addition, improper algorithm selection for tree inferring without applying an MSA similarity score tends to generate the phylogenetic tree with low accuracy and also take much more time. In this work, we present a new practical approach for the phylogenetic tree analysis applying our enhancement for the similarity score estimation and gaps trimming of the MSA blocks. We propose \textit{in-silico} algorithms for automating the concerned similarity score estimation and gaps trimming, and deploy them as web services. We demonstrate the web services utilized by composing them into an integrated stateful WSDL workflow. Our case study datasets are a complete coding sequences (CDS) and sets of complete genome of Dengue Viruses - 2, fetched from the NCBI RefSeq nucleotide database. Our proposed algorithms have correctly returned results, verified and satisfied by our bioinformaticians. Our distributions, user manuals and endpoints of the web services, and the open source programs are available at https://bioservices.sci.psu.ac.th.

Chemical communication plays important roles in the social behavior of ants making them one of the most successful groups of animals on earth. However, the molecular evolutionary process responsible for their chemosensory adaptation is still elusive. Recent advances in genomic studies have led to the identification of large odorant receptor (Or) gene repertoires from ant genomes providing fruitful materials for molecular evolution analysis. The aim of this study was to test the hypothesis that diversification of this gene family is involved in olfactory adaptation of each species. We annotated the Or genes from the genome sequences of two leaf-cutter ants, Acromyrmex echinatior and Atta cephalotes (385 and 376 putative functional genes, respectively). These were used, together with Or genes from Camponotus floridanus, Harpegnathos saltator, Pogonomyrmex barbatus, Linepithema humile, Cerapachys biroi, Solenopsis invicta and Apis mellifera, in molecular evolution analysis. Like the Or family in other insects, ant Or genes evolve by the birth-and-death model of gene family evolution. Large gene family expansions involving tandem gene duplications, and gene gains outnumbering losses, are observed. Codon analysis of genes in lineage-specific expansion clades revealed signatures of positive selection on the candidate cuticular hydrocarbon receptor genes (9-exon subfamily) of Cerapachys biroi, Camponotus floridanus, Acromyrmex echinatior and Atta cephalotes. Positively selected amino acid positions are primarily in transmembrane domains 3 and 6, which are hypothesized to contribute to the odor-binding pocket, presumably mediating changing ligand specificity. This study provides support for the hypothesis that some ant lineage-specific Or genes have evolved under positive selection. Newly duplicated genes particularly in the candidate cuticular hydrocarbon receptor clade that have evolved under positive selection may contribute to the highly sophisticated lineage-specific chemical communication in each ant species.