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.

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.

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.

Banana shrimp (Fenneropenaeus merguiensis) is an economically important shrimp in marine aquaculture. Although there is plenty of transcriptome research for this species, the molecular mechanisms in thoracic ganglia of banana shrimp during ovarian maturation have not yet been investigated. Here we report the transcriptomic data of female banana shrimp obtained from thoracic ganglia during ovarian developmental stages. The samples were collected from four stages of ovarian development with two individual shrimps per stage. Total RNA was extracted and used to prepare the sequencing library. Approximately 188 million pair-end raw reads, ranging from 21 to 31 million reads for each library, were generated using an Illumina HiSeq 2500 platform. Quality control was applied to the raw reads before the assembly process. After de novo assembly, the final transcript assembly was generated by vector decontamination, coding regions prediction, redundancy reduction, and foreign sequence depletion. A total of 77,681 transcripts, ranging between 255 and 23,016 bp with an N50 value of 1,167 were obtained to the final assembly. Finally, the final transcripts assembly was evaluated by calculated assembly completeness with Arthropoda orthologous genes dataset. A total of 92.1% of Arthropoda orthologous genes were found in our final assembly. These data might provide benefits for gene discovery, gene annotation, transcript profiling, and other research topics in the context of banana shrimp.

Influenza virus (IFV) can evolve rapidly leading to genetic drifts and shifts resulting in human and animal influenza epidemics and pandemics. The genetic shift that gave rise to the 2009 influenza A/H1N1 pandemic originated from a triple gene reassortment of avian, swine and human IFVs. More minor genetic alterations in genetic drift can lead to influenza drug resistance such as the H274Y mutation associated with oseltamivir resistance. Hence, a rapid tool to detect IFV mutations and the potential emergence of new virulent strains can better prepare us for seasonal influenza outbreaks as well as potential pandemics. Furthermore, identification of specific mutations by closely examining single nucleotide polymorphisms (SNPs) in IFV sequences is essential to classify potential genetic markers associated with potentially dangerous IFV phenotypes. In this study, we developed a novel R library called “SNPer” to analyze quantitative variants in SNPs among IFV subpopulations. The computational SNPer program was applied to three different subpopulations of published IFV genomic information. SNPer queried SNPs data and grouped the SNPs into (1) universal SNPs, (2) likely common SNPs, and (3) unique SNPs. SNPer outperformed manual visualization in terms of time and labor. SNPer took only three seconds with no errors in SNP comparison events compared with 40 hours with errors using manual visualization. The SNPer tool can accelerate the capacity to capture new and potentially dangerous IFV strains to mitigate future influenza outbreaks.