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.

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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.

Here, we report the complete genome sequences of mannanase-producing bacteria, namely, Niallia sp. strain Man26 and Bacillus subtilis strain Man122, isolated from the intestine of Penaeus monodon , the black tiger shrimp. Mannanases are used in various industries, such as food, animal feed, and biorefinery, to hydrolyze mannan to oligomers and mannose.

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