Background: MicroRNAs (miRNAs) have been known to play an important role in several biological processes in both animals and plants. Although several tools for miRNA and target identification are available, the number of tools tailored towards plants is limited, and those that are available have specific functionality, lack graphical user interfaces, and restrict the number of input sequences. Large-scale computational identifications of miRNAs and/or targets of several plants have been also reported. Their methods, however, are only described as flow diagrams, which require programming skills and the understanding of input and output of the connected programs to reproduce. Results: To overcome these limitations and programming complexities, we proposed C-mii as a ready-made software package for both plant miRNA and target identification. C-mii was designed and implemented based on established computational steps and criteria derived from previous literature with the following distinguishing features. First, software is easy to install with all-in-one programs and packaged databases. Second, it comes with graphical user interfaces (GUIs) for ease of use. Users can identify plant miRNAs and targets via step-by-step execution, explore the detailed results from each step, filter the results according to proposed constraints in plant miRNA and target biogenesis, and export sequences and structures of interest. Third, it supplies bird's eye views of the identification results with infographics and grouping information. Fourth, in terms of functionality, it extends the standard computational steps of miRNA target identification with miRNA-target folding and GO annotation. Fifth, it provides helper functions for the update of pre-installed databases and automatic recovery. Finally, it supports multi-project and multi-thread management. Conclusions: C-mii constitutes the first complete software package with graphical user interfaces enabling computational identification of both plant miRNA genes and miRNA targets. With the provided functionalities, it can help accelerate the study of plant miRNAs and targets, especially for small and medium plant molecular labs without bioinformaticians. C-mii is freely available at http://www.biotec.or.th/isl/c-mii for both Windows and Ubuntu Linux platforms.

Throughout the long history of industrial and academic research, many microbes have been isolated, characterized and preserved (whenever possible) in culture collections. With the steady accumulation in observational data of biodiversity as well as microbial sequencing data, bio-resource centers have to function as data and information repositories to serve academia, industry, and regulators on behalf of and for the general public. Hence, the World Data Centre for Microorganisms (WDCM) started to take its responsibility for constructing an effective information environment that would promote and sustain microbial research data activities, and bridge the gaps currently present within and outside the microbiology communities.

Ophiocordyceps unilateralis is an outstanding insect fungus for its biology to manipulate host ants’ behavior and for its extreme host-specificity. Through the sequencing and annotation of Ophiocordyceps polyrhachis-furcata, a species in the O. unilateralis species complex specific to the ant Polyrhachis furcata, comparative analyses on genes involved in pathogenicity and virulence between this fungus and other fungi were undertaken in order to gain insights into its biology and the emergence of host specificity. O. polyrhachis-furcata possesses various genes implicated in pathogenicity and virulence common with other fungi. Overall, this fungus possesses protein-coding genes similar to those found on other insect fungi with available genomic resources (Beauveria bassiana, Metarhizium robertsii (formerly classified as M. anisopliae s.l.), Metarhizium acridum, Cordyceps militaris, Ophiocordyceps sinensis). Comparative analyses in regard of the host ranges of insect fungi showed a tendency toward contractions of various gene families for narrow host-range species, including cuticle-degrading genes (proteases, carbohydrate esterases) and some families of pathogen-host interaction (PHI) genes. For many families of genes, O. polyrhachis-furcata had the least number of genes found; some genes commonly found in other insect fungi are even absent (e.g. Class 1 hydrophobin). However, there are expansions of genes involved in 1) the production of bacterial-like toxins in O. polyrhachis-furcata, compared with other entomopathogenic fungi, and 2) retrotransposable elements. The gain and loss of gene families helps us understand how fungal pathogenicity in insect hosts evolved. The loss of various genes involved throughout the pathogenesis for O. unilateralis would result in a reduced capacity to exploit larger ranges of hosts and therefore in the different level of host specificity, while the expansions of other gene families suggest an adaptation to particular environments with unexpected strategies like oral toxicity, through the production of bacterial-like toxins, or sophisticated mechanisms underlying pathogenicity through retrotransposons.

In bacterial pathogens, both cell surface-exposed outer membrane proteins and proteins secreted into the extracellular environment play crucial roles in host-pathogen interaction and pathogenesis. Considerable efforts have been made to identify outer membrane (OM) and extracellular (EX) proteins produced by Leptospira interrogans, which may be used as novel targets for the development of infection markers and leptospirosis vaccines.

Analyzing temporal and spatial distributions of airborne particles of biological origins is vital for the assessment and monitoring of air quality, especially with regard to public health, environmental ecology, and atmospheric chemistry. However, the analysis is frequently impeded by the low levels of biomass in the air, especially with metagenomic DNA analysis to explore diversity and composition of living organisms and their components in the air. To obtain sufficient amounts of metagenomic DNA from bioaerosols, researchers usually need a long sampling time with an expensive high-volume air sampler. This work shows the utilization of an air sampling device containing an economical, high-volume portable ventilation fan in combination with customized multi-sheet filter holders to effectively obtain high yields of genomic DNA in a relatively short time. The device, named ‘AirDNA’ sampler, performed better than other commercial air samplers, including MD8 Airport and Coriolis compact air samplers. Using the AirDNA sampler, an average DNA yield of 40.49 ng (12.47–23.24 ng at 95% CI) was obtained in only 1 hour of air sampling with a 0.85 probability of obtaining ≥10 ng of genomic DNA. The genomic DNA obtained by the AirDNA system is of suitable quantity and quality to be further used for amplicon metabarcoding sequencing of 16S, 18S, and cytochrome c oxidase I (COI) regions, indicating that it can be used to detect various prokaryotes and eukaryotes. Our results showed the effectiveness of our AirDNA sampling apparatus with a simple setup and affordable devices to obtain metagenomic DNA for short-term or long-term spatiotemporal analysis. The technique is well suited for monitoring air in built environments, especially monitoring bioaerosols for health purposes and for fine-scale spatiotemporal environmental studies.

Given their ecological importance, bioindicators are used for the assessment of the health of river ecosystems. This study explored the fungal compositions and the potential of fungal taxa as bioindicators for indicating the water quality of the Mekong River, as the use of fungal indicators of the Mekong River was not previously well characterized. The Mekong River exhibited dynamic variations in both physicochemical/hydrochemical properties and fungal communities according to seasons and locations. The results revealed the dominance of alkaline earth metal ions and weak acids in the water. The magnesium-bicarbonate water type was found in the dry season, but the water became the chloride-calcium type or mixed type of magnesium-bicarbonate and chloride-calcium in the rainy season at downstream sites. Fungal composition analysis revealed the dominance of Chytridiomycota in the dry season and intermediate periods, and Ascomycota and Basidiomycota in the rainy season. The fungal communities were influenced by stochastic and deterministic assembly processes, mainly homogeneous selection, heterogeneous selection, and dispersal limitation. The extent of environmental filtering implied that some fungal taxa were affected by environmental conditions, suggesting the possibility of identifying certain fungal taxa suitable for being bioindicators of water quality. Subsequently, machine learning with recursive feature elimination identified specific fungal bins mostly consisting of Agaricomycetes (mainly Polyporales, Agaricales, and Auriculariales), Dothideomycetes (mainly Pleosporales), Saccharomycetes (mainly Saccharomycetales), Chytridiomycota, and Rozellomycota as bioindicators that could predict ambient and irrigation water quality with high selectivity and sensitivity. These results thus promote the use of fungal indicators to assess the health of the river.