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Simple Summary: Farmed Atlantic salmon are routinely exposed to bacterial pathogens, e.g., Piscirickettsia salmonis. Infection by Piscirickettsia sp. leads to a complex array of skin ulcers that can be difficult to treat and increases susceptibility to opportunistic infections. Evidence indicates that bacterial networks residing on salmon skin protect against developing ulcers by excluding pathogen colonization. A collapse of these beneficial interactions is thought to promote susceptibility to pathogen colonization during early stages of infection. We characterized the types and abundances of bacterial constituents on the skin of healthy Atlantic salmon compared with fish suffering from P. salmonis infection to test this hypothesis. The knowledge we gained can be used to optimize methods for early detection and prevention of skin ulcers by disrupting cooperative interactions between pathogenic bacteria. Maintaining the high overall health of farmed animals is a central tenant of their well-being and care. Intense animal crowding in aquaculture promotes animal morbidity especially in the absence of straightforward methods for monitoring their health. Here, we used bacterial 16S ribosomal RNA gene sequencing to measure bacterial population dynamics during P. salmonis infection. We observed a complex bacterial community consisting of a previously undescribed core pathobiome. Notably, we detected Aliivibrio wodanis and Tenacibaculum dicentrarchi on the skin ulcers of salmon infected with P. salmonis, while Vibrio spp. were enriched on infected gills. The prevalence of these co-occurring networks indicated that coinfection with other pathogens may enhance P. salmonis pathogenicity. [ABSTRACT FROM AUTHOR]

Noncoding RNA (ncRNA) research is already a routine in every genomics or transcriptomics initiatives. According to their functions, ncRNAs can be grouped into several different RNA families, which can be represented by conserved primary sequences, secondary structures, or covariance models (CMs). CMs are very sensitive in predicting RNA families in nucleotide sequences and have been widely used in characterizing the repertoire of ncRNAs in organisms from all domains of life. However, the large-scale prediction and annotation of ncRNAs require multiple tools along the process, imposing a great obstacle for researchers with lesser computational or bioinformatics background. StructRNAfinder emerged as an automated tool to avoid these bottlenecks, by performing the automatic identification and complete annotation of regulatory RNA families derived directly from nucleotide sequences. In this chapter, we provide a complete tutorial for both stand-alone and web server versions of StructRNAfinder. This will help users to install the tool and to perform predictions of RNA families in any genome or transcriptome sequences dataset.