Ibrahim, B. | Search bioRxiv

bioRxiv2018

SilentMutations (SIM): a tool for analyzing long-range RNA-RNA interactions in viral genomes and structural RNAs

BackgroundA single nucleotide change in the coding region can alter the amino acid sequence of a protein. In consequence, natural or artificial sequence changes in viral RNAs may have various effects not only on protein stability, function and structure but also on viral replication.\n\nIn recent decades, several tools have been developed to predict the effect of mutations in structured RNAs such as viral genomes or non-coding RNAs. Some tools use multiple point mutations and also take coding regions into account. However, none of these tools was designed to specifically simulate the effect of mutations on viral long-range interactions.\n\nResultsHere, we developed SilentMutations (SIM), an easy-to-use tool to analyze the effect of multiple point mutations on the secondary structures of two interacting viral RNAs. The tool can simulate disruptive and compensatory mutants of two interacting single-stranded RNAs. This allows a fast and accurate assessment of key regions potentially involved in functional long-range RNA-RNA interactions and will eventually help virologists and RNA-experts to design appropriate experiments.\n\nSIM only requires two interacting single-stranded RNA regions as input. The output is a plain text file containing the most promising mutants and a graphical representation of all interactions.\n\nConclusionWe applied our tool on two experimentally validated influenza A virus and hepatitis C virus interactions and we were able to predict potential double mutants for in vitro validation experiments.\n\nAvailabilityThe source code and documentation of SIM are freely available at github.com/desiro/silentMutations.

bioinformatics↗

bioRxiv2018

Identification of potential microRNAs associated with Herpesvirus family based on bioinformatic analysis

MicroRNAs (miRNAs) are known key regulators of gene expression on posttranscriptional level in many organisms encoded in mammals, plants and also several viral families. To date, no homologous gene of a virus-originated miRNA is known in other organisms. To date, only a few homologous miRNA between two different viruses are known, however, no gene of a virus-originated miRNA is known in any organism of other kingdoms. This can be attributed to the fact that classical miRNA detection approaches such as homology-based predictions fail at viruses due to their highly diverse genomes and their high mutation rate.\n\nHere, we applied the virus-derived precursor miRNA (pre-miRNA) prediction pipeline ViMiFi, which combines information about sequence conservation and machine learning-based approaches, on Human Herpesvirus 7 (HHV7) and Epstein-Barr virus (EBV). ViMiFi was able to predict 61 candidates in EBV, which has 25 known pre-miRNAs. From these 25, ViMiFi identified 20. It was further able to predict 18 candidates in the HHV7 genome, in which no miRNA had been described yet. We also studied the undescribed candidates of both viruses for potential functions and found similarities with human snRNAs and miRNAs from mammals and plants.

bioinformatics↗