Author: Desirò, Daniel; Hölzer, Martin; Ibrahim, Bashar; Marz, Manja
Title: SilentMutations (SIM): a tool for analyzing long-range RNA-RNA interactions in viral genomes and structural RNAs Cord-id: mhawyect Document date: 2018_9_23
ID: mhawyect
Snippet: Background A single nucleotide change or mutation 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. In the last decade, several tools have been developed to predict the effect of mutations in structural RNA genomes. Some tools employ multiple point mutations and are also taking coding regions into acc
Document: Background A single nucleotide change or mutation 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. In the last decade, several tools have been developed to predict the effect of mutations in structural RNA genomes. Some tools employ multiple point mutations and are also taking coding regions into account. However, none of these tools was designed to specifically simulate the effect of mutations on viral long-range interactions. Results Here, 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 destructive and compensatory mutants of two interacting single-stranded RNAs. This will facilitate a fast and accurate assessment of key regions, possibly involved in functional long-range RNA-RNA interactions and finally help virologists to design appropriate experiments. SIM only needs 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. Conclusion We 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. Availability The source code and documentation of SIM are freely available at github.com/desiro/SilentMutations
Search related documents:
Co phrase search for related documents- long range and low parameter: 1
Co phrase search for related documents, hyperlinks ordered by date