iMars Mutation Analysis Reporting Software



The sensitivity of any mutational assay is determined by the level at which spontaneous mutations occur in the corresponding untreated controls. Establishing the type and frequency at which mutations occur naturally within a test system is essential if one is to draw scientifically sound conclusions regarding chemically induced mutations. Currently, mutation-spectra analysis is laborious and time-consuming. Thus, we have developed iMARS, a comprehensive mutation-spectrum analysis package that utilises routinely used methodologies and visualisation tools.

To demonstrate the use and capabilities of iMARS, we have analysed the distribution, types and sequence context of spontaneous base substitutions derived from the cII gene mutation assay in transgenic animals. Analysis of spontaneous mutation spectra revealed variation both within and between the transgenic rodent test systems Big Blue Mouse, MutaMouse and Big Blue Rat. The most common spontaneous base substitutions were G:C-->A:T transitions and G:C-->T:A transversions. All Big Blue Mouse spectra were significantly different from each other by distribution and nearly all by mutation type, whereas the converse was true for the other test systems. Twenty-eight mutation hotspots were observed across all spectra generally occurring in CG, GA/TC, GG and GC dinucleotides. A mutation hotspot at nucleotide 212 occurred at a higher frequency in MutaMouse and Big Blue Rat. In addition, CG dinucleotides were the most mutable in all spectra except two Big Blue Mouse spectra. Thus, spontaneous base-substitution spectra showed more variation in distribution, type and sequence context in Big Blue Mouse relative to spectra derived from MutaMouse and Big Blue Rat. The results of our analysis provide a baseline reference for mutation studies utilising the cII gene in transgenic rodent models. The potential differences in spontaneous base-substitution spectra should be considered when making comparisons between these test systems. The ease at which iMARS has allowed us to carry out an exhaustive investigation to assess mutation distribution, mutation type, strand bias, target sequences and motifs, as well as predict mutation hotspots provides us with a valuable tool in helping to distinguish true chemically induced hotspots from background mutations and gives a true reflection of mutation frequency.

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Institute of Life Science, College of Medicine, Swansea University, UK