RNAmutants is a software designed to explore the consequences of mutation(s) on the secondary structures of RNA sequences. Instead of running separately a classical secondary structure prediction software (mfold, RNAfold or RNAstructure), on all possible RNA sequences with k mutations, RNAmutants computes all these predictions in once.
Usage of RNAmutants includes (but is not restricted to):
RNAmutants allows us to study the resilience of an RNA molecule to
pointwise mutations and to predict mutations that will tend to stabilize or
modify the secondary structure (a.k.a. deleterious mutations). Analysis of
the results can leads us to detect regions presumably under evolutionary
pressure (see References).
Using sequence or folding constraints, RNAmutants can also be used
as an efficient and realistic RNA design program (see
Tutorial).
RNAmutants use efficient dynamic programming algorithms allowing
to compute in polynomial time and space the minimum free energy
structures and the Boltzmann partition function for each k-mutants
(sequence with k mutations).
Using classical structure prediction softwares
such as mfold, RNAfold or RNAstructure on each sequences would require an
exponential time and then cannot be applied on real size sequence with a
large number number of mutations. For instance, we provide in
(Waldispühl et al., 2008) an illustration
of the full mutation landscape of sequences with 37 nucleotides (all
sequences of length 37). Without RNAmutants such results would
have been impossible to compute.
You can download a binary distribution of
RNAmutants and run it on your own computer. Alternatively,
a webserver is running in
P. Clote's laboratory and is available at
bioinformatics.bc.edu/clotelab/RNAmutants
A tutorial showing how to install and run
RNAmutants is available on this website. We illustrate the
versatility of the program by presenting some potential uses of the program.
RNAmutants computes the partition function of the grand canonical ensemble of all secondary structures that can be built over all mutants of the given sequence. Then, it rigorously samples from this ensemble, mutant sequences together with a secondary structures on this sequence.
The input RNA sequence is represented at the center while the
k-neighbourhoods (Here k = 1, 2) are represented by concentric rings. Each
individual RNA sequence is associated with a set of secondary structures
that can be mapped onto it (the boxed structures). These comprise the set
of structure that have to be enumerated to compute the Boltzmann partition
function).
From this illustration, it is easy to see how RNAmutants generalize
the previous structure prediction algorithms.