So you have performed some differential gene expression experiments and have discovered a (few) non-coding RNAs that are of conspicuous interest… What now? Unless you are lucky and someone else has already characterised your needle in a haystack, odds are little is known about this transcript. You might be tempted to paste that .fasta file into mfold and say: “Look! It folds into an RNA secondary structure!” yet this won’t tell you much, besides that your RNA might look like a Christmas tree in February. This video explains how you can find out which regions of your RNA transcript of interest might be responsible for its biological function.
Most RNA secondary structure predictions over a few hundred nucleotides are just as likely to represent their real biological structure as you are of winning the keno jackpot. This is because of several parameters that simply cannot (easily) be considered in silico, such as co-transcriptional folding, ion concentration, sub- or near-optimal structures at equilibrium, protein and RNA quaternary interactions, etc.
Thankfully, evolutionary information can be used to validate not only the topology of an RNA structure prediction, but also it’s biological function. If mutations are observed across evolution that are consistent with a common structural feature (i.e. the structure is conserved despite variation of sequence), this indicates that the structure might be important for the reproductive success of the species. In other words, use it or loose it.
Recently, we showed that much more of our genome functions through RNA secondary structure motifs than previously believed. The results are publicly accessible and can now be viewed in the UCSC genome browser. This means that you can look for positions within your lncRNA of interest that overlap evolutionarily conserved RNA structures.