metaRNAmodules
Automated RNA 3D module extraction and modeling with discriminative power.
Introduction
Recent progress in predicting RNA structure is taking a route towards not only
explicit prediction of RNA 3D structure, but also filling the 'gap' in 2D RNA
structure prediction where, for example, predicted internal loops often can
take a structure based on non-canonical base pairs. This is increasingly
recognized with the steady increase of known RNA 3D modules. There is a
general interest in matching modules from one molecule to other
molecules for which the 3D structure is not known. However, a major challenge
is to determine whether the module is trustworthy in the first place.
Another challenge is that module recognition and modeling require time
consuming manual interference. We have created a pipeline, metaRNAmodules, which
completely automates extracting putative
FR3D modules and mapping of
such modules to
Rfam alignments to obtain comparative evidence. In a
subsequent step a module represented as a two-dimensional graph is fed into the
RMDetect program to test the discriminative power on
real and randomized
Rfam alignments. An initial extraction of 22495
3D modules in all
PDB files results in 977 internal loop and 17 hairpin
loop modules with clear discriminative power. Many of these modules describe only
minor variants of each other. Indeed, mapping of the modules onto Rfam families
results in 35 unique locations in 11 different families.
Download
The standalone version of the metaRNAmodules pipeline is available for download
here.
