  In this paper we introduce a novel, context-free grammar, {\it
RNAFeatures$^*$}, capable of generating any RNA structure including pseudoknot
structures (pk-structure). We represent pk-structures as orientable fatgraphs,
which naturally leads to a filtration by their topological genus. Within this
framework, RNA secondary structures correspond to pk-structures of genus zero.
{\it RNAFeatures$^*$} acts on formal, arc-labeled RNA secondary structures,
called $\lambda$-structures. $\lambda$-structures correspond one-to-one to
pk-structures together with some additional information. This information
consists of the specific rearrangement of the backbone, by which a pk-structure
can be made cross-free. {\it RNAFeatures$^*$} is an extension of the grammar
for secondary structures and employs an enhancement by labelings of the symbols
as well as the production rules. We discuss how to use {\it RNAFeatures$^*$} to
obtain a stochastic context-free grammar for pk-structures, using data of RNA
sequences and structures. The induced grammar facilitates fast Boltzmann
sampling and statistical analysis. As a first application, we present an $O(n
log(n))$ runtime algorithm which samples pk-structures based on ninety tRNA
sequences and structures from the Nucleic Acid Database (NDB).
